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<reference>
Automated Maize Ear Phenotyping Using 3D Reconstructions
Ritwesh A. Kumar1 , Som Tripathi2 , Peja Matthews2,3 , Srikar Reddy2,4 , Talukder Zaki Jubery2 , Patrick Schnable5 ,
Adarsh Krishnamurthy2,6,* , and Baskar Ganapathysubramanian2,6,*

arXiv:2609.01921v1 [cs.CV] 1 Sep 2026

1

Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
2
Translational AI Research and Education Center, Iowa State University, Ames, IA 50011, USA
3
Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, NC 28301, USA
4
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
5
Department of Agronomy, Iowa State University, Ames, IA 50011, USA
6
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
*
Corresponding Authors: adarsh | baskar@iastate.edu

Abstract
Maize kernel traits such as row number, kernels per row, and kernel size vary largely for genetic
reasons and are consistently associated with regions of the genome that influence yield. Manual measurement of these traits, however, cannot keep pace with the volume of maize generated in a breeding
program. To address this, we developed and validated a fully automated pipeline for extracting these
traits from 3D point clouds of corn ears, built on a recently developed video-to-point-cloud platform.
Raw video frames are processed through COLMAP and NeRF, the ear is isolated via density-based separation, and the point cloud is distance-calibrated to physical units. The calibrated ear point cloud was
Z-axis aligned via PCA and cylindrically unwrapped to a 2D image. We enhanced contrast and performed zero-fine-tuning instance segmentation using Cellpose-SAM. A triple-juxtaposed unwrap strategy was used to prevent double-counting at the seam. The pipeline achieved kernel count R2 = 0.921
(MAPE = 10.33%) and kernel row number within ±2 rows for 95.2% of ears (MAE = 0.75 rows)
on a 168-ear held-out set from the 268-ear labeled dataset. The resulting multi-trait dataset has known
genotype identity for each ear, positioning it for phenotype-to-genotype association analyses.

Plain Language Summary
Counting and measuring kernels on corn ears by hand is slow and error-prone, limiting how quickly plant
breeders can evaluate new crop varieties. We developed a high-throughput automated analysis pipeline that
extracts eleven traits related to kernel count, size, shape, color, and packing geometry along the ear from base
to tip, building on the low-cost turntable imaging and 3D reconstruction platform of Young et al. (2026).
The system was applied to 1,091 corn ears, with accuracy validated on the 168-ear held-out set, achieving
a kernel count R2 of 0.921 and correctly identifying the number of kernel rows to within two rows for 160
out of 168 ears. Each ear has a known genotype identity. The resulting dataset of 1,091 ears spans heritable
traits (e.g., kernel row number) and management-sensitive traits (e.g., kernels per row). This dataset is ready
for genetic breeding and agronomic decision-making to pursue higher yield.

1

Abbreviations

AABB
AP
AR
BB
BPA
CLAHE
COLMAP
CPSAM
EMD
FFT
GT
HSV
Hue
KC
KNN
KPR
KRN
MAE
MAPE
NeRF
Pack
PCA
RMSE
SA
SD
Signed ME
Vol

Axis-Aligned Bounding Box
Axial Profile (mean hue and mean aspect ratio vs. height)
Bounding Box Aspect Ratio
Bounding Box
Ball-Pivoting Algorithm
Contrast Limited Adaptive Histogram Equalization
Structure-from-Motion and Multi-View Stereo pipeline
Cellpose-SAM
Earth-Mover Distance
Fast Fourier Transform
Ground Truth
Hue Saturation Value
Circular Mean Kernel Hue
Kernel Count
K Nearest Neighbor
Kernels Per Row
Kernel Row Number
Mean Absolute Error
Mean Absolute Percentage Error
Neural Radiance Field
Kernel Packing Traits (Gabriel graph neighbor count and mean neighbor distance)
Principal Component Analysis
Root Mean Square Error
Kernel Surface Area
Standard Deviation
Signed Mean Error
Kernel Convex Hull Volume Proxy

1 Introduction
Corn (Zea mays L.) is the world’s most produced cereal crop and a primary target of modern breeding
programs aimed at improving yield under increasingly variable environmental conditions (Food and Agriculture Organization of the United Nations, 2023). Yield in maize is largely determined by ear and kernel
traits, including kernel row number (KRN), kernels per row (KPR), kernel size, and total kernel count, that
carry substantial genetically determined variance and have been repeatedly associated with yield-related
loci in quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) (Peng et al.,
2011; Liu et al., 2015). These traits, however, are not interchangeable. KRN is established early in ear
development and exhibits high broad-sense heritability with consistent QTL associations (Peng et al., 2011;
Liu et al., 2015). In contrast, KPR reflects the degree to which each floret position is successfully filled
during grain fill and is therefore more sensitive to environmental stress, resource availability, and management conditions (Andrade et al., 2002). The spatial organization of kernels on the ear reflects the timing
and completeness of floret initiation and grain fill, and its genotype-by-environment (G×E) component has
been directly linked to the positional patterning of kernel development and abortion along the ear (Oury
et al., 2022). Despite this informativeness, measuring these traits at the scale required by modern breeding programs remains a manual, labor-intensive bottleneck. It limits phenotypic throughput and introduces
observer-dependent measurement error (Makanza et al., 2018; Araus et al., 2018).
Automated phenotyping has advanced rapidly, with platforms spanning two-dimensional (2D) digital imag2

Table 1: Comparative analysis of automated maize ear phenotyping methods. Abbreviations marked a are outputs of
the cited method only; all others are defined in the Abbreviations section.
Method

Modality

Hardware

Makanza et al. (2018)
Warman et al. (2021)
Gonzalez et al. (2022)
Shi et al. (2022)
Zaremehrjerdi et al.
(2025)
Li et al. (2025)
Sun et al. (2026)
Zhao et al. (2025)

2D image
Rotational scan
2D image
2D image
2D image

Consumer
Consumer
Consumer
Specialist
Consumer

Fan et al. (2026)

360◦ video (2D Consumer
projection)

Ours

360◦ video

a
g

2D image
Consumer
Structured light Specialist
Micro-CT
Specialist

Training Outputs
No
Yes
No
Yes
No

ELa , EWa , KC, KWa
KPhena
ELa , EWa , TFa , Tapa , Cura , Cola , KRN
KC, KRN, KPR
KPR

Yes (PQT) KC, KRN, KPR
No
KC, KRN, KPR, ELa , EDa , Barren Tip Length
No
Vol, SA, ENDIa , ENIIa , EMVSRa , SCTIa ,
ENDUIa
Yes
ELa , EDa , EVa , EWa , KC, KRN, KPR, KTa ,
KWa , TKWa

Consumer

No

KC, KRN, KPR, SA, Vol, Packg , Hue, AR,
APg

Output of cited method only; see respective reference for definition.
Pack and AP each comprise two distinct traits (see Abbreviations), giving eleven in total.

ing, rotational scanning, and three-dimensional (3D) reconstruction. A fundamental limitation of singleimage 2D approaches is that only one face of the ear is visible, leaving roughly half the kernels unobserved.
Methods differ in how they handle this: some apply a blanket doubling factor assuming perfect bilateral symmetry (Zhao et al., 2015). In contrast, others count only the visible face and treat it as a yield proxy (Makanza
et al., 2018; Shi et al., 2022). Rotational scanning systems partially address occlusion by capturing the full
ear surface. Warman et al. (2021) developed a maize ear scanner that rotates an ear under a fixed camera,
followed by digital flattening of the resulting video using a 2D panoramic projection, recovering per-kernel
positional information from all sides of the ear. More recently, Fan et al. (2026) introduced OpenEar, a
DIY 360° video-based platform that uses YOLOv11-based deep learning segmentation on cylindrical surface projections to extract several ear- and kernel-level traits (including ear length, ear diameter, ear volume,
kernel number, KRN, and KPR) on consumer-grade hardware. These approaches demonstrate that taskspecific deep learning pipelines can achieve high-throughput, low-cost ear phenotyping. They nevertheless
share two constraints: they require supervised training on manually annotated kernel and ear masks rather
than zero-shot segmentation, and they operate on a 2D projection of the ear surface.
The reliance on a 2D projection is the more fundamental of these two limitations, as it constrains the set of
traits that can be recovered. Projection of the ear surface onto a plane collapses the radial dimension, such
that tightly packed or overlapping kernels cannot be distinguished by shape alone, and kernel length, width,
and fill are conflated within a single silhouette (Gonzalez et al., 2022; Li et al., 2025). Reconstruction of the
ear as a 3D surface avoids this loss. Although the cob-facing portion of each kernel is necessarily occluded
from all lines of sight, the recovered outer surface patch retains radial depth and inter-kernel spacing, and
thereby permits the extraction of relative shape descriptors and packing geometry that are not accessible
from a projected representation. Such surface-based reconstruction provides a low-cost, readily deployable
alternative to sub-surface imaging modalities such as micro-CT, albeit one that characterizes the exposed
kernel cap rather than the complete kernel.
Three-dimensional methods address the projection limitation directly, but existing systems sit at one of two
extremes. Structured-light scanning achieves sub-millimeter kernel segmentation and morphological trait
extraction from full-surface point clouds (Sun et al., 2026), and micro-CT imaging enables simultaneous
3

recovery of internal kernel volume, surface area, and density parameters across diverse inbred populations (Zhao et al., 2025). Both recover kernel-level 3D geometry, but require specialist instrumentation
that constrains deployment scale. At the opposite extreme, a low-cost stationary-camera neural radiance
field (NeRF) pipeline has been used to recover skeleton length, convex hull volume, and cross-sectional
shape descriptors from a maize diversity panel (300 ears processed, 250 passing quality control) using
consumer-grade hardware and a cylindrical fiducial for automatic metric scaling (Young et al., 2026). This
approach demonstrates that 3D reconstruction is viable at breeding scale, though the traits recovered are
ear-level rather than kernel-level. Instance segmentation has been applied separately to maize ears, but only
for a single kernel-level trait (Zaremehrjerdi et al., 2025). No existing pipeline jointly measures kernel
count, volumetric shape proxies, and packing traits (specifically the number of physically touching kernel neighbors and their mean inter-kernel distances) from a single, consumer-accessible imaging workflow
without supervised training. Kernel color and shape are informative but remain unaddressed by existing 3D
pipelines: carotenoid concentration varies with kernel position along the cob (Calvo-Brenes et al., 2019),
so axial gradients in hue and elongation carry a developmental signal that a single per-ear mean discards.
Table 1 summarizes existing automated phenotyping methods and situates the present work among them.
In this study, we develop and validate a fully automated pipeline for extracting eleven ear- and kernel-level
phenotypic traits from 3D point clouds of corn ears, extending the corn ear NeRF reconstruction method
of Young et al. (2026) for trait extraction. Our contributions fall along three axes (method, validation, and
resource), as detailed below. The specific contributions of this work are as follows:
1. A training-free segmentation pipeline for maize kernels. The calibrated ear point cloud is aligned
to its principal axis via principal component analysis (PCA), cylindrically unwrapped to a 2D image,
contrast-enhanced via contrast limited adaptive histogram equalization (CLAHE), and segmented into
individual kernel instances using the zero-fine-tuned Cellpose-SAM model. A triple-juxtaposed unwrap
strategy resolves the seam introduced by cylindrical projection, ensuring each kernel is counted exactly
once.
2. Joint extraction of eleven ear- and kernel-level traits. Segmented kernels are re-projected to 3D point
sets, from which kernel count, kernel row number (KRN), kernels per row (KPR), per-kernel surface area,
per-kernel volume proxy, kernel packing geometry, kernel hue, and kernel aspect ratio are measured. Hue
and aspect ratio are additionally profiled in 10% height bands along the ear’s principal axis, characterizing
axial gradients in kernel color and shape from base to tip.
3. Isolated per-trait validation against known ground-truth geometry. Accuracy is assessed on a 27-ear
synthetic dataset with exactly known generating geometry, a six-ear manually annotated subset annotated
exhaustively at the level of individual kernels, and a 168-ear held-out set of manually annotated ears. This
multi-tiered approach isolates segmentation and trait-extraction error from physical measurement error.
4. A breeding-scale phenotypic dataset. The pipeline is applied to 1,091 ears of known genotype identity,
yielding a multi-trait dataset positioned for phenotype-to-genotype association analyses in future studies.
Together, these contributions establish that eleven traits spanning count, shape, color, and packing geometry are recoverable from 360° video alone. The pipeline requires neither specialist instrumentation nor
supervised, crop-specific models. Its throughput is compatible with breeding-scale deployment.
The rest of the paper is structured as follows. Section 2 details the video acquisition and 3D point cloud
reconstruction pipeline, following Young et al. (2026), and describes the zero-shot segmentation, per-kernel
trait extraction, and validation protocols developed in this work. Section 3 presents results on the 27-ear synthetic dataset, the 168-ear held-out set, and the six-ear manually annotated subset. Finally, Section 4 interprets these results in light of the pipeline’s structural occlusion limitations, examines the synthetic dataset’s
row-detection discrepancy, and discusses the implications of this work for breeding-scale phenotyping.

4

adapted from Young et al. (2025)

Input:
Metriccalibrated (mm)
3D ear point cloud

2D → 3D
segment
mapping

Cellpose-SAM
zero-shot

PCA
Z-axis
alignment

Triplejuxtapose
(seam handling)

CLAHE
(L channel)

Cylindrical
unwrap
(+ index map)

Total kernel count
largest
connected
component

Per-kernel
3D point set

Surviving
points Sk

Per-kernel morphology
• Surface area
• Volume (convex hull)
• Mean hue (circular)
• Aspect ratio (AABB)
• Hue vs. height (10% bands)
• Aspect ratio vs. height (10%
bands)

BPA
mesh
Gabriel graph
(periodic)

Calibrated input stage
Section 2.1 Preprocessing Steps
Intermediate 3D geometries
Trait extraction algorithms
Output: Section 2.2 Ear- and Section 2.3 kernel-level traits

Area-weighted
centroids ck

Kernel packing
• Neighbor count
• Mean neighbor distance

θ-histogram
+ adaptive FFT

KRN (row number)

Row-chain
graph

KPR (kernels / row)

Output: Eleven ear- and kernel-level traits (count, KRN, KPR, 6 morphology, 2 packing)
⇒ multi-trait dataset over 1,091 ears with known genotype
⇒ phenotype → genotype (QTL / GWAS) association

Figure 1: Full pipeline for 3D maize ear phenotyping and kernel trait extraction, with the video-to-point-cloud platform stage adapted from Young et al. (2026), and the subsequent 2D–3D projection, kernel segmentation, and spatial
graph trait extraction stages representing the novel contributions of this work.

2 Materials and Methods
The overall workflow, from raw 360° video to eleven ear- and kernel-level phenotypic traits, is summarized
in Figure 1. The 3D reconstruction procedure converting raw 360° video into a point cloud representation was adapted from the approach described by Young et al. (2026) and is detailed in the Supplemental
Material, “Corn Ear 3D Reconstruction Using Stationary Camera”. The pipeline components developed in
this work are described in the four sections that follow. Section 2.1 describes the 3D point cloud alignment, cylindrical unwrapping, contrast enhancement, and zero-shot kernel instance segmentation steps.
Section 2.2 outlines the extraction of ear-level phenotypic traits, and Section 2.3 details the extraction of

5

Table 2: Summary of the ear populations used in this study and their relationships.
Population

Size

Role

Relationship

Reconstructed
population
Labeled dataset

1,091 ears

Superset; contains the 268-ear labeled dataset

268 ears

Tuning set

100 ears

Held-out set

168 ears

Full breeding-scale dataset;
applied pipeline output
Ears with manual KC/KRN
ground truth
Parameter optimization
(Section 3.1)
Final validation (Section 3.3)

Manually
annotated subset

6 ears

Synthetic dataset

27 ears

Exhaustive kernel-level
validation (Section 3.4);
synthetic parameter
calibration
Per-trait validation against
exact generating geometry
(Section 2.4)

Subset of the 1,091; split into tuning + held-out
Subset of the 268-ear labeled dataset
Subset of the 268-ear labeled dataset; disjoint from
tuning set
Independent field study; no overlap with the 1,091
or 268-ear sets

Procedurally generated; default parameters
calibrated from aggregate statistics of the six-ear
subset, but no individual synthetic ear is derived
from a real scan

individual kernel geometric, volumetric, and packing traits. Section 2.4 then defines the statistical metrics
and computational cost measurement protocol used to evaluate pipeline performance.
Dataset Composition

Three distinct ear populations were used in this study. The pipeline was applied at scale to a population of
1,091 reconstructed ears of known genotype identity (Section 3). Of these, 268 ears had manually annotated
kernel count (KC) and kernel row number (KRN) ground truth available and constitute the labeled dataset;
the others were not annotated manually, reflecting the labor-intensive nature of manual phenotyping that
motivates this work. The 268-ear labeled dataset was further split into a 100-ear tuning set, used to optimize
all pipeline parameters (Section 3.1), and a 168-ear held-out set, reserved for final validation (Section 3.3).
Separately, a six-ear manually annotated subset was used for exhaustive kernel-level validation of spatial
packing and row-tracking accuracy (Section 3.4) and to calibrate default parameters for the synthetic dataset
(Section 2.4). These six ears were drawn from an independent field study and have no overlap with either the
1,091-ear reconstructed population or the 268-ear labeled dataset. The two datasets differ in annotator count
and annotation depth; full detail is provided in the Supplemental Material, “Manual Annotation Protocol”
(Figure A.1). Finally, a 27-ear synthetic dataset with exactly known generating geometry was procedurally
constructed for per-trait validation independent of manual annotation (Section 2.4). Table 2 summarizes
these populations and their relationships.
2.1

Preprocessing Steps

Z-Axis Alignment via PCA

To standardize the orientation of the ear for subsequent processing, the ear point cloud was aligned so that
its principal axis coincided with the Z-axis. Principal Component Analysis (PCA) was applied to the point
cloud; the first principal component, corresponding to the long axis of the ear, was identified. A rotation
matrix was computed to align this principal axis with [0, 0, 1]T using the axis-angle rotation formula, and all
points were transformed accordingly. Figure 2 shows the resulting isolated, calibrated, and Z-axis-aligned
point clouds for the four representative ears.
Cylindrical Unwrapping

Per-kernel traits in 3D require segmenting individual kernels first. Because segmentation was performed
on 2D imagery, the point cloud was first unwrapped to a 2D image, the kernels were segmented in that
6

image, and the resulting 2D segments were re-projected to 3D point sets. The aligned 3D point cloud was
mapped onto a 2D image via cylindrical unwrapping. Each point pi = (xi , yi , zi ) was first projected onto
the XY plane, and the geometric centroid (cx , cy ) was computed from the moments of the XY convex hull
of {(xi , yi )}. Each point was then mean-centered:
x′i = xi − cx ,

yi′ = yi − cy .

(1)

The per-point radius and effective cylinder radius were computed as:
q
′2
ri = x′2
R = P98 ({ri }),
i + yi ,

(2)

where R was set empirically to the 98th percentile of all radii. This choice formed the first step of tuning
stage 1 (Section 3.1): percentile values from 95 to 99 were swept on the 100-ear tuning set, and 98 gave the
closest agreement between predicted and manually counted total kernel counts. Next, the azimuthal angle
was computed as:
θi = atan2(yi′ , x′i ),
(3)
then mapped to [0, 2π) by adding 2π where θi < 0. Each point was then mapped to a pixel at column ui
and row vi :




zi − zmin
θi
vi =
(4)
(W − 1) ,
(H − 1) ,
ui =
2π
Hmm
where W and H are the image width and height in pixels and Hmm = zmax − zmin . Both image dimensions
were set proportionally to the physical circumference 2πR and the ear height Hmm at a fixed resolution of
0.25 mm pixel−1 . That resolution was selected in the second step of tuning stage 1 (Section 3.1), sweeping
values from 0.1 to 1.0 in steps of 0.05 on the 100-ear tuning set to maximize kernel boundary separability
and segmentation accuracy against manually measured total kernel count. Where multiple points projected
to the same pixel, the point with the minimum RGB value was retained, enhancing kernel boundary contrast
and improving subsequent segmentation accuracy. The resulting 2D image represents the full 360◦ surface
of the ear laid flat, with the circumference axis horizontal and the ear height axis vertical. Each pixel retains
an index mapping back to its source 3D point, so that 2D segments can later be re-projected to 3D point sets.

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure 2: Isolated and calibrated 3D point clouds for the four representative ears. Density-based separation and
distance calibration to physical units follow Young et al. (2026); Z-axis alignment via PCA is contributed by this
work.

7

25

0

0
Height (mm)

Height (mm)

25
25
50
75
100

25
50
75
100

0

50
100
Circumference (mm)

0

(a) Raw unwrapped image

50
100
Circumference (mm)
(b) After CLAHE

Figure 3: Cylindrically unwrapped image of ear 24-P2042_3 before (a) and after (b) CLAHE contrast enhancement.
The full 360° surface is laid flat with circumference horizontal and ear height vertical.

Contrast Enhancement via Contrast Limited Adaptive Histogram Equalization (CLAHE)

To normalize local contrast and improve kernel boundary visibility prior to segmentation, the unwrapped
image was converted from RGB to LAB color space. Contrast Limited Adaptive Histogram Equalization
(CLAHE) (Zuiderveld, 1994) was applied exclusively to the L channel. The image was then converted back
to RGB, yielding a contrast-enhanced unwrapped image used as input to segmentation. A clip limit of 1.0
and a tile grid size of 8 × 8 pixels were used. Both parameters were tuned sequentially on the 100-ear tuning
set as tuning stage 2 (Section 3.1), minimizing MAE against manual kernel counts, before the CellposeSAM (CPSAM) segmentation parameters were tuned. Because CLAHE operates on the luminance channel
without reference to segmentation boundaries, optimizing it for downstream count accuracy rather than
intrinsic contrast quality means the chosen parameters reflect the needs of the full pipeline rather than local
contrast alone. In the first step of stage 2, the clip limit was swept from 0.5 to 2.0 in steps of 0.5, yielding
an optimal value of 1.0. In the second step, with the clip limit fixed at 1.0, the tile grid size was swept from
4 × 4 to 16 × 16 in steps of 2 × 2, yielding an optimal size of 8 × 8. Figure 3 shows the resulting contrast
enhancement for a representative ear, with kernel boundaries substantially more separable after CLAHE
than in the raw unwrapped image.
Triple-Juxtaposed Unwrap Strategy

Cylindrical unwrapping introduces a seam at θ = 0 where kernels that straddle the seam boundary are
split across the left and right edges of the image, causing them to be double-counted if segmented naively.
To resolve this, the contrast-enhanced unwrapped image was tripled horizontally by concatenating three
copies side by side prior to segmentation. Instance segmentation was then performed on this tripled image.
After segmentation, only segments whose center of mass fell within the middle copy (x ∈ [W, 2W )) were
retained, counting each segment, whether fully contained within the middle copy or straddling a seam,
exactly once. The total kernel count was defined as the number of retained segment centers. Figure 4 shows
the tripled image with retained and discarded segments overlaid, illustrating how a kernel straddling the
seam is counted once from its occurrence in the middle copy.

8

Height (mm)

0
50
100

0

50

100

150 200 250 300
Circumference (mm)

350

400

Figure 4: Triple-juxtaposed unwrapped image with CPSAM segmentation overlaid (shown for ear 24-P2042_3).
Three copies of the contrast-enhanced image are concatenated horizontally; only segments whose center of mass
falls within the middle copy (highlighted in purple) are retained, ensuring kernels straddling the seam boundary are
counted exactly once (seam-straddling segments shown in red).

Segmentation via CPSAM

Individual kernels were delineated using CPSAM (Pachitariu et al., 2025), a foundation-model-based instance segmentation method. CPSAM was applied zero-shot to the tripled, contrast-enhanced unwrapped
image, with no task-specific fine-tuning on corn kernel imagery, using a cell probability threshold of 2.0,
a flow threshold of 0.6, and a minimum segment size of 150 pixels. These three parameters were tuned
sequentially, in the order listed, on the 100-ear tuning set as tuning stage 3 (Section 3.1), minimizing MAE
against manual kernel counts with the upstream CLAHE configuration held fixed. In the first step of stage
3, the cell probability threshold was swept over {1.0, 1.5, 2.0, 2.5, 3.0}, yielding an optimal value of 2.0.
In the second step, with the cell probability threshold fixed at 2.0, the flow threshold was swept over {0.4,
0.5, 0.6, 0.7, 0.8}, yielding an optimal value of 0.6. In the third step, with both prior parameters fixed, the
minimum segment size was swept over {50, 100, 150, 200, 250} pixels, yielding an optimal value of 150
pixels. Figure 5 shows the resulting kernel instance segmentation for the four representative ears, with the
retained kernel count reported for each.
Mapping 2D Segments to 3D Point Sets

Each 2D kernel segment was used to collect the corresponding 3D points from the aligned ear point cloud.
For every pixel within a segment that had a valid entry in the index map (i.e., a 3D point was projected
to that pixel during unwrapping), the corresponding 3D point index was recorded. The unique set of 3D
point indices associated with each segment thus defined a per-kernel 3D point set, forming the basis for all
subsequent volumetric and geometric trait computations.
2.2

Ear-Level Traits

Kernel Count, Kernel Row Number, and Kernels Per Row

Beyond the per-kernel traits described in Section 2.3, three traits characterize the ear at the whole-ear level:
total kernel count (KC), kernel row number (KRN), and kernels per row (KPR). KC was obtained directly as
the total number of retained kernel segments following the triple-juxtaposed segmentation strategy described
in Section 2.1. KRN and KPR were estimated via an adaptive FFT (Figure 6) and a row chain graph
(Figure 7), respectively, as described in the two subsections that follow.

9

Height (mm)

Height (mm)

25
0
25
50
75
100

0

25
0
25
50
75

50
100
Circumference (mm)

0

(b) 24-P2071_2

40
20
0
20
40
60

Height (mm)

Height (mm)

(a) 24-P2042_3

0

50
100
Circumference (mm)

50
100
Circumference (mm)

80
60
40
20
0
20
0

(c) 24-P2146_2

50
100
Circumference (mm)
(d) 24-P2199_1

Figure 5: CPSAM kernel instance segmentation results for the four representative ears. The total number of kernels
is shown with count equal to the total number of distinct segments.
Row Count Estimation via Adaptive FFT

Kernels arranged in the same longitudinal row on the ear share similar azimuthal angles θ in the unwrapped
space. Consequently, the number of kernel rows can be estimated by the number of evenly distributed peaks
in the histogram of θ values across all kernel centroids, which in turn equals the dominant frequency in the
Fast Fourier Transform (FFT) of that histogram. The azimuthal angles of all kernel centroids were therefore
binned into a histogram of B = 360 bins over [0, 2π). The coefficient of variation of the histogram counts
{hb }B
b=1 was computed as:
σh
CV =
,
(5)
µh
where µh and σh are the mean and standard deviation of the bin counts respectively. The smoothing kernel
size and CV threshold were jointly optimized on the 100-ear tuning set as tuning stage 4, independently of
the three sequential kernel count tuning stages (Section 3.1). The sweep covered odd kernel sizes from 3
to 15 bins and 200 evenly spaced CV threshold values spanning the full range of CV values observed on
that set. The combination minimizing MAE against manual row counts, with RMSE as a tie-breaker, gave a
kernel size of 15 bins and a threshold of 1.8725.
10

Table 3: Effect of the even-parity rounding rule (Eq. 10) on KRN prediction accuracy for the 100-ear tuning set.
KRN Estimate

MAE (rows)

Signed ME (rows)

0.88
0.86

−0.08
+0.26

Raw FFT peak, R̂ (no rounding)
Even-rounded, R̂final (used)

If CV ≥ 1.8725, indicating a highly uneven angular distribution, the histogram was smoothed with a
uniform moving-average kernel of width 15 bins prior to FFT to suppress noise; otherwise, no smoothing
was applied. The mean was subtracted from the histogram to remove the DC component:
h̃b = hb − µh ,

(6)

and the real-valued FFT was computed as F = |RFFT({h̃b })|. The dominant frequency within the expected
row range f ∈ [8, 24] was identified as
R̂ = arg max F(f ),

(7)

f ∈[8, 24]

where R̂ is the initial predicted kernel row number (KRN). For a histogram containing N = 360 angular
bins, the FFT frequency axis is defined as
fk =

k
,
Nd

k = 0, 1, . . . ,

N
,
2

(8)

where d is the sample spacing. Since the histogram was uniformly sampled over one revolution, d = 1/N ,
yielding
k
fk =
= k.
(9)
N (1/N )
Therefore, the FFT frequencies correspond exactly to the integer harmonics 0, 1, . . . , N/2, making the dominant FFT frequency, and hence R̂, inherently an integer. The search range f ∈ [8, 24] corresponds to the
biologically observed KRN range in cultivated maize. To account for the predominance of even kernel row
numbers in maize, the final KRN prediction was computed as
(
R̂,
if R̂ is even,
(10)
R̂final =
R̂ + 1, if R̂ is odd,
where R̂final denotes the final predicted KRN used in subsequent analysis. This biologically motivated postprocessing rule was selected from evaluation on the 100-ear tuning set (Table 3), where rounding odd-valued
predictions up to the next even integer lowered MAE relative to the unrounded estimate. Because the rule
only ever increases a prediction, it shifts the signed mean error (Signed ME) in the positive direction, and
Signed ME is reported alongside MAE to quantify that shift. The raw estimate is nearly unbiased (Signed
ME = −0.08 rows), while the even-rounded estimate carries a small positive bias (Signed ME = +0.26
rows). This bias is the rule working as intended: KRN in cultivated maize is predominantly even, so a
positive shift toward the nearest even integer moves odd predictions closer to the ground-truth distribution,
and MAE improves under the induced bias.
Row Chain Graph and Per-Row Kernel Counts

To assign individual kernels to rows and compute kernels per row, a row chain graph was constructed on the
3D kernel centroids. The azimuthal angle θi of each kernel centroid was computed as in Equation 3, and the
11

Amplitude

Count - mean

30

0.5
0.0
0.5
0

2
4
Theta (rad)

20
10
0

6

Pred=14
GT=14

0

10
20
30
Frequency (rows)

1

Amplitude

Count - mean

(a) 24-P2042_3

0
1

0

2
4
Theta (rad)

Pred=14
GT=14

100
50
0

6

0

10
20
30
Frequency (rows)

40

0.5

Amplitude

Count - mean

(b) 24-P2071_2

0.0
0.5

0

2
4
Theta (rad)

20
0

6

Pred=14
GT=14

0

10
20
30
Frequency (rows)

0.5

Amplitude

Count - mean

(c) 24-P2146_2

0.0
0

2
4
Theta (rad)

20
10
0

6

Pred=10
GT=12

0

10
20
30
Frequency (rows)

(d) 24-P2199_1

Figure 6: θ histograms and FFT spectra for each representative ear. In panel (d), two competing peaks fall within the
search range and the FFT selects the lower-frequency peak, underpredicting KRN as 10 against a ground truth of 12.

12

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure 7: Row chain assignments visualized on the 3D ear surface for each representative ear. Each color represents
a distinct row chain produced by the angular spanning forest algorithm.

circular angular distance between two kernels was defined as δ(θi , θj ) = min(|θi − θj |, 2π − |θi − θj |). An
angular spanning forest was built by connecting each kernel to its nearest angular neighbors within a per-ear
optimized angular threshold α, with the degree of each node constrained to at most two throughout, forming
chains of kernels that trace individual rows from base to tip.
After the initial graph was constructed, vertically discontinuous chains were reconnected. A candidate link
joined the top node u of one chain to the bottom node v of another, subject to zu < zv , δ(θu , θv ) ≤ α,
and both u and v having degree one. The candidate pair minimizing δ(θu , θv ) was linked, and this step
was repeated until no further connections were possible, with the degree-two constraint enforced after each
addition. The threshold α was determined per ear at inference time by sweeping from 5◦ to 30◦ in 1◦
increments. Writing C(α) for the number of connected components produced by the spanning forest at
threshold α, the selected α was the one minimizing C(α) − R̂ among all angles producing C(α) ≥ R̂.
If the resulting number of chains still exceeded R̂, the smallest chain
P by kernel
P count was repeatedly merged
into the chain with the nearest mean angular position θ̄ = atan2( i sin θi , i cos θi ) under δ, until exactly
R̂ chains remained. The size of each final chain, that is, the number of kernels in it, was recorded as the
kernels-per-row measurement for that row. Because the number of retained chains is fixed at R̂, KPR
accuracy is conditional on correct KRN estimation: an underestimated R̂ forces kernels from distinct true
rows into a single predicted chain.
2.3

Kernel-Level Traits

Kernel Surface Area

Per-kernel surface area was estimated from each kernel’s 3D point set using the Ball-Pivoting Algorithm
(BPA) (Bernardini et al., 1999). For each kernel, surface normals were estimated using a K Nearest Neighbor
(KNN) search with k = min(30, nk − 1), where nk is the number of points in kernel k, and oriented
consistently using tangent plane propagation. Letting di denote the distance from each point to its nearest
neighbor, the two BPA ball radii were set to:
r1 = P98 ({di }),

r2 = 2 r1 ,

(11)

where P98 ({di }) denotes the 98th percentile of the nearest-neighbor distance distribution for each kernel
point set, chosen so that the pivoting ball is large enough to bridge typical inter-point gaps while remaining
13

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure 8: Per-kernel 3D centroids (red spheres) overlaid on each reconstructed ear point cloud.

small enough to resolve individual kernel surface geometry. Denoting the vertices of triangle t as v0t , v1t , v2t ,
the area of each triangle was computed as:
at =

1
(v1t − v0t ) × (v2t − v0t ) ,
2

(12)

X

(13)

and the total surface area of kernel k as:
Ak =

at .

t

The area-weighted 3D centroid of kernel k was then computed as:
 t

P
v0 + v1t + v2t
t at
3
P
ck =
,
t at

(14)

and used as the 3D centroid of each kernel for downstream computations. Figure 8 shows the resulting
per-kernel centroids overlaid on each representative ear’s point cloud. The vertices of the largest connected
component of the BPA mesh for kernel k are denoted Sk ; the following subsections use Sk and the per-kernel
centroids ck to extract the pipeline’s remaining kernel-level traits, describing kernel size, color, shape, and
packing.
Kernel Convex Hull Volume Proxy

We define the convex hull volume proxy of kernel k as the convex hull of the 3D point set Sk , computed
using the Quickhull algorithm as implemented in scipy.spatial.ConvexHull (Virtanen et al., 2020).
Because the kernel interior and the proximal cob-facing surface are occluded from all camera views, the
convex hull closes across the occluded region, enclosing only the exposed outer cap geometry. The resulting
convex hull volume proxy is therefore a relative size descriptor of the visible kernel geometry rather than an
estimate of the kernel’s true physical volume.
Mean Kernel Hue

The circular mean hue of each kernel was computed from the RGB values of Sk . Each point’s RGB value
was converted to HSV and its hue angle hi ∈ [0◦ , 360◦ ) recorded. The circular mean was then computed as:


P
P
h̄k = atan2 |S1k | i sin hi , |S1k | i cos hi mod 360◦ ,
(15)
to correctly handle the wraparound at 0◦ /360◦ .
14

3D Bounding Box Aspect Ratio

The axis-aligned bounding box (AABB) of each kernel was computed from Sk . The three edge lengths
ℓ1 = xmax − xmin ,

ℓ2 = ymax − ymin ,

ℓ3 = zmax − zmin

(16)

were sorted in descending order as ℓ(1) ≥ ℓ(2) ≥ ℓ(3) , and the aspect ratio defined as:
ARk =

ℓ(1)
≥ 1.
ℓ(2)

(17)

Because each kernel’s 3D segment bounding box captures only the exposed outer cap, its dimensions reflect
visible surface depth. The resulting aspect ratio (ARk ) functions as a consistent relative shape descriptor
optimized for non-destructive, line-of-sight phenotyping.
Mean Hue and Mean Aspect Ratio vs. Height

To characterize how kernel color and shape vary along the ear from base to tip, kernel centroids were
partitioned into ten equal height bands of 10% each along the PCA-aligned Z-axis (base to tip). For each
band b, the set of kernels Kb whose Z coordinate fell within the corresponding interval of the total ear height
was identified. The mean hue vs. height profile was computed as the circular mean of the per-kernel circular
mean hues h̄k (Section 2.3) over all k ∈ Kb , giving each kernel equal weight:


P
P
h̄b = atan2 |K1b | k∈Kb sin h̄k , |K1b | k∈Kb cos h̄k mod 360◦ .
(18)
The mean aspect ratio vs. height profile was computed as the arithmetic mean of ARk (Section 2.3) over all
k ∈ Kb .
Gabriel Graph Construction

To characterize the spatial packing structure of kernels on the ear, the area-weighted 3D centroids ck computed during surface area estimation were first projected back onto the 2D unwrapped image coordinate
system. The azimuthal angle θk = atan2(yk − cy , xk − cx ) of each centroid was mapped to a column
coordinate and its z coordinate to a row coordinate using the same cylindrical mapping applied during unwrapping, yielding a 2D center uk = (uk , vk ) for each kernel. A Gabriel graph (Gabriel and Sokal, 1969)
was then constructed on these projected 2D centers. In a Gabriel graph, two nodes p and q are connected by
an edge if and only if no other node lies within the circle whose diameter is the segment pq.
Because the unwrapped image is periodic in the horizontal (circumferential) direction, the graph was computed on a cylindrically-periodic domain: three copies of the kernel centers (offset by −W , 0, and +W
pixels) were used to construct a Delaunay triangulation, from which candidate Gabriel edges were extracted
and tested for the empty-circle condition across the periodic domain. Neighbor relationships were counted
only for middle-copy nodes, ensuring each edge was recorded exactly once. Finally, the number of Gabriel
graph neighbors was recorded for each kernel as a measure of local packing density. Because this construction operates directly on kernel centroids and does not consult R̂, the Gabriel graph packing traits are
independent of the row detection step. Figure 9 maps the resulting per-kernel neighbor counts onto the
unwrapped image for each representative ear.
Mean Nearest-Neighbor Distance

For each kernel, the mean Euclidean distance in 3D between its centroid ck and the centroids of all its Gabriel
graph neighbors was computed. The Gabriel graph was stored as a sparse adjacency structure, from which
the direct-neighbor edge weights in 3D were retrieved and averaged per kernel. This quantity characterizes
the average physical spacing between a kernel and its proximal neighbors, providing a continuous measure
of local kernel packing density.
15

40

20

60
80

Height (mm)

Height (mm)

40

0
20
40
60

100

10
9
8
7
6
5
4
3
2
1

80
0

25

50 75 100 125
Circumference (mm)

0

20

(a) 24-P2042_3

40 60 80 100
Circumference (mm)
(b) 24-P2071_2

80

40

0
20
40

60
Neighbor Count

10
9
8
7
6
5
4
3
2
1

Height (mm)

20
Height (mm)

10
9
8
7
6
5
4
3
2
1

Neighbor Count

20

20
Neighbor Count

10
9
8
7
6
5
4
3
2
1

Neighbor Count

0

20
0
20

60
0

40

25
50
75 100
Circumference (mm)

0

25
50
75 100
Circumference (mm)

(c) 24-P2146_2

(d) 24-P2199_1

Figure 9: Kernel neighbor count maps derived from the Gabriel graph, overlaid on the unwrapped image for each
representative ear. Each segmented kernel is colored by its number of Gabriel graph neighbors, providing a spatial
view of local packing density across the ear surface.

2.4

Evaluation Metrics

The evaluation metrics used throughout the evaluations below were computed as follows. For a set of N
paired predicted and true values {(xi , yi )}N
i=1 , the mean absolute error (MAE) was computed as
N

MAE =

1 X
|xi − yi | .
N

(19)

i=1

The root mean square error (RMSE), used as a tie-breaking criterion during FFT parameter tuning (Section 3.1) and reported alongside kernel count accuracy, was computed as
v
u
N
u1 X
t
RMSE =
(xi − yi )2 .
(20)
N
i=1

The mean absolute percentage error (MAPE), used to report kernel count accuracy on the 168-ear held-out
set, was computed as
N
100% X xi − yi
MAPE =
,
(21)
N
yi
i=1

16

where yi > 0 is the true (manually annotated) value for ear i and xi the corresponding prediction. For
circular quantities (hue, in degrees), the circular MAE was computed using the wrapped angular difference,
N

MAEcirc =

1 X
min(|xi − yi | , 360◦ − |xi − yi |) .
N

(22)

i=1

The coefficient of determination (R2 ), used to assess agreement between predicted and true per-ear kernel
counts, was computed as
2

PN

R = 1 − Pi=1
N

(yi − xi )2

i=1 (yi − ȳ)

2

,

(23)

where ȳ is the mean of the true values {yi }. KRN accuracy on the held-out set was additionally summarized
as a tolerance-based accuracy rate: the fraction of ears whose predicted KRN fell within k rows of the
manually annotated value,
N
1 X
Acc±k =
⊮[ |xi − yi | ≤ k ] ,
(24)
N
i=1

where ⊮[·] is the indicator function; k = 2 was used when reporting the headline KRN accuracy figure.
Computational cost was quantified as the end-to-end wall-clock runtime (in seconds) required to process a
single ear from the input point cloud to the final extraction of all eleven phenotypic traits, measured on the
hardware reported in Table 10.
To characterize axial variation in kernel shape across the population, each kernel centroid was assigned a
normalized longitudinal position,
z − zmin
,
(25)
znorm =
zmax − zmin
where z is the kernel centroid z-coordinate, and zmin and zmax are the minimum and maximum kernel
centroid z-coordinates of the corresponding ear. The normalized coordinate was partitioned into three equal
intervals corresponding to the bottom, middle, and top thirds of the ear.
For ear i, the mean kernel aspect ratio within height band b was computed as
Ni,b

X
¯ i,b = 1
AR
ARi,b,k ,
Ni,b

(26)

k=1

where ARi,b,k denotes the aspect ratio of kernel k within height band b, and Ni,b is the number of kernels
assigned to that band for ear i.
Population-level summaries for each height band were then obtained by averaging the corresponding per-ear
means,
M
X
¯ b= 1
¯ i,b ,
AR
AR
(27)
M
i=1

where M is the total number of analyzed ears. The reported standard deviation (SD) for each height band
was computed over the corresponding set of per-ear mean aspect ratios,
v
u
M
u 1 X

¯ i,b − AR
¯ b 2.
SDb = t
AR
(28)
M −1
i=1

To capture spatial differences in kernel shape development, the bounding box aspect ratio (AR) was also
computed separately over the base, middle, and tip thirds of each ear. The resulting aspect ratio by thirds is
compiled in Table 7 and interpreted in the Results and Discussion sections.
17

Distributional Agreement via Earth-Mover Distance

With all eleven traits defined, the remaining subsections describe the validation protocols: a distributional
agreement metric, the synthetic ground-truth protocol (Section 2.4), kernel matching via Hungarian assignment, and trait-specific evaluation notes. To quantify agreement between automated and manually annotated
kernels-per-row (KPR) and Gabriel graph neighbor-count distributions on the six-ear manually annotated
subset, the Earth-Mover Distance (EMD), equivalent to the first Wasserstein distance (Rubner et al., 2000),
was computed separately for each ear and each trait between the empirical distribution of manually annotated values and the corresponding automated predictions. For two one-dimensional empirical distributions
with cumulative distribution functions FP and FQ , the EMD is defined as
Z ∞
|FP (x) − FQ (x)| dx,
(29)
EMD(P, Q) =
−∞

computed here using the scipy.stats.wasserstein_distance implementation (Virtanen et al.,
2020). Lower EMD values indicate closer agreement between the automated and manual distributions,
with an EMD of zero indicating identical distributions. Since the EMD preserves the physical units of the
underlying trait, an EMD value below 1.0 indicates that the cumulative distribution of automated predictions
deviates from the manual annotation distribution by less than one unit of the trait across the population.
Synthetic Ground-Truth Validation Protocol

Manual annotation of all eleven ear- and kernel-level traits at exact, pixel-perfect accuracy is infeasible at
scale. Per-trait extraction accuracy was therefore additionally assessed against a complementary dataset of
27 procedurally generated synthetic ears. Each synthetic ear was built directly from a set of known geometric
and lattice parameters (ear length, curvature, cross-sectional taper, kernel row number (KRN), kernels per
row (KPR), and per-kernel ellipsoid dimensions), with no fitting to any individual real scan. Construction
placed ellipsoidal kernels on a parametric cob: a constant-curvature centerline defined the ear’s axial spine,
and a radius profile governed cross-sectional size from the base through a maximum-width position to the
tip. Kernels were arranged on a KRN × KPR lattice (KRN even, in [8, 24]), with an optional alternate-row
circumferential offset and per-kernel jitter in size and circumferential angle; the angular jitter also perturbs
each kernel’s position along its row.
Kernels were removed by a tip-biased Bernoulli dropout, with each kernel’s drop probability increasing
linearly with its normalized axial position τk ∈ [0.06, 0.97] (row positions were sampled with a small
margin left at the base and tip; 0 = base, 1 = tip):
pdrop (τk ) = fmiss (0.4 + 1.2 τk ),

(30)

where fmiss is the ear’s missing-fraction parameter. Each kernel was retained independently with probability
1 − pdrop (τk ), so a kernel at the base row was roughly 0.47fmiss likely to be dropped and one at the tip row
roughly 1.56fmiss likely to be dropped, emulating incomplete tip fill. The realized fraction of missing
kernels on a given ear therefore fluctuates stochastically around fmiss instead of matching it exactly.
For each kernel, only the outward-facing hemispherical cap of its ellipsoid, the portion visible to an external
camera, was sampled into 3D points, mirroring the cob-facing occlusion present in the empirical pipeline.
Gaussian along-normal position noise was then added to approximate reconstruction noise. Default parameter values were calibrated once, offline, to match the aggregate geometry, color, and row count of the
independent six-ear manually annotated subset described in Section 3.4, while no real point cloud entered
the generation of any individual synthetic ear. Ground truth was therefore known exactly by construction
rather than measured post hoc.
Ground-truth kernel count, KRN, and per-row kernel count (KPR) were recorded directly from the generating lattice; per-kernel surface area and volume were computed analytically from each ellipsoid’s three
18

known semi-axes using the Thomsen approximation for surface area (Kresta et al., 2015) (below), halved to
reference the same outward cap geometry recovered by the empirical pipeline.

A(a, b, c) = 4π

(ab)p + (ac)p + (bc)p
3

1/p
,

p = 1.6075,

(31)

with ground-truth cap surface area and volume referenced to the outward-facing hemisphere as Acap = A/2
and Vcap = 43 πabc/2. The 27 synthetic ears were generated as a full 3×3×3 factorial sweep over kernel row
number (KRN ∈ {12, 16, 20}), spine curvature (∈ {0, 0.002, 0.004} rad mm−1 ), and tip-biased missingkernel fraction (∈ {0, 0.05, 0.15}), with all other parameters held at their calibrated defaults and a distinct
reproducible seed assigned to each of the 27 combinations.
Two ground-truth quantities carry documented limitations relative to the pipeline’s corresponding outputs.
First, ground-truth hue is the population mean hue parameter used to generate each kernel’s color, not the
specific per-kernel hue actually sampled (each kernel’s hue is an independent draw around this mean); it
is therefore a distributional reference for aggregate hue comparisons rather than an exact per-kernel target.
Second, ground-truth neighbor count and mean neighbor distance are computed from lattice-topological
adjacency (row/column neighbors on the generating grid, including brick-offset diagonals, with periodic
wraparound in the circumferential direction) instead of from a Gabriel graph. These packing-trait comparisons should therefore be read as evaluating agreement in overall packing density and topology rather than
an exact edge-by-edge match to the pipeline’s cylindrical Gabriel graph construction.
The full pipeline (PCA-based Z-axis alignment, cylindrical unwrapping, CLAHE contrast enhancement,
CPSAM segmentation, BPA-based surface area, convex hull volume, hue and aspect ratio extraction, Gabriel
graph packing, and row detection via adaptive FFT and the row chain graph) was executed end-to-end on
each synthetic point cloud. The same implementation was used as for the 168-ear held-out set, with no
access to the ground-truth kernel table at any stage.
Kernel Matching via Hungarian Assignment
N

N

pred
gt
For a given ear, let {pi }i=1
denote the predicted 3D kernel centroids and {gj }j=1
the ground-truth centroids, both expressed in the shared PCA-aligned reference frame. The pairwise cost matrix was defined as
the Euclidean centroid distance,
Cij = ∥pi − gj ∥2 ,
(32)

and the globally optimal one-to-one correspondence π ∗ between predicted and ground-truth kernels, over
the full set of predicted and ground-truth kernels for that ear, was obtained by solving the linear assignment
problem
X
π ∗ = arg min
Ci,π(i) ,
(33)
π

i

via the Hungarian algorithm (Kuhn, 1955; Crouse, 2016). Each optimal pair (i, π ∗ (i)) was then filtered
post hoc against a per-ear distance cutoff,
 
Ci,π∗ (i) ≤ τmatch ,
τmatch = 21 medianj d¯jgt ,
(34)
where d¯jgt is the ground-truth lattice-topological mean neighbor distance of kernel j, so that τmatch scales
naturally with each ear’s kernel density. Pairs exceeding τmatch were rejected as spurious; the corresponding predicted kernel was recorded as a false positive and the corresponding ground-truth kernel as a false
negative. This post hoc distance filter prevents distant, incidental pairings, which the Hungarian algorithm
may still return as globally optimal when Npred ̸= Ngt , from being scored as correct matches.

19

Trait-Specific Evaluation Notes

For each of the eleven traits, MAE (or circular MAE, for hue) was computed across matched kernel pairs
using the definitions in Section 2.4, then averaged across all 27 ears. Kernels-per-row (KPR) MAE was
computed after aligning predicted row labels to ground-truth row labels via a second, independent linear
assignment, which maximizes shared kernel count over a row-membership contingency table instead of
minimizing centroid distance. This MAE was averaged only over rows present in both the aligned predicted
and ground-truth sets, so rows absent from one side after alignment do not contribute.
That is,
1 X pred
MAEKPR =
nr − ngt
(35)
r ,
|R|
r∈R

where R is the set of row labels present in both the aligned predicted rows and the ground-truth rows.
Ground-truth aspect ratio was derived from each kernel’s three known ellipsoid semi-axes, sorted in descending order and ratioed as
gt
gt
ℓgt
(1) ≥ ℓ(2) ≥ ℓ(3) = sort(acirc , aaxial , aradial ) ,

ARgt
k =

ℓgt
(1)
ℓgt
(2)

.

(36)

The predicted aspect ratio was instead computed from the axis-aligned bounding box of the BPA-surviving
point set (Section 2.3), so the two constructions are analogous without being identical. The axial hue and
aspect ratio profiles were evaluated by comparing predicted and ground-truth values within matched 10%height bands along the PCA-aligned ear axis, each computed independently from each set’s own height
range.

3 Results
Results are presented across three complementary validation tiers, structurally summarized in Figure 10,
preceded by an overview of the parameter tuning configuration and followed by a computational cost analysis. Section 3.1 details the optimization of pipeline parameters. Section 3.2 evaluates per-trait accuracy
against the 27-ear synthetic dataset, where exact ground truth isolates pipeline performance from physical
measurement error. Section 3.3 reports kernel count and KRN accuracy on the 168-ear held-out set, situates the pipeline against existing benchmarks, and provides qualitative per-ear trait distributions for four
representative ears. Section 3.4 assesses spatial packing and row-tracking fidelity against the six-ear manually annotated subset. Section 3.5 details the computational execution times and hardware scalability of the
workflow.
3.1

Parameter Tuning Dataset

All empirically tuned parameters were optimized on the 100-ear tuning set, drawn from the 268-ear labeled
dataset, with the remaining 168-ear held-out set reserved for final evaluation. The 100-ear tuning set was
constructed to be representative of the full dataset in two steps. First, 50 ears were selected manually by
choosing samples spanning the full range of ear curvature, prioritizing highly curved and moderately curved
ears (assessed visually prior to selection) to ensure adequate coverage of challenging geometries. Second,
the remaining 50 ears were selected to match the kernel row number distribution of the full 268-ear labeled
dataset: at least three ears of each observed row count category were included (or all available ears where
fewer than three existed), and the remaining slots were filled by sampling each category in proportion to
its frequency in the full dataset, with specific ears chosen randomly within each category. All parameters
reported in subsequent sections were tuned on this 100-ear tuning set and evaluated on the 168-ear held-out
set. Because the tuning set was deliberately enriched for highly curved and moderately curved ears, it is not
a random sample of the labeled dataset and constitutes a more challenging population than the held-out set.
Performance on the tuning set therefore places no upper bound on held-out performance.
20

Results: Three validation tiers

Synthetic
27 ears, exact geometry

Per-trait accuracy vs. exact ground truth
• Kernel count agreement (Table 4)
• Geometry, color, shape traits (Table 4)
• Row-structure traits (Table 4)

Held-Out
168 ears, manual annotation

Kernel count & KRN accuracy
• Predicted vs. manual kernel counts (Figure 12)
• Predicted vs. manual row counts (Figure 13)

Manual Validation
6 ears, exhaustive

KPR & neighbor-count agreement
• Distributional agreement, EMD (Figure 29)
• Predicted vs. ground-truth KRN (Table 8)

Benchmarking & compute cost
• Comparison against prior methods (Table 5)
• Per-ear processing time by stage (Table 10)

Qualitative per-ear distributions (4 representative ears)
• Surface area (Figure 14, Figure 17, Figure 18)
• Volume proxy (Figure 15, Figure 19, Figure 20)
• Packing: neighbor count & distance (Figure 16, Figure 21, Figure 22)
• Row-level kernel counts (Figure 23)
• Hue & aspect ratio (Figure 24, Figure 25)
• Axial hue & aspect profiles (Figure 26, Figure 27)
• Full-dataset trait distributions (Figure 28)

Multi-tiered validation of all eleven traits
Pipeline accuracy under exact ground truth (synthetic)
Breeding-scale accuracy on real ears (held-out)
Spatial tracking fidelity at kernel resolution (manual)

Figure 10: Overview of the multi-tiered validation framework presented in Section 3: exact ground-truth validation
using a 27-ear synthetic dataset (Section 3.2), breeding-scale accuracy using a 168-ear held-out set (Section 3.3), and
kernel-resolution tracking validation on a six-ear manually annotated subset (Section 3.4).

Parameters targeting kernel count accuracy were optimized sequentially in pipeline order across three stages
(stage 1: cylindrical unwrapping parameters; stage 2: CLAHE parameters; stage 3: CPSAM parameters),
minimizing MAE against manual kernel counts on the 100-ear tuning set. Within each stage, individual
parameters were swept one at a time in the order listed, with each parameter sweep constituting a step
within that stage. The FFT smoothing kernel size and CV threshold, which govern row count accuracy,
were optimized jointly in stage 4, independently of the three sequential kernel count tuning stages, as the
two-dimensional parameter space was small enough to enumerate exhaustively.
3.2

Synthetic Ground-Truth Validation

Per-trait extraction accuracy, assessed against the 27-ear synthetic dataset described in Section 2.4, is summarized in Table 4. For each of the eleven traits, mean absolute error (MAE), or circular MAE for hue, was
computed across matched kernel pairs within an ear, then averaged across all 27 ears.
Quantitative evaluation on the synthetic dataset reveals a higher error rate in row-number detection compared
to the real-ear dataset, with a pooled KRN MAE of 7.26 rows (Table 4). To investigate the root cause of this
discrepancy, we analyzed individual failure cases in the frequency analysis step.
Real ears carry natural biological variation and structural irregularities that smooth the spatial frequency
21

Table 4: Performance metrics across phenotypic traits on the 27-ear synthetic dataset. KC achieved an R2 of 0.769.
Trait

MAE

Unit

KC
KRN
KPR
SA
Vol
Neighbor Count
Mean NN Distance
Circular Hue
Aspect Ratio
Circular Hue vs. Height
Aspect Ratio vs. Height

26.48
7.26
11.28
9.19
6.62
2.05
1.23
3.24
0.14
2.30
0.04

kernels
rows
kernels
mm2
mm3
neighbors
mm
degrees
unitless
degrees
unitless

Amplitude

Count - mean

spectrum into a single dominant peak. The synthetic ears instead possess a perfectly regular, rigid lattice
structure. This mathematical regularity introduces discrete grid-aliasing artifacts and prominent structural
harmonics. Because the adaptive peak-selection step searches for a dominant frequency, these sharp secondary harmonic spikes can mislead it. When a high-frequency harmonic spike carries a greater amplitude
than the true fundamental frequency, the adaptive selection step locks onto it, resulting in a predicted row
count that represents the harmonic rather than the ground truth. This specific failure mode is illustrated in
Figure 11 using a synthetic ear generated with a true kernel row number of 12, where the peak selection
locks onto the dominant harmonic spike at a frequency of 24.

5
0
0

2
4
Theta (rad)

Pred=24
GT=12

200
0

6

0

10
20
Frequency (rows)

30

Figure 11: FFT row-detection failure due to harmonic locking on a synthetic ear lattice (KRN = 12, zero curvature,
fully-filled ear with no missing kernels). (Left) The highly regular smoothed azimuthal histogram. (Right) The corresponding FFT spectrum showing a prominent secondary structural harmonic at f = 24. The adaptive peak-selection
step locks onto this harmonic peak (Pred = 24) rather than the true fundamental frequency (GT = 12).

The geometric and color traits (surface area, convex hull volume proxy, hue, aspect ratio, and both axial
profiles) show close agreement with the generating parameters, confirming that the segmentation and perkernel trait extraction stages recover fine-grained relative shape descriptors under known ground truth. The
row-structure traits (KRN and KPR) show substantially larger error on this dataset than on the real held-out
set; this discrepancy is examined in Section 4.
3.3

Held-Out Set Validation and Benchmarking

Of the 268-ear labeled dataset, the 168-ear held-out set was reserved for validation. Performance is reported
side-by-side for both the tuning and validation sets in Figure 12 and Figure 13.
22

Predicted Kernel Count

700

800

Perfect prediction

700

600
500
400
300
n = 100
R² = 0.9471
RMSE = 29.58
100
MAE = 20.74
MAPE = 8.70%
0 100 200 300 400 500 600 700 800
Ground Truth Kernel Count
200

Predicted Kernel Count

800

Perfect prediction

600
500
400
300
n = 168
R² = 0.9212
RMSE = 37.31
100
MAE = 26.58
MAPE = 10.33%
0 100 200 300 400 500 600 700 800
Ground Truth Kernel Count
200

(a) 100-Ear Tuning Set

(b) 168-Ear Held-Out Set

24

24

22

22

20

20

18

18

Predicted KRN

Predicted KRN

Figure 12: Kernel count prediction accuracy for (a) the 100-ear tuning set and (b) the 168-ear held-out set.

16
14
12
10
8

Exact match
|diff| 2 rows
|diff| > 2 rows
Perfect (y=x)

16
14
12

n = 100
MAE = 0.86 rows
Exact = 63/100 (63.0%)
Within ±2 = 96/100 (96.0%)

10
8

8 10 12 14 16 18 20 22 24
Ground Truth KRN

Exact match
|diff| 2 rows
|diff| > 2 rows
Perfect (y=x)

n = 168
MAE = 0.75 rows
Exact = 121/168 (72.0%)
Within ±2 = 160/168 (95.2%)

8 10 12 14 16 18 20 22 24
Ground Truth KRN

(a) 100-Ear Tuning Set

(b) 168-Ear Held-Out Set

Figure 13: KRN prediction accuracy for (a) the 100-ear tuning set and (b) the 168-ear held-out set.

KRN was predicted exactly for 121 of 168 held-out ears (72.0%) and within ±2 rows for 160 of 168 (95.2%),
against 63 of 100 (63.0%) exact and 96 of 100 (96.0%) within ±2 on the tuning set. The higher exactmatch rate on the held-out set reflects the composition of the tuning set, not an anomaly of the model:
as described in Section 3.1, the tuning set was deliberately enriched for highly curved ears, on which the
angular regularity assumption underlying the FFT row detection is most often violated.
Table 5 compares hardware class, dataset size, trait count, and reported accuracy across existing automated
ear phenotyping methods and the present pipeline.
Disparate imaging protocols, varied crop varieties, and custom validation datasets limit direct performance
comparisons across these methods; Table 5 therefore serves as a high-level comparative assessment, not a
controlled baseline experiment on identical samples. For instance, Shi et al. (2022), Li et al. (2025), and
this work report regression-based KC, while others rely on correlation coefficients or geometric indices. For
Gonzalez et al. (2022), yield-component verification focuses on discrete KRN tier classification rather than
continuous regression curves, yielding a 0.68 classification rate over a highly diverse germplasm pool. For
Zaremehrjerdi et al. (2025), the tabulated R2 = 0.87 denotes primary-path KPR evaluation (see footnote b ).
While dedicated industrial 3D profiling frameworks (Sun et al., 2026) achieve near-perfect counting preci-

23

Table 5: Comparative benchmarking of automated maize ear phenotyping methods. Abbreviations are defined in the
Abbreviations section. b Single-path KPR evaluation (Zaremehrjerdi et al., 2025). c Evaluated across the full 574
test ears for KC, and selected benchmark subsets of 150 and 50 ears for KPR and KRN, respectively (Li et al., 2025).
d
Distinct kernel-level phenotypic outputs. e Pearson’s correlation coefficient r. Warman et al. (2021) and Zhao et al.
(2025) are excluded as neither reports KC, KRN, or KPR regression accuracy. f 105 ears used for primary trait (KC,
KRN) validation; a 43-ear subset used for KPR (Fan et al., 2026).
Method

Traitsd Dataset Size Reported Metric Performance

Hardware
Class

Makanza et al. (2018)

Consumer
Camera
Gonzalez et al. (2022)
Document
Scanner
Shi et al. (2022)
Specialist
Camera
Zaremehrjerdi et al. (2025) Smartphone
Camera
Li et al. (2025)
Smartphone
Camera
Sun et al. (2026)
Industrial
Scanner
Fan et al. (2026)
Consumer-grade
Ours

Consumer
Video

4

180 ears

KC r = 0.98e

7

250 ears

KRN Classification Rate = 0.68

3

300 ears

1

136 ears

KC R2 = 0.942; KRN MAE = 0.32; KPR
MAE = 1.07
KPR R2 = 0.87b

3

574 earsc

6

30 ears

10

148 earsf

11

168 ears

KC R2 = 0.764; KRN 80.3% exact (MAE
= 0.23); KPR R2 = 0.645
KC MAPE = 0.91% (R2 = 0.992); KRN
100% exact
KC R2 = 0.980; KRN R2 = 0.888; KPR
R2 = 0.852
KC MAPE = 10.33% (R2 = 0.921); KRN
MAE = 0.75 (95.2% within ±2 rows)

Table 6: KC prediction error stratified by ground-truth KRN, 168-ear held-out set. n denotes the number of ears in
each KRN class.
KRN

n

MAE

Signed ME

MAPE

10
12
14
16
18
20†

20
44
46
44
13
1

22.15
15.27
20.43
41.41
40.54
62.00

+10.55
+4.95
−0.48
+7.27
+22.38
+62.00

14.62%
8.58%
8.26%
11.79%
11.50%
16.89%

†

Only one ear in this KRN class (n = 1); excluded from interpretation.

sion, they rely on rigid, high-cost optical scanning configurations that restrict deployment scale. By contrast,
2D imaging methods (Makanza et al., 2018; Gonzalez et al., 2022; Shi et al., 2022; Zaremehrjerdi et al.,
2025; Li et al., 2025; Fan et al., 2026) maximize accessibility but are limited to surface-projected metrics
and do not recover volumetric or packing geometry. The present pipeline occupies the intermediate position: it delivers spatial packing data and volumetric shape proxies from standard consumer video streams
without specialized hardware calibration, at the cost of a higher kernel count error than the best-performing
projection-based methods. The sources of that error are examined in Section 4.
Held-out KC error was stratified by ground-truth KRN (Table 6) on the 168-ear held-out set to characterize
where this error concentrates.
Mean bounding box aspect ratio was computed separately for the base, middle, and tip thirds of each ear to
evaluate how kernel shape varies systematically along the longitudinal axis of the cob. Table 7 presents the
resulting aspect ratio by thirds, in which mean kernel elongation is highest in the middle third and lowest at
24

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure 14: Per-kernel surface area mapped onto the 3D ear point cloud for each representative ear. Color encodes
surface area (mm2 ).

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure 15: Per-kernel convex hull volume proxy mapped onto the 3D ear point cloud for each representative ear.
Color encodes convex hull volume (mm3 ).

the tip.
Table 7: Ear-level averaged kernel AR across vertical height thirds for 1,091 ears.

Height Band (Third)

Aspect Ratio (Mean ± SD)

Bottom Third (Base)
Middle Third (Mid-ear)
Top Third (Tip)

1.29 ± 0.11
1.33 ± 0.12
1.26 ± 0.09

Representative per-ear trait distributions for the four ears detailed in the Methods are presented in Figures 14–27. These include spatially mapped per-kernel surface area on the 3D ear (Figure 14), per-kernel
convex hull volume proxy on the 3D ear (Figure 15), mean nearest-neighbor distance on the 3D ear (Figure 16), per-kernel surface area histogram (Figure 17), surface area stratified by ear third (Figure 18), convex
hull volume proxy (Figure 19), convex hull volume proxy stratified by ear third (Figure 20), Gabriel graph
neighbor count (Figure 21), mean nearest-neighbor distance (Figure 22), per-row kernel count (Figure 23),
circular mean hue (Figure 24), bounding box aspect ratio (Figure 25), mean hue vs. height (Figure 26), and
mean aspect ratio vs. height (Figure 27). Trait distributions over all ears are shown in Figure 28.

25

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

20
0

50
100
Surface Area (mm²)

(a) 24-P2042_3

60
40
20
0

50
100
Surface Area (mm²)

Number of Kernels

40

100
80
60
40
20
0

Number of Kernels

60

Number of Kernels

Number of Kernels

Figure 16: Mean nearest-neighbor distance mapped onto the 3D ear point cloud for each representative ear. Color
encodes mean 3D Gabriel graph neighbor distance (mm).

(b) 24-P2071_2

30
20
10
0

50
100
Surface Area (mm²)

(c) 24-P2146_2

50
100
Surface Area (mm²)

(d) 24-P2199_1

20

0
30

Number of Kernels

Number of Kernels

10

20
10
0
30
20
10
0

50
100
Surface Area (mm²)

(a) 24-P2042_3

50
40
30
20
10
0
50
40
30
20
10
0
50
40
30
20
10
0

30

15

20

10

10

5

0

0

30

15

20
10
0

(b) 24-P2071_2

10
5
0

30

15

20

10

10

5

0

50
100
Surface Area (mm²)

Number of Kernels

30

Number of Kernels

Figure 17: Per-kernel 3D surface area, estimated via the Ball-Pivoting Algorithm (BPA).

50
100
Surface Area (mm²)

(c) 24-P2146_2

0

50
100
Surface Area (mm²)

(d) 24-P2199_1

Figure 18: Per-kernel surface area distributions stratified by ear third, from the base (bottom row) to the tip (top row).

26

20
0

100
75
50
25
0

100
200
Convex Hull Volume (mm³)

(a) 24-P2042_3

60
40
20
0

100
200
Convex Hull Volume (mm³)

Number of Kernels

40

Number of Kernels

Number of Kernels

Number of Kernels

30
60

(b) 24-P2071_2

20
10
0

100
200
Convex Hull Volume (mm³)

100
200
Convex Hull Volume (mm³)

(c) 24-P2146_2

(d) 24-P2199_1

Figure 19: Per-kernel convex hull volume proxy distributions.

0
30

Number of Kernels

Number of Kernels

10

20
10
0
30
20
10
0

100
200
Convex Hull Volume (mm³)

(a) 24-P2042_3

30

15

20

10

10

5

0
30

0

Number of Kernels

20

50
40
30
20
10
0
50
40
30
20
10
0
50
40
30
20
10
0

Number of Kernels

30

20
10
0
30

15
10
5
0
15

20

10

10
0

100
200
Convex Hull Volume (mm³)

(b) 24-P2071_2

5
0

100
200
Convex Hull Volume (mm³)

(c) 24-P2146_2

100
200
Convex Hull Volume (mm³)

(d) 24-P2199_1

1 2 3 4 5 6 7 8 9 10
Neighbor Count

(a) 24-P2042_3

60
40
20
0

1 2 3 4 5 6 7 8 9 10
Neighbor Count

(b) 24-P2071_2

Number of Kernels

125
100
75
50
25
0

Number of Kernels

100
80
60
40
20
0

Number of Kernels

Number of Kernels

Figure 20: Per-kernel convex hull volume proxy distributions stratified by ear third, from the base (bottom row) to the
tip (top row).

1 2 3 4 5 6 7 8 9 10
Neighbor Count

(c) 24-P2146_2

Figure 21: Gabriel graph neighbor count distributions.

27

40
30
20
10
0

1 2 3 4 5 6 7 8 9 10
Neighbor Count

(d) 24-P2199_1

50
0

10
20
30
Mean NN Distance (mm)

(a) 24-P2042_3

150

Number of Kernels

100

200
150
100
50
0

Number of Kernels

Number of Kernels

Number of Kernels

150

100

10
20
30
Mean NN Distance (mm)

50
0

(b) 24-P2071_2

60
40
20
0

10
20
30
Mean NN Distance (mm)

(c) 24-P2146_2

10
20
30
Mean NN Distance (mm)

(d) 24-P2199_1

30

30

Kernel Count

Kernel Count

Figure 22: Distributions of 3D mean nearest-neighbor distance to each Gabriel graph neighbor.

20
10
0

20
10
0

1 2 3 4 5 6 7 8 9 10 11 12 13 14
Row

1 2 3 4 5 6 7 8 9 10 11 12 13 14
Row

15

(b) 24-P2071_2

Kernel Count

Kernel Count

(a) 24-P2042_3

10
5
0

20
10
0

1 2 3 4 5 6 7 8 9 10 11 12 13 14
Row

1

2

(c) 24-P2146_2

3

4

5

Row

6

7

8

9

10

(d) 24-P2199_1

Figure 23: Per-row kernel count distributions, derived from the row chain graph.
0° 10°
20°

30°

Kernel count

80

40°
50°

60

Kernel count

100

Circular Mean 31.8°

40
20
0

120
100
80
60
40
20
0

(a) 24-P2042_3

40

0° 10°
20°

30°

40°
50°

30

Circular Mean 23.7°

20

30°

40°
50°
Circular Mean 30.3°

(b) 24-P2071_2

Kernel count

Kernel count

50

0° 10°
20°

10
0
(c) 24-P2146_2

30
25
20
15
10
5
0

0° 10°
20°

30°

40°
50°
Circular Mean 28.3°

(d) 24-P2199_1

Figure 24: Per-kernel circular mean hue distributions for the four representative ears. Each bar is colored to match
the kernel hue angle it represents.

28

0

40
20
0

1.5
2.0
2.5
BB Aspect Ratio

1.5
2.0
2.5
BB Aspect Ratio

(a) 24-P2042_3

Number of Kernels

20

40
30
20
10
0

Number of Kernels

Number of Kernels

Number of Kernels

40

30
20
10
0

1.5
2.0
2.5
BB Aspect Ratio

(b) 24-P2071_2

1.5
2.0
2.5
BB Aspect Ratio

(c) 24-P2146_2

(d) 24-P2199_1

0
90
-10

50
-60

0-1

0

0

Circular Mean Hue (°)

360°
300°
240°
180°
120°
60°
0°

90
-10

50
-60

0-1

0

0

Circular Mean Hue (°)

360°
300°
240°
180°
120°
60°
0°

90
-10

50
-60

90
-10

0-1

0

50
-60

0-1

360°
300°
240°
180°
120°
60°
0°

0

Circular Mean Hue (°)

360°
300°
240°
180°
120°
60°
0°

0

Circular Mean Hue (°)

Figure 25: Per-kernel 3D bounding box aspect ratio distributions.

Height Band (10% bins, Base Tip)

Height Band (10% bins, Base Tip)

Height Band (10% bins, Base Tip)

Height Band (10% bins, Base Tip)

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

1.5

2.5

2

2.0

1

1.5

0
90
-10

50
-60

0

0

0

90
-10

0
0-1

0
90
-10

Mean BB Aspect Ratio

1
0

50
-60

0
0-1

0
90
-10

2.0

3

0-1

1.5

0

50
-60

0-1

0

0

1

2

50
-60

1.5

2.0

2.5

Height Band (10% bins, base tip)

Height Band (10% bins, base tip)

Height Band (10% bins, base tip)

Height Band (10% bins, base tip)

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure 27: Mean bounding-box aspect ratio per 10% height band (base to tip).

29

Aspect Ratio

1

2

3

Aspect Ratio

2.0

2.5

Aspect Ratio

2

3

Mean BB Aspect Ratio

2.5

Mean BB Aspect Ratio

3

Aspect Ratio

Mean BB Aspect Ratio

Figure 26: Circular mean hue per 10% height band (base to tip) for the four representative ears. Each bar is colored
to match the kernel hue angle it represents.

Number of Ears

Number of Ears

60
200
100
0

40
20
0

8 10 12 14 16 18 20 22 24
KRN

0

80
60
40
20
0

3.5

4.0
4.5
5.0
Mean Neighbor Count

50
4

6
8 10 12 14
Mean NN Distance (mm)

(d) Mean 3D nearest-neighbor distance distribution.

150

100

Number of Ears

Number of Ears

100

0

5.5

(c) Mean Gabriel graph neighbor count distribution.

75
50
25
0

600

(b) Kernel count distribution.

Number of Ears

Number of Ears

(a) KRN distribution.

200
400
Total Kernel Count

100
50
0

20 40 60 80 100
Mean Kernel Surface Area (mm²)

(e) Mean kernel 3D surface area distribution.

0
50
100
150
200
Mean Kernel Convex Hull Volume (mm³)

(f) Mean kernel convex hull volume proxy distribution.

Figure 28: Trait distributions across all 1,091 ears.

30

3.4

Manually Annotated Subset Validation

Spatial arrangement and packing traits require dense per-kernel visual tracking, which is prohibitively laborintensive to perform manually at scale. An exhaustive manual validation was therefore performed on the
six-ear manually annotated subset, drawn from an independent field study with no overlap with the 1,091-ear
reconstructed population or the 268-ear labeled dataset (Section 2), to verify spatial arrangement and packing
accuracy. The pipeline’s tracking fidelity on this subset is evaluated through kernels-per-row (KPR) and
Gabriel graph neighbor-count (packing) distributions, quantified by the Earth-Mover Distance (Section 2.4).
A representative example (Sample 1) is shown in Figure 29. The predicted and ground-truth KRN for all
six samples are summarized in Table 8, and the per-ear EMD values for both traits in Table 9; the remaining
individual distribution plots (Samples 2–6) are provided in the Supplemental Material.
Gabriel graph neighbor-count agreement is uniformly close, with per-ear EMD between 0.33 and 0.84 (mean
0.57), every ear falling below the single-count threshold defined in Section 2.4. Kernels-per-row agreement
is less consistent, with per-ear EMD between 0.75 and 7.58 (mean 3.04), concentrated in Samples 4 and 6.
These are the same two ears for which KRN was underpredicted (Table 8); the mechanism linking the two
is examined in Section 4.
4

Ground Truth KPR
Predicted KPR
EMD = 1.25

Count

Count

3
2
1
0

32

34

36 38 40
Kernels in row

42

44

280
240
200
160
120
80
40
0

Ground Truth Neighbor Count
Predicted Neighbor Count
EMD = 0.33

1 2 3 4 5 6 7 8 9 10
Neighbor count

(a) KPR distribution

(b) Neighbor count distribution

Figure 29: Representative kernels-per-row (KPR) and Gabriel graph neighbor-count accuracy for one ear (Sample
1) from the six-ear manually annotated subset.

Table 8: Predicted vs. ground-truth KRN per ear for the manually validated subset.

Sample

Predicted KRN

GT KRN

1
2
3
4
5
6

16
16
14
16
16
16

16
16
14
18
16
18

31

Table 9: Earth-Mover Distance between automated and manually annotated distributions, per ear, for the six-ear
manually annotated subset. Values are taken from Figure 29 (Sample 1) and Figure A.2 and Figure A.3 (Samples 2–6).

3.5

Sample

KPR EMD (kernels)

Neighbor Count EMD (neighbors)

1
2
3
4
5
6

1.25
0.75
1.21
7.58
2.62
4.85

0.33
0.84
0.41
0.61
0.83
0.42

Mean

3.04

0.57

Computational Cost and Throughput

The automated workflow was deployed at scale across all 1,091 ears to evaluate pipeline scalability. All
downstream analytical stages, including cylindrical unwrapping, CLAHE contrast enhancement, CPSAM
segmentation, BPA surface reconstruction, convex hull volume, hue and aspect ratio extraction, Gabriel
graph packing, and FFT/row-chain-based row and kernel-per-row estimation, were executed on the Iowa
State University Nova high-performance computing cluster.
A detailed breakdown of the processing throughput and hardware configurations per ear is provided in Table 10. Downstream trait extraction, measured from the calibrated, PCA-aligned point cloud (Section 2.1)
through final trait output, averaged approximately 10 seconds per ear using a single NVIDIA A100 GPU.
Applied across the full dataset of 1,091 ears, this component requires a total wall-clock time of approximately 3 hours. This processing footprint occurs downstream of the upstream video-to-point-cloud reconstruction stage (COLMAP (Schönberger and Frahm, 2016) pose estimation and NeRF training), which
follows Young et al. (2026); NeRF training alone averages approximately 14 minutes per ear in that platform.
Table 10: Computational cost per ear, by pipeline stage.

Stage

Hardware

NeRF traininga

NVIDIA A100
Trait extraction (this work) NVIDIA A100
a

Time per ear
∼14 min
∼10 s

NeRF training only, following Young et al. (2026).

Of the 268-ear labeled dataset, 100 ears were used for parameter tuning and the remaining 168 reserved as
a held-out set; the tuned pipeline was subsequently applied to all 1,091 reconstructed ears. On the 168-ear
held-out set, the pipeline achieved a kernel count R2 of 0.921 (MAPE = 10.33%) and KRN within ±2
rows for 95.2% of ears, operating on point clouds from the low-cost turntable imaging platform of Young
et al. (2026) without task-specific model training. Trait extraction downstream of point cloud reconstruction
required approximately 10 seconds per ear on a single NVIDIA A100 GPU (Section 3.5), or roughly 3 hours
in total across the full 1,091-ear dataset, supporting the pipeline’s suitability for breeding-scale deployment
when paired with the low-cost acquisition hardware of Young et al. (2026).

4 Discussion and Conclusions
The three validation tiers, synthetic, held-out, and manually annotated, together isolate distinct sources
of pipeline error, and the sections below interpret each in turn before addressing the pipeline’s structural
limitations and broader implications for breeding-scale phenotyping.
Triple-juxtaposed unwrapping eliminated seam-boundary double-counting, a failure mode inherent to naive
32

cylindrical projection of roughly cylindrical objects such as maize ears. Most row count prediction error
fell within two rows, and larger deviations arose only for ears with non-aligned rows, where the angular
regularity assumption underlying the FFT approach is violated. The same constraint appeared in the sixear manually annotated subset: the adaptive FFT predicted the exact KRN for four of the six ears, and the
remaining two deviated by two rows because of minor axial row non-alignment. This same six-ear subset separates the two packing-related outputs cleanly (Table 9). Gabriel graph neighbor-count distributions
agree closely with manual annotation on every ear, whereas kernels-per-row distributions agree less consistently, with the disagreement concentrated in Samples 4 and 6 (Section 3.4). Those are precisely the two ears
whose KRN was underpredicted as 16 against a ground truth of 18 (Table 8). This coupling is structural: the
row chain graph merges chains until exactly R̂ remain (Section 2.2), so an underestimated R̂ forces kernels
from two true rows into a single predicted chain, inflating the predicted kernels-per-row for the merged chain
and shifting the distribution away from ground truth. KPR accuracy is therefore conditional on correct KRN,
and the KPR EMD reflects the downstream consequence of the row-detection limitation. Sample 5 departs
from this pattern, showing moderate KPR disagreement despite correct KRN. Neighbor count, computed
directly from the Gabriel graph on kernel centroids without reference to R̂ (Section 2.3), is unaffected by
this coupling, consistent with its uniformly low EMD.
Turning from row structure to kernel count itself, the kernel count MAPE of 10.33% exceeds that reported by
several 2D consumer-hardware methods, consistent with reconstruction noise compounding through cylindrical projection. NeRF reconstructions of tightly packed kernels blur the boundaries between adjacent
kernels, so CPSAM sometimes merges two true kernels into one segment (undercounting) and sometimes
splits a single kernel’s blurred boundary into two segments (overcounting). Residuals in Figure 12 widen
with increasing ground-truth kernel count, consistent with a proportional rather than additive error. Stratifying held-out KC error by ground-truth KRN (Table 6) shows MAPE ranging from 8.26% (KRN= 14)
to 14.62% (KRN= 10) across the five row classes with more than ten ears each (n, the number of ears in
each KRN class, ranges from 13 to 46 among these five). KRN = 16 and 18 show the highest absolute
error (MAE = 41.41 and 40.54 kernels), roughly double that of the adjacent KRN = 12 and 14 groups
(MAE = 15.27 and 20.43) despite comparable ear counts (n = 44, 46, 44, and 13, respectively, for KRN
= 12, 14, 16, 18). Signed ME is near zero for KRN = 14 (−0.48) but strongly positive for KRN = 10 and
18 (+10.55 and +22.38), indicating overprediction rather than symmetric scatter in those two classes. The
smaller ear count for KRN = 18 (n = 13 ears) and the single KRN = 20 ear (n = 1) warrant caution in
interpreting those estimates.
Beyond count and row accuracy, the pipeline recovers packing and volumetric traits that have not previously been jointly extracted from a consumer imaging workflow. Gabriel graph packing traits (neighbor
count, mean neighbor distance) and volumetric traits (surface area and convex hull volume proxy) describe
spatial organization that has been directly linked to the positional patterning of kernel development and
abortion along the ear (Oury et al., 2022). Mean neighbor distance provides a continuous measure of local kernel packing density, with smaller values indicating more tightly packed kernels, and Gabriel graph
neighbor count provides a complementary per-kernel measure of the same property. Structured-light scanning and micro-CT imaging recover volumetric kernel traits at higher resolution (Sun et al., 2026; Zhao
et al., 2025); the present pipeline instead provides accessible approximations of these traits from consumergrade turntable video, extending 3D kernel phenotyping to breeding programs without specialist equipment
or large capital investment. This dimensional richness is obtained at a measurable cost in count accuracy,
however: Fan et al. (2026) report a kernel count R2 of 0.980 on consumer hardware, exceeding the 0.921
achieved here, and Shi et al. (2022) report a lower KRN MAE using specialist cameras. Both operate on
2D projections of the ear surface and recover neither volumetric shape proxies nor packing geometry, so the
comparison is best read as a trade: projection-based methods optimize the accuracy of the traits a projection
can express, whereas the present pipeline accepts higher error on those same traits in exchange for the radial

33

dimension that a projection discards. Which trade is preferable depends on whether a breeding program
requires count precision or trait breadth.
Three further considerations bound the interpretation of the traits reported here: physical occlusion, the abstractions built into the synthetic validation dataset, and the generalizability of the imaging conditions. The
first is physical occlusion. Because the camera stream captures only the visible outer surface cap of individual kernels, the lateral contact boundaries and the proximal surfaces embedded in the central cob remain
hidden, and the derived convex hull volume proxy and axis-aligned bounding box under-observe the radial
dimension in depth. These metrics are therefore relative shape descriptors and structural spatial proxies
rather than absolute measurements: absolute kernel volume requires destructive shelling or non-destructive
X-ray micro-CT, and their absolute fidelity remains untested against physical measurement. The synthetic
validation establishes only that the pipeline recovers these descriptors faithfully relative to a known generating geometry. The second consideration concerns the synthetic dataset itself. The KRN and KPR MAE
on the 27-ear synthetic set (7.26 rows and 11.28 kernels, Table 4) exceed those measured on the real heldout set by a wide margin, and the kernel count R2 likewise falls to 0.769. The discrepancy is isolated to
the frequency analysis step, and the elevated KPR error follows from the elevated KRN error by the same
chain-merging mechanism described above for Samples 4 and 6. This performance gap is, in fact, the reverse of the typical domain-transfer problem: the mathematical regularity of the synthetic ears penalizes
the adaptive frequency-domain pipeline, whereas real-world biological noise aids it, as illustrated by the
harmonic-locking failure shown in Figure 11. Highly idealized synthetic lattices, while necessary for establishing exact ground truth, introduce sharp phase boundaries and rigid structural symmetries that real ears
do not exhibit. Natural biological variation and physical kernel crowding smooth out these discontinuities in
real maize, so real-world ears suppress the high-frequency harmonics that mislead the peak-selection step on
synthetic data. Future synthetic validation frameworks for spatial-frequency tasks may need to model biological stochasticity, such as localized kernel jitter or growth irregularities, to better approximate real-world
performance. Given this gap, the synthetic dataset should be read as validating the geometric and color
traits under exact ground truth, with the row-structure traits evaluated on the real held-out and manually annotated sets instead. The synthetic baseline also introduces structural abstractions: neighbor configurations
are evaluated via lattice-topological adjacency instead of a Gabriel graph, and hue is defined by population mean parameters rather than the specific per-kernel draw. These control states validate the pipeline’s
capacity to capture relative packing density and topology rather than serving as an exact edge-by-edge or
per-kernel benchmark. The third and final consideration concerns generalizability: the 1,091 ears were imaged on a controlled turntable platform under consistent lighting, and may not span the full range of maize
ear morphologies or field conditions, so performance on varieties with markedly different kernel color, ear
curvature, or packing density has not been tested here.
Beyond these bounding considerations, the axial profiles reveal spatial structure that a single per-ear mean
discards. Across the four representative ears, mean aspect ratio vs. height profiles (Figure 27) show modestly higher kernel elongation in the middle height bands relative to the base and tip. This trend persists
at the population scale: across all 1,091 analyzed ears, the ear-level averaged kernel aspect ratio peaks in
the middle third of the cob and decreases toward both boundaries (Table 7), although the between-band
difference is smaller than the within-band standard deviation. This pattern is consistent with a physiological gradient along the axis, potentially reflecting the sequential nature of pollination or localized resource
competition during the grain-filling window rather than a uniform, ear-wide morphological constraint. Hue
profiles reveal a parallel axial structure: mean hue vs. height profiles (Figure 26) show color gradients that
vary subtly from ear to ear along the developmental axis, and where present, these gradients may reflect
positional variation in carotenoid content, as kernel carotenoid concentration has been shown to vary with
position along the cob (Calvo-Brenes et al., 2019).
This study presents a fully automated, high-throughput pipeline extracting eleven ear- and kernel-level traits

34

(total kernel count, KRN, KPR, surface area, convex hull volume proxy, Gabriel graph neighbor count,
mean neighbor distance, circular mean kernel hue, 3D bounding box aspect ratio, mean hue vs. height, and
mean aspect ratio vs. height) from calibrated 3D point clouds produced by the turntable imaging platform
of Young et al. (2026), with no task-specific training. Validation spanned three tiers: the 27-ear geometric
synthetic dataset, the six-ear manually annotated subset, and the 168-ear held-out set. Across these tiers, the
pipeline recovers kernel count within 10.33% MAPE, KRN within ±2 rows for 95.2% of ears, and kernel
packing topology within a mean Earth-Mover Distance of 0.57 neighbors, using consumer-accessible imaging hardware and no supervised crop-specific models. Applied to 1,091 ears with known genotype identity,
the pipeline yields a multi-trait dataset positioned for phenotype-to-genotype association analyses. That
dataset spans both heritable traits such as KRN and traits sensitive to environmental stress and management
such as KPR (Peng et al., 2011; Liu et al., 2015; Andrade et al., 2002). No association analysis is attempted
here; applying these phenotypes to genotype selection and agronomic management decisions is left to future
work.

Acknowledgments
We thank Ms. Lisa Coffey of the Schnable Lab, who supervised a team of undergraduate assistants who
imaged ears and generated ground truth data. Team members included: Owen Hildebrandt, Roland Stetz,
Isaac Tharp, Haylee Thomas, and Ryan Wolf.
Funding Information
This work was supported by the AI Institute for Resilient Agriculture (USDA-NIFA 2021-67021-35329)
and Iowa State University’s Plant Science Institute.
Conflict of Interest
The authors declare no conflict of interest.
ORCID
Ritwesh A. Kumar: https://orcid.org/0009-0006-1956-5273
Talukder Zaki Jubery: https://orcid.org/0000-0003-4107-1617
Patrick Schnable: https://orcid.org/0000-0001-9169-5204
Adarsh Krishnamurthy: https://orcid.org/0000-0002-5900-1863
Baskar Ganapathysubramanian: https://orcid.org/0000-0002-8931-4852

35

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Supplementary Material
Manual Annotation Protocol
As described in Section 2, both manually annotated datasets used in this study, the 268-ear labeled dataset
and the six-ear manually annotated subset, followed the same base-to-tip numbering convention for kernel
rows, with row 1 nearest the ear base and row numbers increasing toward the tip. Rows were counted
sequentially around the full circumference of the ear, starting from an arbitrary row designated as row 1; the
starting point does not affect the reported kernel row number or kernels-per-row values, which depend only
on the total count and per-row kernel assignment, not on which physical row was labeled first. Figure A.1
illustrates this convention for Sample 1 of the six-ear subset: each kernel row is traced and numbered starting
from row 1 at the base, with kernel position within a row numbered in the same base-to-tip direction. The
two datasets differ in annotation depth and annotator count.

Figure A.1: Kernel row and kernel-position numbering protocol used for manual annotation of the six-ear manually
annotated subset, illustrated for Sample 1. Traced lines mark individual rows (labeled 1, 2, 3, ...), numbered from the
ear base to the tip; kernel position within each row was numbered in the same direction. All six ears were annotated
according to this convention.

For the 268-ear labeled dataset, a single expert annotator recorded only total kernel count (KC) and kernel
row number (KRN) per ear, without resolving individual kernel positions within each row. For the six-ear
manually annotated subset, three independent annotators (two ears each) additionally recorded full perrow kernel identity and position, following the same base-to-tip convention illustrated here, enabling the
exhaustive kernels-per-row and packing validation reported in Section 3.4. This numbering was applied
identically across all six ears in that subset, so that per-row kernel identity and position were unambiguous
and consistent with the convention despite each ear being labeled by a single individual.

Additional Distribution Plots
This section contains the additional distribution plots validating the tracking and packing performance of the
automated pipeline. While Sample 1 is detailed above, Figure A.2 and Figure A.3 complete the evaluation
across the entire six-ear manually annotated subset introduced in Section 3.4. Specifically, they present the
39

remaining kernels-per-row (KPR) and Gabriel graph neighbor-count accuracy distributions for Samples 2–6.
The corresponding Earth-Mover Distances are collected in Table 9.
5
4
3
2

2
1

1
0

Ground Truth KPR
Predicted KPR
EMD = 1.21

3
Count

Count

4

Ground Truth KPR
Predicted KPR
EMD = 0.75

32

34

36
38
40
Kernels in row

0

42

(a) Sample 2: KPR distribution

28 30 32 34 36 38 40 42
Kernels in row

(b) Sample 3: KPR distribution
4

Ground Truth KPR
Predicted KPR
EMD = 2.62

6

Ground Truth KPR
Predicted KPR
EMD = 7.58

4

2
1

3
2

0

1
0

10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
Kernels in row

(c) Sample 4: KPR distribution

20 22 24 26 28 30 32 34 36 38
Kernels in row

(d) Sample 5: KPR distribution

4

Ground Truth KPR
Predicted KPR
EMD = 4.85

3
Count

Count

5

Count

3

2
1
0

4

6

8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Kernels in row

(e) Sample 6: KPR distribution
Figure A.2: KPR accuracy distributions for Samples 2–6.

40

Ground Truth Neighbor Count
Predicted Neighbor Count
EMD = 0.84
Count

Count

320
280
240
200
160
120
80
40
0

1 2 3 4 5 6 7 8 9 10
Neighbor count

320
280
240
200
160
120
80
40
0

270
240
210
180
150
120
90
60
30
0

Ground Truth Neighbor Count
Predicted Neighbor Count
EMD = 0.61

1 2 3 4 5 6 7 8 9 10
Neighbor count

270
240
210
180
150
120
90
60
30
0

Count

(c) Sample 4: Neighbor count distribution
270
240
210
180
150
120
90
60
30
0

1 2 3 4 5 6 7 8 9 10
Neighbor count

(b) Sample 3: Neighbor count distribution

Count

Count

(a) Sample 2: Neighbor count distribution

Ground Truth Neighbor Count
Predicted Neighbor Count
EMD = 0.41

Ground Truth Neighbor Count
Predicted Neighbor Count
EMD = 0.83

1 2 3 4 5 6 7 8 9 10
Neighbor count

(d) Sample 5: Neighbor count distribution
Ground Truth Neighbor Count
Predicted Neighbor Count
EMD = 0.42

1 2 3 4 5 6 7 8 9 10
Neighbor count

(e) Sample 6: Neighbor count distribution
Figure A.3: Neighbor-count accuracy distributions for Samples 2–6.

Corn Ear 3D Reconstruction Using Stationary Camera
This section outlines the steps, adapted from Young et al. (2026), for reconstructing the corn ear in 3D from
a stationary camera.
360° Video Acquisition

Each ear was placed on a motorized rotating turntable against a dark background. A stationary camera
captured a continuous 360° video as the ear completed a full rotation. The known diameter of the cylindrical
sample holder served as a metric reference for subsequent distance calibration. Raw video frames were
extracted and used as input to the 3D reconstruction pipeline. Figure A.4 shows representative frames of
each of the four representative ears mounted on the turntable prior to acquisition.
Camera Pose Estimation via COLMAP

Raw video frames were processed using COLMAP (Schönberger and Frahm, 2016), a Structure-fromMotion (SfM) and Multi-View Stereo (MVS) pipeline. COLMAP estimated the intrinsic and extrinsic
camera parameters for each frame, producing a sparse 3D reconstruction and a set of camera poses describing the camera’s position and orientation relative to the scene at each frame. These poses served as
input to the NeRF reconstruction described next.

41

(a) 24-P2042_3

(b) 24-P2071_2

(c) 24-P2146_2

(d) 24-P2199_1

Figure A.4: Representative video frames showing each of the four corn ears mounted on the motorized rotating
turntable prior to 360° video acquisition, following the imaging setup of Young et al. (2026). The known diameter of
the cylindrical sample holder provided the metric reference for subsequent distance calibration.

Neural Radiance Field Reconstruction

A Neural Radiance Field (NeRF) (Mildenhall et al., 2022) was trained on the video frames and their corresponding camera poses to produce a dense, photorealistic 3D representation of the scene. The trained NeRF
was then queried to extract a dense 3D point cloud of the full scene, including the ear and turntable.
Ear Isolation via Density-Based Separation

The raw scene point cloud contains points from both the turntable and the corn ear, which differ in spatial
point density: the corn ear forms a compact, high-density cluster, whereas the turntable and surrounding
structures are more sparsely distributed. A density-based separation method retained only the high-density
cluster corresponding to the ear.
Distance Calibration

Because NeRF reconstructions are defined up to an arbitrary scale factor, the point cloud was calibrated
to physical units (millimeters) using the known diameter of a cylindrical sample holder visible in every
frame. The scale factor was computed as the ratio of the known holder diameter to its measured diameter in
the reconstructed point cloud, and all point coordinates were multiplied by this factor to yield a metrically
accurate point cloud in millimeters.

42
</reference>

<statements>
1. The evidence supports three load-bearing conclusions, each developed below from quantified pipeline results and control results.
2. a consumer maize-ear pipeline reached kernel-count R² = 0.921 but only relative volume proxies
3. and \(z\) is the extracted trait vector
4. maize-ear kernels were segmented and reprojected into 3D trait sets
5. In maize ears, the calibrated point cloud is Z-axis aligned via PCA, cylindrically unwrapped to 2D, contrast-enhanced with CLAHE, segmented with zero-shot Cellpose-SAM using a triple-juxtaposed unwrap to resolve the projection seam, and reprojected to per-kernel 3D point sets for traits including kernel count, kernel row number, kernels per row, surface area, volume proxy, packing geometry, hue and aspect ratio
6. Consumer SfM and neural-rendering pipelines are scalable but inherit registration and reconstruction error
7. NeRF blurring can merge or split kernels
8. Representation choice must be validated against ground truth and bias-corrected for trait analysis
9. Maize-ear phenotyping is explicitly positioned for phenotype-to-genotype association analyses, with 1,091 ears of known genotype identity; kernel count reached R² = 0.921 with MAPE = 10.33%, and kernel row number was within two rows for 160 of 168 held-out ears
10. Kernel row number is described as established early in ear development and exhibiting high broad-sense heritability with consistent QTL associations
11. For example, variety-level classification failure in rice, systematic volume underestimation in L1-trained deep models, and relative volume proxies in maize all show that trait extraction must include calibration, cross-validation and ground-truth checks
12. Validation should be multi-level: metrological, computational, biological and operational, because the cited studies evaluate accuracy, convergence, biological discrimination and field operation
13. Are traits biologically meaningful? ANOVA F-statistics, genotype association, heritability, cross-validation R²
14. maize KRN is described as highly heritable with QTL associations
15. The case studies show how the architecture should specialize across benchtop, consumer organ-level and field scales
16. Organ-level kernel traits are feasible without specialist hardware, but absolute volume requires caution
17. 1,091 ears were processed; kernel count R² was 0.921 and KRN was within two rows for 160/168 ears; volumes are relative proxies not verified against destructive or micro-CT measurement
18. Volume carving can produce systematic volume error depending on view count [11], deep-learning volume estimates can be systematically underestimated with L1 loss [11], voxel carving can overestimate volumes because of missed concavities and occlusions [16], and maize kernel volumes are described as relative proxies rather than absolute measurements [10]
19. For head-, ear- and canopy-level traits, passive RGB SfM, MVS, NeRF and 3DGS are viable at breeding scale when combined with segmentation, calibrated poses and environmental scheduling, as shown by maize-ear consumer pipelines, Wheat3DGS field reconstruction and PhenoRob-F field validation [10][15][4]
20. Evidence that would change the design judgment would include a published joint MPC over acquisition variables with trait-uncertainty cost, a consolidated variance decomposition from registration and mixed-pixel error into grain-trait error, and long-term field validation beyond the 27-day maize deployment and single-season wheat and maize case studies [3][15][10]
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.