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PhenoRob-F: An autonomous ground-based robot for high-throughput phenotyping of field crops - PMC

Understanding the genetic basis of quantitative traits related to crop growth, yield, and stress response requires the acquisition of large-scale, high-quality phenotypic datasets. High-throughput phenotyping platforms have become effective tools ...

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Plant Phenomics
. 2025 Aug 13;7(3):100085. doi:
10.1016/j.plaphe.2025.100085

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PhenoRob-F: An autonomous ground-based robot for high-throughput phenotyping of field crops

Meng Yang

Meng Yang

a
College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

b
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

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Zhengda Li

Zhengda Li

a
College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

b
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

c
Wuhan X-Agriculture Intelligent Technology Co., Ltd, Wuhan, 430070, PR China

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Zhengda Li

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Jiale Cui

Jiale Cui

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College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

b
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

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Yang Shao

Yang Shao

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College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

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National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

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Ruifang Zhai

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College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China

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Wuhan X-Agriculture Intelligent Technology Co., Ltd, Wuhan, 430070, PR China

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Wanneng Yang

Wanneng Yang

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College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

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National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

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⁎
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Peng Song

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National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

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a
College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

b
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China

c
Wuhan X-Agriculture Intelligent Technology Co., Ltd, Wuhan, 430070, PR China

d
College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China

⁎
Corresponding author. College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China

⁎⁎
Corresponding author. College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China
songp@mail.hzau.edu.cn

1
These authors contributed equally to this work.

Received 2025 Mar 15; Revised 2025 May 29; Accepted 2025 Jun 22; Collection date 2025 Sep.

© 2025 The Authors

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

PMC Copyright notice

PMCID: PMC12709880 PMID:
41416203

Abstract

Understanding the genetic basis of quantitative traits related to crop growth, yield, and stress response requires the acquisition of large-scale, high-quality phenotypic datasets. High-throughput phenotyping platforms have become effective tools for meeting this requirement. Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads, their operational flexibility, and their proximity to crops, which allows for higher imaging resolution. In this study, we introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions. The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field phenotyping, with integrated visual and satellite navigation systems enabling autonomous operation. We validated the performance of the robot through a series of experiments involving various crop canopies. By capturing RGB images of rice and wheat, we independently performed wheat ear detection and rice panicle segmentation. For wheat ear detection, we achieve a precision of 0.783, a recall of 0.822, and a mean average precision (mAP) of 0.853 when the YOLOv8m model is used. For rice panicle segmentation, the SegFormer_B0 model yielded a mean intersection over union (mIoU) of 0.949 and an accuracy of 0.987. Additionally, by capturing RGB-D data of maize canopies, we performed 3D reconstructions to calculate plant height, achieving an R
2
of 0.99 compared with manual measurements. Similar experiments with rapeseed yielded an R
2
of 0.97. Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories, with classification accuracies ranging from 0.977 to 0.996. Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.

Keywords:
Phenotyping robot, High-throughput, Data processing, Autonomous control
1. Introduction

Global agriculture faces the dual challenges of meeting the increasing demand for food, feed, fiber, and fuel while adapting to the impacts of climate change, including drought, extreme temperatures, heavy rainfall, land degradation, and water scarcity [
1
]. To address these challenges, plant breeders are developing crop varieties that are high yielding, stress resistant, and adaptable to future climatic conditions [
2
,
3
]. The foundation of these breeding efforts lies in genetic diversity [
4
,
5
], and extensive research on crop genomics has been conducted by experts worldwide, yielding commendable outcomes. However, the translation of genetic information into practical breeding outcomes requires comprehensive phenotypic data—specifically, data that link genetic variation to observable traits such as growth, yield, and stress response [
6
,
7
]. Traditionally, phenotypic traits have been measured manually—a process that is labor intensive, time-consuming, and subject to human error. The advent of high-throughput phenotyping platforms has revolutionized this field, providing tools to collect phenotypic data at a scale and precision previously unattainable. Initial applications of these technologies were confined mainly to controlled environments such as growth chambers or greenhouses, where conditions can be tightly regulated. However, phenotypic data collected in these environments may not accurately reflect the performance of crops under natural field conditions [
8
,
9
].

Recent advancements have involved the integration of sensors with mobile platforms to enable phenotyping in the field, with minimal human intervention [
10
]. These field-based phenotyping systems are broadly categorized into aerial and ground-based platforms. Ground-based systems, such as gantry or cable-driven platforms, have the advantages of noninvasive data collection with high payload capacity and precise control over sensor positioning. However, these systems are typically limited by their fixed installation sites, high costs, and restricted coverage areas. Moreover, these systems generally capture only top-down views of crops, missing critical side-view data that can be essential for comprehensive phenotyping [
[11]
,
[12]
,
[13]
,
[14]
]. Unmanned aerial vehicles (UAVs) have become versatile tools for field phenotyping and are capable of covering large areas rapidly [
15
]. Despite their advantages, UAVs are constrained by their limited payload capacity, short flight duration, and variability in data quality caused by changes in flight altitude and atmospheric conditions [
16
]. Some researchers have explored the use of manned helicopters or tethered balloons for phenotyping [
17
,
18
], which offer greater payloads and extended operation times; however, these methods have disadvantages such as poor positional accuracy, high operational costs, and limited usability. In contrast, ground-based mobile phenotyping systems, which combine the mobility of UAVs with the stability and precision of ground-based platforms, are a suitable alternative. Early implementations involved mounting sensors on tractors, which, owing to their large payload capacity, could carry multiple sensors and associated equipment. Tractors can travel in the field with substantial power [
19
], but their large size and weight pose challenges, such as soil compaction and limited maneuverability in densely planted fields. Additionally, vibrations from internal combustion engines can compromise data quality, and the need for experienced operators further limits their applicability in phenotyping.

On the other hand, autonomous mobile robots present a lighter, more flexible solution. These robots can be equipped with multiple sensors positioned and oriented as needed to capture detailed phenotypic data. Autonomous mobile robots are designed to navigate different plots with minimal impact on the soil and use electric motors powered by batteries to reduce noise and vibration, thereby ensuring data quality [
20
,
21
]. Unlike UAVs, these robots can maintain close proximity to crops, resulting in higher-resolution images, and they can concentrate on individual plants. Moreover, these robots can operate fully autonomously, eliminating the need for human operators [
22
].

Autonomously mobile phenotyping robots can be classified into wheeled, tracked, and wheel-legged types on the basis of their locomotion systems. The choice of locomotion depends on the terrain and the specific phenotyping needs. Wheeled robots offer simplicity and high maneuverability, whereas tracked robots provide superior terrain adaptability, making them suitable for rough or muddy fields. Wheel-legged robots combine the speed and efficiency of wheeled robots with the terrain adaptability of legged robots, improving mobility and the ability to adjust their size (height and width) to fit different field layouts [
23
,
24
]. However, the complexity and cost of wheel-legged systems are often not justified by their benefits, causing most researchers to favor simpler designs. Phenotyping robots can also be categorized on the basis of their operational mode into interrow and cross-row types. Inter row robots are typically smaller and designed to move between crop rows, making them ideal for dense planting conditions. These robots can collect detailed phenotypic data from individual plants from multiple angles, including side and top views. Their small size allows for greater flexibility during row changes, minimizing land usage and maximizing the planting area. For taller crops, interrow robots provide greater convenience in adjusting sensor height, with relatively low costs [
25
]. In contrast, cross-row robots are larger and move above the crops, providing greater stability and data collection quality. These robots can carry heavier payloads and provide more space for mounting additional sensors, lighting systems, and other necessary equipment, with higher levels of automation and user interaction. Larger battery capacities also ensure longer operating times [
[26]
,
[27]
,
[28]
,
[29]
,
[30]
].

In the previous discussion, we covered various types of high-throughput phenotyping platforms used in the field. Each platform is capable of collecting different types of phenotypic data, depending on the sensors used, the perspective of the crop being captured, and whether the data are collected at the individual plant level or across a field. Each platform has its own advantages and limitations, depending on the specific crop parameters being measured. In the realm of crop phenotyping, traditional methods rely heavily on manual labor, which is time-consuming, labor intensive, and prone to human error [
31
,
32
]. Additionally, existing automated systems often lack the flexibility to adapt to diverse field conditions and fail to provide standardized, high-throughput data collection. To bridge these gaps, we designed an autonomous mobile phenotyping robot featuring a wheeled structure for cross-row movement, integrating visual and satellite navigation to enable autonomous operation. The contributions of this work are as follows: (1) We designed an autonomous mobile platform and phenotyping system tailored to the needs of phenotypic data collection and developed corresponding functional software, resulting in a convenient and automated phenotyping process. (2) Through a series of phenotyping experiments, we successfully extracted the morphological and physiological–biochemical characteristics of crops, validating the practicality and effectiveness of the system.
2. Development of PhenoRob-F

2.1. System overview

Field-based phenotyping provides critical insights into the real-world performance of crops under natural environmental conditions. Our approach centers on a “sensor-to-plant” phenotyping model, where sensors are brought directly to the field for in situ measurements. The PhenoRob-F system that we developed consists of two main components: a mobile platform and a phenotyping collection system. The mobile platform, which is designed with a wheeled structure, moves across crop rows, carrying the phenotyping system mounted above it. The platform is equipped with an autonomous navigation system that combines visual information with satellite positioning to follow preset paths to designated field locations, adjusting its movement on the basis of crop growth conditions. Manual remote control is also available as an option. The core of the phenotyping collection system is the array of sensors, which are supported by an electrical control system and data acquisition software tailored to the specific needs of field phenotyping. The system can also operate fully autonomously, triggering sensor operations based on the location of the platform to ensure seamless data collection without human intervention.
2.2. Design of the mobile platform

2.2.1. Hardware structure

To ensure that the phenotyping module positions itself above the target crops accurately, a stable and flexible mobile platform is essential. We designed a platform with an arch-shaped structure that spans crop rows, allowing it to pass over plants of varying heights and structures. After consultation with crop breeders across different research fields, we determined the platform dimensions on the basis of common planting configurations: an internal wheel width of 1.8 ​m, an external width of 2.05 ​m, and a length of 2 ​m. The platform is equipped with narrow, V-patterned agricultural tractor tires, 60 ​cm in diameter and 10 ​cm wide, suitable for a variety of dryland terrains and occupies minimal space between rows. To prevent soil compaction and protect the crop growth environment, we minimized the weight of the platform as much as possible while ensuring structural integrity. The final design, shown in
Fig. 1
(D), balances weight and strength effectively. To improve mobility in the field, the platform uses a four-wheel drive system, with each wheel powered by a 750 ​W motor connected via a 100:1 harmonic reducer, mounted horizontally. Four 400 ​W motors, each with a 100:1 planetary reducer, control the steering, with the motors mounted vertically. The single-wheel structure of the platform, which is shown in
Fig. 1
(E), was designed for stability and flexibility. The power source of the robot is supplied by a 100AH lithium battery pack. With this configuration, our platform can reach a maximum speed of 1 ​m/s. The platform is capable of stably collecting phenotypic data for approximately 3–6 ​h, ensuring efficient and continuous phenotyping operations in the field.

Fig. 1.

Open in a new tab

(A) 3D structure of the phenotyping module, (B) sensor unit, (C) illuminant, (D) 3D structure of the mobile platform, and (E) structure of the robot wheel in detail.
2.2.2. Control methods

The control system of the mobile platform involves a four-layer structure, as shown in
Fig. 2
. Sensor layer: We use an Intel RealSense D435i for visual navigation and a Huace CGI-410 combined navigation device for positioning. When it is stationary in the field with RTK differential signals, CGI-410 can achieve centimeter-level accuracy with a maximum data update rate of 100 ​Hz. Upper navigation system: This system runs on a Jetson Orin Nano control board, which receives real-time RGB-D data and GNSS raw data through USB and RS232 connections, respectively. Mid-level control system: This system is managed by an STM32F103ZET6 microcontroller. Execution system: This system consists of eight servo motors that drive and steer the platform.

Fig. 2.

Open in a new tab

Workflow of the mobile platform control system.
The system processes data in two threads to enable hybrid navigation. The visual navigation thread (core autonomy), which is based on a depth camera, dynamically segments crop regions from RGB images, extracts navigation feature points using depth information, maps these points to a 3D world coordinate system and projects them onto the ground plane to derive real-time navigation lines and parameters that are fed into the main thread, adaptively identifying crop rows at various growth stages to eliminate reliance on predefined paths and enabling autonomous operation without any pregenerated map. The satellite-assisted global planning thread (optional), if a pregenerated map is available, compares the GNSS-derived coordinates of the robot against a high-precision map generated by a DJI Mavic 3 drone for initial global path planning, although this map is not required for autonomous operation, as the visual thread provides self-contained local navigation. The main thread, running on the Jetson Orin Nano, decides between navigation modes: if the map exists, satellite navigation guides the robot to row heads using pure pursuit control; otherwise, visual navigation handles all tasks via fuzzy control for interrow travel, with commands for both navigation modes transmitted via RS232 to the STM32F103ZET6 microcontroller to support seamless switching between map-aided operation and map-agnostic operation.

In addition to autonomous navigation, we design a remote control function, equipping the platform with an FS-i6S transmitter and an FS-iA10B receiver, with a range of 500–1500 ​m under unobstructed conditions. The microcontroller prioritizes remote control commands over autonomous navigation instructions when both are received. The PPM signals from the remote control and navigation system commands are decoded within the microcontroller (MCU). On the basis of the motion model of the mobile platform, the MCU calculates the rotational speeds for the four drive motors and the steering angles for the four steering motors. Then, the MCU sends the commands to the eight servo drivers via the CAN interface using the CANOpen protocol, which then controls the motors. The speed and steering angle of each motor are stabilized by the servo driver using a fuzzy PID control approach, effectively countering nonlinear resistances from the environment. To synchronize the four drive motors and four steering motors, we employ an improved deviation coupling control method, allowing either full or proportional synchronization. The MCU receives feedback on motor speeds and angles, processes it using the improved deviation coupling algorithm, and issues speed compensation signals to each motor. This method is designed to handle the uneven resistance faced by the four wheels under field conditions. All eight motors work in a coordinated manner to execute the movement commands for the mobile platform.
2.3. Design of the phenotyping module

2.3.1. Component units

The phenotyping module is an independent structure mounted on a mobile platform constructed from aluminum profiles for lightweight and flexible mounting needs. The module includes an imaging box (where sensors are typically housed), a lateral rail, a display screen, imaging light sources, and a control box, as shown in
Fig. 1
(A, B, and C). Currently, two main sensors are used: a SPECIM FX17 hyperspectral camera and an Azure Kinect. These sensors collect phenotypic data as the platform moves through the field. SPECIM FX17 captures hyperspectral images in 224 bands ranging from 900 ​nm to 1700 ​nm with an 8 ​nm spectral resolution, 1024 pixels of spatial pixel count and a 38° field of view. The Azure Kinect can capture RGB and depth information at a set frame rate and support various operational modes with different fields of view, resolutions, and working ranges. The Azure Kinect can capture both RGB and depth information from crops, continuously acquire RGB and depth images at a specified frame rate as the robot moves, and support various operating modes, each corresponding to different fields of view, resolutions, and working ranges. The imaging box has multiple mounting points and connectors, allowing for different sensor orientations. The sensors are adjustable laterally on the rail, and their height can be manually adjusted. The imaging light sources, which are specifically configured for the hyperspectral camera, consist of four 500 ​W linear halogen lamps that provide uniform, full-spectrum illumination. These lamps are mounted at a specific height and angle to ensure adequate light intensity on the crop surface. As the crop height increases, the light source can be manually increased.

In terms of hardware, the two cameras transmit the collected data to the industrial control computer (IPC) via the GigE Vision and USB interfaces. The IPC communicates with a motion control card via an Ethernet connection. The motion control card, in turn, controls the power supply of the two cameras and the lighting through an IO interface, and it controls the servo motor of the lateral rail via RS485, as shown in
Fig. 3
.

Fig. 3.

Open in a new tab

Phenotyping module system architecture and data flow.
2.3.2. Phenotyping module functionality

The data collection and control processes of the phenotyping module are managed through custom-developed software built on a Windows platform using C#. This software, which is operated via a touchscreen interface, enables user interaction with the system. The interface, as shown in
Fig. 4
, includes settings for the SPECIM FX17 hyperspectral camera and Azure Kinect, allowing users to adjust parameters such as the depth camera operating mode, color camera resolution, and frame rate of the Kinect, as well as the frame rate and exposure time of the SPECIM camera. In addition, data storage paths can also be chosen. Hyperspectral images (uncorrected) and RGB/depth images (pseudocolor images) are displayed on the interface in real time.

Fig. 4.

Open in a new tab

Phenotype collection software interface.
For automated operation, the software can process location information sent by the mobile platform, automatically triggering data collection when the platform reaches the start of a row and stopping when it reaches the end of a row. The collected data are stored in subfolders named according to the starting position of the row. Additionally, the software has preliminary functionality for extracting and viewing spectral reflectance data. After data collection, users can import the data for analysis, where the software provides corrected spectral images and reflectance curves, enabling researchers to quickly verify the data quality under current conditions. The complete dataset can be exported via a hard drive for further offline phenotypic trait extraction and data analysis.
3. Field experiments and results

In this section, we describe three experiments conducted to validate the ability of PhenoRob-F to collect phenotypic data from various crops. The first experiment involved capturing top-view RGB images of wheat and rice during the heading stage and using the YOLOv8m and SegFormer_B0 models to detect and segment wheat ears and rice panicles, respectively, to count the number of heads or panicles. The second experiment used a depth camera to collect RGB-D information from maize canopies at different growth stages and rape at one time point, enabling precise 3D reconstruction and dynamic tracking of plant height. Furthermore, a correlation analysis between the plant height values obtained and those measured manually was conducted. The third experiment focused on hyperspectral data collection from drought-stressed rice plants, where the spectral data were processed to derive spectral indices for each plant. A model was subsequently developed by using these data, ultimately allowing for accurate classification of drought severity.

3.1. Phenotyping robot operational experiment

To balance image quality and operational speed, we configured the SPECIM FX17 to run at 50 FPS with an exposure time of 20 ​ms. The Azure Kinect's depth camera was set to 15 FPS with a 10 ​ms exposure time, while its RGB camera operated at 15 FPS with a 20 ​ms exposure time. And the robot moved at a speed of 0.094 ​m/s. Two representative phenotyping experiments were conducted to evaluate the performance of the developed robot. The first was conducted on potted corn plants in Wuhan in 2023, and the second was on wheat in fields in Yunnan Province in 2024. In both cases, the robot was used to simultaneously capture RGB-D and hyperspectral data. The detailed experimental setups were as follows: Scenario A: a potted plant area with 9 rows and 3750 pots. Scenario B: A field plot measuring 60 ​m ​× ​30 ​m with 9 ridges.

The experiment showed that the time of robot completing a single round of phenotyping in Scenario A is approximately 2 ​h, and in Scenario B, it's about 2.5 ​h, shown in
Fig. 5
. Overall, when simultaneously collecting RGB-D and hyperspectral data, the robot achieved an efficiency of approximately 1875 pots per hour in pot task and 0.18 acre per hour in filed task.

Fig. 5.

Open in a new tab

Robot working efficiency.
3.2. RGB phenotyping collection and analysis

The wheat canopy image experiment was conducted at the experimental field of Huazhong Agricultural University on April 15, 2023, whereas the rice canopy image experiment occurred at the rice planting base of the university on September 20, 2023. Both experiments were conducted on sunny days, with the crops at maturity. The RGB images were captured using an Azure Kinect DK, positioned vertically downward at a height of 2 ​m from the ground, with a frame rate of 2 fps, a shake speed of 1/30 ​s and a resolution of 2560 ​× ​1440. The phenotyping robot moved at a speed of 0.1 ​m/s, with the visual navigation system adjusting the position of the robot to keep the crops directly beneath the sensor. A total of 159 wheat canopy images and 150 rice canopy images were captured for further offline processing.

To balance computational efficiency and model performance, YOLOv8m was selected as the wheat ear detection model. First, the model was trained on a global wheat head detection dataset, achieving a precision of 0.924, a recall of 0.859, and a mean average precision (mAP) of 0.930 on the GWHD validation set. Second, the model was fine-tuned on our wheat dataset, with manual corrections applied to the annotations. The rice panicle segmentation model, SegFormer_B0, was trained on canopy images collected using mobile devices in rice fields. The images were cropped to 1024x1024 pixels, with 2000 randomly selected patches used as the training and validation sets.

In our experiments, the wheat detection model achieved a precision of 0.783, a recall of 0.822, and a mean average precision (mAP) of 0.853 on the validation set. Some wheat ears, particularly those in the center and edges of the images, where overlapping ears and small sizes posed challenges, were not detected. For rice canopy segmentation, the model achieved a mean intersection over union (mIOU) of 0.949 and an accuracy of 0.987 on the validation set. The process mentioned above is shown in
Fig. 6
.

Fig. 6.

Open in a new tab

Detection process for wheat ears and rice panicles.
The wheat ear detection model performs poorly in detecting overlapping wheat ears at the edges of the images. Owing to the angle, the overlapping area of the ears is large, causing the object detection model to discard detection boxes with high intersection over union (IoU) ratios. This issue can be mitigated by improving image acquisition methods and expanding the dataset to enhance model performance. One of the primary goals of wheat ear detection and rice panicle segmentation is yield prediction. For wheat, in addition to counting the number of ears per unit area, estimating the plant area or the number of plants is crucial. For rice, canopy images alone are insufficient for accurate yield prediction. Combining canopy images with other phenotypic traits of the plants, such as the number of grains per panicle and grain weight, may increase the accuracy of yield predictions. This process can be achieved by capturing images from multiple angles and using various sensors to collect more comprehensive phenotypic data. While yield prediction based on RGB images is relatively convenient and quick, the current accuracy of this method still need to be improved. However, the use of robots to collect canopy RGB images can standardize the image acquisition process, potentially improving model performance.
3.3. 3D phenotyping collection and analysis

We collected RGB-D data from a batch of maize plants via Azure Kinect DK, which captured both depth information and color texture data over 60 days at five different time points. The maize plants, representing 20 different varieties, were grown in pots with four replicates per variety and arranged side by side. Data collection was performed at night with supplemental lighting, and the camera was mounted at different heights depending on the crop growth stage, maintaining a distance of approximately 40 ​cm from the plants. The phenotyping robot moved at a constant speed of 0.1 ​m/s, with the camera capturing RGB and depth images at 15 fps and a resolution of 1920x1080.

After data collection, we used scale-invariant feature transform (SIFT) to detect keypoints in the depth images and compute rough alignment matrices and displacement distances between frames. On the basis of these calculations, we selected frames for point cloud matching and applied the colored iterative closest point (ICP) algorithm to refine the alignment, generating a 3D reconstruction of the maize plants, as shown in
Fig. 7
(C1). We then used the RANSAC method to fit the soil surface plane and clustered the point cloud to segment individual plants, measuring the distance from the highest point of each plant to the soil surface to estimate the plant height. We created a heatmap of the average height of the 20 varieties over five time points (
Fig. 7
(B)), clearly showing the height variation and differences between varieties at different stages. Comparing the plant height measurements of the final time point with manual measurements yielded an R
2
of 0.99 (
Fig. 7
(C2)).

Fig. 7.

Open in a new tab

3D phenotyping collection and analysis: (A) Robotic work scenarios, (B) heatmaps of average heights of 20 material varieties at 5 time points, (C) 3D reconstruction of maize and correlation analysis between manual and machine measurements of plant height, and (D) 3D reconstruction of rape and correlation analysis between manual and machine measurements of plant height.
Additionally, we conducted a similar experiment with 50 varieties of rapeseed at the seedling stage, with three replicates per variety, which were also arranged side by side. Data collection was performed during the day, with the same methods used to capture and process the data, resulting in a 3D reconstruction of the plants and extraction of plant height. The reconstruction is shown in
Fig. 7
(D1), and the correlation analysis yielded an R
2
of 0.97 (
Fig. 7
(D2)). The slightly lower R
2
value compared with the maize experiment may be attributed to the lower height of the rapeseed plants and the variability in growth posture, which introduced greater measurement error during manual height assessment.

For maize phenotypic trait extraction, if each image could simultaneously capture a color calibration chart to standardize the color across multiple images, it would allow for more detailed analysis of the plant traits on the basis of color in the postprocessing stage. Additionally, during maize data preprocessing, instance segmentation models such as Mask R-CNN could be employed to track and extract individual plants, further reducing the need for manual involvement in data processing.
3.4. Hyperspectral-based drought classification in rice

Using the hyperspectral camera of PhenoRob-F, which covers a spectral range of 900–1700 ​nm, we designed an experiment to scan the canopies of potted rice plants under drought stress conditions. The experiment involved 200 accessions from a core collection of 533 rice germplasm resources, with two treatments—drought stress and the normal control—each with three biological replicates. The plants were arranged in rows, with different varieties interspersed between rows. Data collection was conducted at night, with the robot moving autonomously at a speed of 0.1 ​m/s. The visual navigation system adjusted the position of the robot to ensure that the camera was directly above the experimental materials, capturing spectral data at a frame rate of 50 fps and an exposure time of 20 ​ms.

Spectral data were collected on the 3rd, 7th, 9th, and 14th days after drought onset. LabVIEW 2015 software (National Instruments, USA) was used to read and process the binary files of the hyperspectral data. First, the data were corrected using whiteboard and dark current files obtained during the experiment, and the OTSU segmentation algorithm was applied to the spectral images to extract binary masks. Second, these masks were used to calculate the total reflectance (T) and average reflectance (A) of each plant, as well as derivative spectral indices such as first-order derivatives (dA and dT), second-order derivatives (ddA and ddT), and logarithmic transformations (lgA and lgT).

After preliminary outlier removal, noise reduction, and mean aggregation, the high-dimensional spectral indices were subjected to feature reduction using the competitive adaptive repeated sampling (CARS) algorithm combined with manual selection to identify the optimal spectral indices. A random forest classifier was then used to build a drought classification model, which categorized the rice plants into five classes on the basis of the number of drought days [
33
]. The model achieved classification accuracies of 99.34 ​%, 97.71 ​%, 99.55 ​%, 99.10 ​%, and 99.50 ​% for the five drought classes, respectively. The hyperspectral data collection and processing pipeline is shown in
Fig. 8
.

Fig. 8.

Open in a new tab

Hyperspectral data collection and processing pipeline.
4. Discussion

4.1. System optimization and scalability

Currently, most of our experiments are conducted at night due to the absence of natural light, which eliminates complications caused by changing sunlight. However, to extend the operational hours of the robot and account for certain phenotyping studies that require observations of diurnal rhythms in crops, daytime operation should also be considered. During the day, the light intensity and angle gradually change, and shadows exist. One solution for daytime phenotyping is to equip the robot with a darkroom system, which would provide a controlled lighting environment within the enclosed space. However, this approach would increase the complexity of the system, as well as the size and weight of the robot, thereby reducing its agility. Designing an efficient and feasible structure for this system is challenging. Another solution is to leverage natural sunlight while using a proper whiteboard calibration system to mitigate the effects of changing light intensity, angle, and shadows. We are currently exploring solutions from both structural and algorithmic perspectives. If sunlight can be fully utilized, eliminating the need for artificial lighting would significantly increase the endurance time of the robots.

In addition, the sensors in our current experiments are predominantly mounted above the crops, capturing canopy images. While canopy images provide valuable phenotypic information, studies have shown that multiangle imaging, such as side views, can reveal additional phenotypic traits. In the future, we plan to improve multiangle data collection, including deploying robotic arms to access areas below the canopy to gather internal information or placing sensors in direct contact with crops to acquire physiological and biochemical data.

Planting width, crop height, and soil conditions vary across different crop breeding experiments. Variability is especially pronounced in paddy field environments. The system described in this article is designed with scalability in mind. To ensure that the phenotyping robot can adapt to these conditions, we transferred this technology to robots with different structures.
Fig. 9
(A) shows a field phenotyping robot working in a wheat field, and
Fig. 9
(B) shows a phenotyping robot suitable for paddy fields, which is conducting phenotyping work in a rice field.
Fig. 9
(C) shows a phenotyping platform capable of being adjusted vertically between 1.6 ​m and 2.8 ​m, which allows for phenotyping throughout the entire growth cycle of maize. Acquiring phenotyping traits from the side view in facility environments can be realized by the robot shown in
Fig. 9
(D). And for random angle-of-view detection, we have the robot shown in
Fig. 9
(E) equipped with a six-degree-of-freedom robotic arm and a depth camera. Additionally, the flexible imaging phenotyping robot shown in
Fig. 9
(F) can freely adjust both height and angle-of-view.

Fig. 9.

Open in a new tab

Phenotyping robots for different scenarios.
4.2. Phenotyping robot operational efficiency

The efficiency of phenotyping robots is determined largely by their speed and the parameters of the sensors used for data acquisition. In our system, the mobile platform can achieve speeds up to 1 ​m/s, but higher speeds cause significant vibration, resulting in image blurring. For phenotypic data sensors, SPECIM FX17 can theoretically capture up to 670 frames per second (FPS), but at frame rates above 50 FPS, the maximum allowable exposure time drops below 20 ​ms due to hardware limitations. Our experiments have shown that an optimal imaging outcome is achieved when the frame rates is set at 50FPS and robot moves at a speed of 0.094 ​m/s. As the frame rate increases, the robot must move faster to maintain image without distortion, but shorter exposure times and higher vibration will lead to blurring. The Azure Kinect DK's depth camera has a maximum exposure time of 12.8 ​ms, and its RGB camera can achieve up to 133.33 ​ms under a 60 ​Hz light source. In theory, increasing the exposure time to its maximum value and adjusting the robot's speed can enhance data collection efficiency. However, our tests revealed that when the robot moves at 0.2 ​m/s, larger vibration will significantly affect image clarity. To further improve data collection and reduce the effect of vibration on image quality, future efforts will focus on developing a gimbal-based stabilization system for sensors to counteract movement in all degrees of freedom. In addition, the chassis design of the robot can be improved to better handle uneven terrain by incorporating advanced shock absorption mechanisms.

Currently, the robot operates on a 100 AH lithium battery, allowing for 3 ​h of continuous operation when both RGB-D and hyperspectral data are collected. The primary limitation arises from the halogen lamps used to illuminate the hyperspectral camera, which consume more than 1200 ​W and account for more than 80 ​% of the total power usage of the system. However, facing large operational areas and substantial amounts of plant material, the robot is required to work for as long as possible. This requirement creates an urgent need for low-power light sources that can provide a full-spectrum range with sufficient intensity. Thus, selecting appropriate light-emitting beads and designing suitable reflectors to focus the light more efficiently, ensuring optimal energy utilization, are important.
4.3. Data processing platform and advanced applications

The data processing platform is a critical component that supports the entire robotic system. Currently, raw data collected by the multiple sensors of a robot—such as hyperspectral and RGB-D cameras—are processed through separate workflows, each running on different system architectures. In the future, it will be necessary to integrate common phenotypic trait extraction methods for maize, rice, and wheat canopy data, including hyperspectral and RGB-D data, into unified phenotyping analysis software. This software allows users to import raw data collected by the robot and, through semiautomated or fully automated processes, obtain phenotypic trait data directly.

In the three experiments presented in this study, although the data collection of the robot focused on extracting traits such as ear number, plant height, and drought classification in rice, the system enables researchers to capture more detailed and diverse phenotypic data, gaining deeper insights. For example, RGB data could be used to count ear numbers more accurately through multiangle imaging, improving yield predictions. Similarly, three-dimensional data can be improved by employing additional cameras to create more comprehensive point clouds, allowing for a more precise reconstruction of the 3D morphology of the crop, which would enable the extraction of additional morphological traits. In the case of spectral data, vast amounts of spectral traits can be analyzed in conjunction with crop genotypic data to identify genomic loci associated with traits such as stress tolerance, aiding in the selection of superior crop varieties.
5. Conclusion

In this paper, we introduced PhenoRob-F, a high-throughput, fully autonomous crop phenotyping robot equipped with a flexible mobile platform and a navigation system capable of autonomous operation in outdoor environments. Through experiments on various crops, including maize, rice, wheat, and rapeseed, the robot successfully collected hyperspectral and RGB-D data. Using a series of data processing methods, we extracted the number of wheat and rice panicles, measured the plant heights of maize and rapeseed, and built a rice drought classification model that allows for rapid and accurate drought monitoring. These experiments validate the practicality of the PhenoRob-F system, revealing its potential to provide efficient, low-cost, and autonomous solutions for field crop phenotyping.
Author contributions

M.Y. and Z.L. designed the robot, analyzed the data, and wrote the manuscript. J.C., Y.S. and W.Q. performed the field experiment. R. Z. also performed the experiments and analyzed the data. W.Y. and P.S. supervised the project.
Data availability

Part of the data and code supporting this study are openly available with the following link:
https://github.com/balloonhaha/PhenoRob-F
. All other reasonable requests for data and research materials will be fulfilled upon contacting the corresponding authors.
Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments

This work was supported by the National Key Research and Development Program of China (2021YFD1200504, 2022YFD2002304), the National Natural Science Foundation of China (32471992), the Key Core Technology Project in Agriculture of Hubei Province (HBNYHXGG2023-9), and the Supporting Project for High-Quality Development of the Seed Industry of Hubei Province (HBZY2023B001-06).
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Part of the data and code supporting this study are openly available with the following link:
https://github.com/balloonhaha/PhenoRob-F
. All other reasonable requests for data and research materials will be fulfilled upon contacting the corresponding authors.

Articles from Plant Phenomics are provided here courtesy of
Nanjing Agricultural University

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</reference>

<statements>
1. The published grain, field and reconstruction pipelines justify treating the scanner, motion stage or robot, illumination, focus, reconstruction software and trait-analysis layer as one controllable dynamic system.
2. The design contribution is therefore not to invent a new sensor, but to couple these methods into a supervisory digital twin that optimizes scan speed, viewpoint, focus, illumination and scheduling against a trait-uncertainty cost, while explicitly budgeting registration, mixed-pixel, laser-penetration, occlusion and benchmark-transfer errors that the literature quantifies only separately
3. The measured dependencies in the cited pipelines—stage speed, acquisition geometry, focus, illumination and camera height, and plant motion—support abstracting the controllable system as
4. where \(x\) is the platform and object state
5. illumination and camera height are constrained by robot power and crop proximity
6. PhenoRob-F reaches a maximum speed of 1 m/s and mounts the camera at different heights with approximately 40 cm working distance
7. halogen lamps consume more than 1200 W and over 80% of total system power
8. PhenoRob-F uses RTK positioning with centimetre-level accuracy when stationary and an improved deviation-coupling algorithm for wheel-speed compensation
9. PhenoRob-F achieved wheat ear precision 0.783, recall 0.822 and mAP 0.853 with YOLOv8m on local field images, lower than its GWHD validation performance of precision 0.924, recall 0.859 and mAP 0.930
10. Rice panicle segmentation reached mIoU 0.949 and accuracy 0.987 with SegFormer_B0, maize plant height reached R² = 0.99 against manual measurement, and rapeseed height reached R² = 0.97
11. Near-infrared spectral data classified five drought-severity categories with accuracies from 0.977 to 0.996
12. A suitable architecture is hierarchical because the cited systems separate platform control, acquisition control and supervisory scheduling.
13. The lowest layer controls physical motion and optical focus, as evidenced by robot speed and camera-height control
14. PhenoRob-F provides a complementary field-platform reference, with cross-row wheeled mobility, RTK positioning, visual navigation and a maximum speed of 1 m/s.
15. The acquisition layer should use model predictive control over illumination and camera height
16. The evidence supplies separate constraints but not a single published joint controller; the separate constraints are power
17. PhenoRob-F quantifies power: halogen illumination consumes more than 1200 W and over 80% of system power, limiting continuous operation on a 100 AH battery to 3–6 h.
18. The separate cost terms can be drawn from the cited measurements: energy from illumination power and battery duration
19. \(E_{\text{energy}}\) is power consumption
20. \(E_{\text{energy}}\) is constrained by illumination power and battery duration
21. Validation should be multi-level: metrological, computational, biological and operational, because the cited studies evaluate accuracy, convergence, biological discrimination and field operation
22. Are organs detected and segmented? precision, recall, mAP, IoU, F1
23. PhenoRob-F reported wheat ear precision 0.783, recall 0.822 and mAP 0.853, and rice panicle mIoU 0.949
24. The case studies show how the architecture should specialize across benchtop, consumer organ-level and field scales
25. Platform control and biological analysis are coupled through resolution, lighting, power and occlusion
26. Wheat ear detection, rice panicle segmentation, maize height R² = 0.99 and drought classification were reported, but overlapping ears, nighttime operation and >1200 W lighting were limitations
27. PhenoRob-F reports that wheat ear detection struggles with overlapping ears at image edges, that RGB-based yield prediction still needs improvement, that many experiments are conducted at night to avoid changing sunlight, and that halogen illumination dominates power use [4]
28. The evidence supports each component separately: scan speed [2], acquisition geometry [6], focus [5], illumination power [4]
29. 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]
</statements>

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