Smart phenotyping robot transforms crop monitoring for food security
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Smart phenotyping robot transforms crop monitoring for food security

01/09/2025 TranSpread

Featuring an adjustable wheel track, a precision-controlled gimbal for sensors, and advanced multisensor fusion algorithms, the system enables more efficient and accurate plant phenotyping, laying the groundwork for breakthroughs in crop improvement and sustainable agriculture.

Crop genetic improvement is central to addressing global food challenges, yet success depends on bridging the gap between genomics and observable traits. Plant phenomics—the large-scale study of plant characteristics—provides the link, but traditional methods for measuring crop structure, physiology, and development are labor-intensive and limited in scale. High-throughput phenotyping (HTP) platforms integrate sensors with mobile systems to automate data collection, enabling researchers to study crops at scale. While aerial systems offer broad coverage, they face payload and endurance constraints. Ground-based robots offer accuracy and versatility but are often restricted by fixed chassis designs and limited sensor adaptability. Developing a robust phenotyping robot that can adapt to variable agricultural conditions and integrate multi-source data remains a critical challenge.

A study (DOI: 10.1016/j.plaphe.2025.100014) published in Plant Phenomics on 20 March 2025 by Yan Zhu & Weixing Cao’s team, Nanjing Agricultural University, represents a significant step toward scalable, precise monitoring of crop traits in real-world agricultural settings.

In this study, researchers conducted field experiments at the National Engineering and Technology Center for Information Agriculture in Rugao, Jiangsu Province to evaluate the performance of a newly developed phenotyping robot. The first stage involved performance tests on the robot’s chassis and gimbal using a GNSS-RTK navigation system, which monitored speed, trajectory, and chassis posture. Simulation with Adams software predicted critical metrics such as maximum climbing angle, tipping limits, and obstacle traversal height, and subsequent field tests in both dryland and paddy environments confirmed these results, demonstrating the chassis’ reliability and adaptability. The adjustable wheel track mechanism was repeatedly tested 50 times, showing accurate closed-loop feedback and an adjustment speed of 19.8 mm/s, proving effective in coping with different row spacings. The gimbal, equipped with three servo motors and a PID control algorithm, achieved precise angle adjustments across pitch, roll, and yaw, with response times under one second, confirming fast and stable orientation control for mounted sensors. The second stage assessed multisensor registration and fusion by deploying multispectral, thermal infrared, and depth cameras on the robot and comparing outputs with handheld instruments across wheat plots of varying varieties, planting densities, and nitrogen levels. Calibration methods were applied for each sensor type to ensure accuracy. Data were collected seven times during key wheat growth stages, and pixel-level fusion using Zhang’s calibration and BRISK algorithms achieved image registration errors under three pixels. Correlation analysis revealed strong agreement between robot-mounted and handheld data, with R² values above 0.98 for spectral reflectance, 0.90 for canopy distance, and 0.99 for temperature measurements. Bland-Altman plots confirmed consistency across all parameters. Together, these results validate the robot’s ability to accurately, reliably, and efficiently collect high-throughput phenotypic data in diverse agricultural field conditions.

By enabling flexible adaptation to different crops and environments, the system provides researchers and breeders with powerful tools to accelerate the discovery of genes linked to yield, stress resistance, and quality traits. Beyond breeding programs, the robot could be adapted for other field operations such as fertilization, spraying, and weeding, further extending its value in sustainable agriculture. The integration of pixel-level data fusion also creates opportunities for more accurate predictive models in yield estimation and crop stress detection, helping to close the gap between laboratory research and field application.

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References

DOI

10.1016/j.plaphe.2025.100014

Original URL

https://doi.org/10.1016/j.plaphe.2025.100014

Funding information

The work was supported by the National Key Research and Development Program of China (Grant No. 2021YFD2000101).

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.

Title of original paper: Design and implementation of a high-throughput field phenotyping robot for acquiring multisensor data in wheat
Authors: Miao Su, Dong Zhou, Yaze Yun, Bing Ding, Peng Xia, Xia Yao, Jun Ni, Yan Zhu, Weixing Cao
Journal: Plant Phenomics
Original Source URL: https://doi.org/10.1016/j.plaphe.2025.100014
DOI: 10.1016/j.plaphe.2025.100014
Latest article publication date: 20 March 2025
Subject of research: Not applicable
COI statement: The authors declare that they have no competing interests.
Attached files
  • Figure 2. Three-dimensional diagram illustrating the structure of the phenotyping robot. (a)Three-dimensional rendering depicting the phenotyping robot. (b). Exploded view showcasing the chassis of the phenotyping robot. (c). Three-dimensional rendering portraying the chassis structure of the phenotyping robot. (d). Overhead view of a rendered image illustrating the wheelbase adjustment mechanism within the chassis of the phenotyping robot.
01/09/2025 TranSpread
Regions: North America, United States, Asia, China
Keywords: Applied science, Engineering

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