Research Article | Open Access

Volume 2025 |Article ID 100085 | https://doi.org/10.1016/j.plaphe.2025.100085

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

Meng Yang,^1,2,5 Zhengda Li,^1,2,3,5 Jiale Cui,^1,2 Yang Shao,^1,2 Ruifang Zhai,⁴ Wen Qiao,³ Wanneng Yang,^1,2 Peng Song ^1,2

¹College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, PR China
²National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, PR China
³Wuhan X-Agriculture Intelligent Technology Co., Ltd, Wuhan, 430070, PR China
⁴College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China
⁵These authors contributed equally to this work.

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 R2 of 0.99 compared with manual measurements. Similar experiments with rapeseed yielded an R2 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.