Design and implementation of a high-throughput field phenotyping robot for acquiring multisensor data in wheat

作者：Miao Su, Dong Zhou, Yaze Yun, Bin Ding, Peng Xia, Xia Yao, Jun Ni, Yan Zhu, Weixing Cao · 发表于：Plant Phenomics · 年份：2025 · DOI：10.1016/j.plaphe.2025.100014 · 被引用次数：12 · 研究领域：Wheat and Barley Genetics and Pathology、Smart Agriculture and AI、Genetic Mapping and Diversity in Plants and Animals

Ensuring food security has become a global challenge owing to climate change and population growth. High-throughput phenotyping can effectively drive crop genetic enhancement, which can potentially solve food crisis. Phenotyping robot is an essential part of crop ground phenotyping information monitoring, although there are challenges such as the inability to adjust the fixed track width, poor load capacity of the detection robotic arm, and inability to fuse information in real-time. This study reports a phenotyping robot with a gantry-style chassis featuring an adjustable wheeltrack (1400–1600 mm) to adapt to different row spacing arrangements and reduced damage, and function effectively in both dry field and paddy field environments. A six-degree-of-freedom sensor gimbal with high payload capacity is also developed to enable precise height (1016–2096 mm) and angle adjustments. Additionally, this study introduces an enhanced method for data acquisition from multiple imaging sensors through registration and fusion using Zhang's calibration and feature point extraction algorithm, calculating a homography matrix for high-throughput data collection at fixed positions and heights. The experimental validation results demonstrate that the RMSE of the registration algorithm does not exceed 3 pixels. The gimbal data strongly correlated with that of a handheld instrument data (r 2 > 0.90). The robot is practical, reliable, and fully functional, offering a solid theoretical foundation ...