Phenotypic Trait Acquisition Method for Tomato Plants Based on RGB-D SLAM

Penggang Wang, Yuejun He (), Jiguang Zhang (), Jiandong Liu, Ran Chen and Xiang Zhuang
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Penggang Wang: North China Institute of Aerospace Engineering, Langfang 065000, China
Yuejun He: North China Institute of Aerospace Engineering, Langfang 065000, China
Jiguang Zhang: Institute of Automation of Chinese Academy of Sciences, Beijing 100090, China
Jiandong Liu: North China Institute of Aerospace Engineering, Langfang 065000, China
Ran Chen: North China Institute of Aerospace Engineering, Langfang 065000, China
Xiang Zhuang: North China Institute of Aerospace Engineering, Langfang 065000, China

Agriculture, 2025, vol. 15, issue 15, 1-25

Abstract: The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union ( mIoU ) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error ( RMSE ) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making.

Keywords: ORB-SLAM3; plant phenotypic traits; semantic segmentation; tomato plant; unmanned vehicle (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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