Development and Evaluation of a Multiaxial Modular Ground Robot for Estimating Soybean Phenotypic Traits Using an RGB-Depth Sensor

Abstract

Achieving global sustainable agriculture requires farmers worldwide to adopt smart agricultural technologies, such as autonomous ground robots. However, most ground robots are either task- or crop-specific and expensive for small-scale farmers and smallholders. Therefore, there is a need for cost-effective robotic platforms that are modular by design and can be easily adapted to varying tasks and crops. This paper describes the hardware design of a unique, low-cost multiaxial modular agricultural robot (ModagRobot), and its field evaluation for soybean phenotyping. The ModagRobot’s chassis was designed without any welded components, making it easy to adjust trackwidth, height, ground clearance, and length. For this experiment, the ModagRobot was equipped with an RGB-Depth (RGB-D) sensor and adapted to safely navigate over soybean rows to collect RGB-D images for estimating soybean phenotypic traits. RGB images were processed using the Excess Green Index to estimate the percent canopy ground coverage area. 3D point clouds generated from RGB-D images were used to estimate canopy height (CH) and the 3D Profile Index of sample plots using linear regression. Aboveground biomass (AGB) was estimated using extracted phenotypic traits. Results showed an R2, RMSE, and RRMSE of 0.786, 0.0181 m, and 2.47%, respectively, between estimated CH and measured CH. AGB estimated using all extracted traits showed an R2, RMSE, and RRMSE of 0.59, 0.0742 kg/m2, and 8.05%, respectively, compared to the measured AGB. The results demonstrate the effectiveness of the ModagRobot for in-row crop phenotyping.

Plant phenotyping relevance

低コスト移動ロボット、RGB-D撮像、画像・点群解析によるダイズ形質推定を開発・評価しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's soybean phenotyping data (RGB-D images, ground-truth canopy height and AGB measurements) are not publicly deposited; the Data Availability Statement says they are available only on request from the corresponding author. No author analysis code, scripts, or trained models are stated to be publicly available,

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