PhenoBee: Drone-based robot for advanced field in vivo contact-based phenotyping in agriculture

Abstract

Spectral imaging has been widely applied for soybean phenotyping to find and maintain favorable traits. Specifically, in soybean phenotyping, hyperspectral imaging through contact-based proximal sensing demonstrates better signal-to-noise ratio and resolution compared to remote sensing. However, it has not been adapted for large-scale field applications due to its low throughput and high labor costs. Additionally, no automation solution has been developed to collect in vivo contact-based hyperspectral images of soybean plants. In this study, a novel drone-based robotic system was developed to automate the collection of in vivo contact-based hyperspectral images in the field. The system consists of a machine vision system to detect and estimate the pose of soybean leaflets, an articulated robotic arm with specialized control and path planning algorithms to operate contact-based sensors to grasp and image the leaf, and a customized high-payload drone to provide mobility for sampling at different locations across a field. The average accuracy of the optimized machine vision algorithm is 95.88% for leaf detection and 97.54% for leaf pose estimation, and the average success rate of leaf grasping is 90.55%. This study presents an innovative method for expanding the applicability in vivo contact-based hyperspectral imaging for extensive agricultural applications.

Plant phenotyping relevance

植物葉の接触型ハイパースペクトル画像を自動取得するロボットシステムと、葉検出・姿勢推定・把持の技術を開発しており、植物フェノタイピング手法が研究の中心である。

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