3D pose estimation of tomato peduncle nodes using deep keypoint detection and point cloud

Abstract

Greenhouse production of fruits and vegetables in developed countries is challenged by labour scarcity and high labour costs. Robots offer a good solution for sustainable and cost-effective production. Acquiring accurate spatial information about relevant plant parts is vital for successful robot operation. Robot perception in greenhouses is challenging due to variations in plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which provides essential information to harvest the tomato bunches. Specifically, this paper proposes a method that detects four anatomical landmarks in the colour image and then integrates 3D point-cloud information to determine the 3D pose. A comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the performance of different parts of the method. The results showed: (1) high accuracy in object detection, achieving an Average Precision (AP) of [email protected]=0.96; (2) an average Percentage of Detected Joints (PDJ) of the keypoints of [email protected] = 94.31%; and (3) 3D pose estimation accuracy with mean absolute errors (MAE) of 11o and 10o for the relative upper and lower angles between the peduncle and main stem, respectively. Furthermore, the capability to handle variations in viewpoint was investigated, demonstrating the method was robust to view changes. However, canonical and higher views resulted in slightly higher performance compared to other views. Although tomato was selected as a use case, the proposed method has the potential to be applied to other greenhouse crops, such as pepper, after fine-tuning.

Plant phenotyping relevance

トマトの器官ランドマークを画像・点群から抽出し、ペドンクル節の3D姿勢という植物形態形質を推定する手法を開発・評価しており、収穫ロボットへの応用を超えて表現型取得が中心である。

Code and data availability

The supplied blocks describe the paper's tomato peduncle node dataset (503 training / 145 test RGB images with keypoint annotations and aligned point clouds) and its Keypoint R-CNN pipeline, but contain no authors' public deposit of the dataset, images, code, or trained models. The only URLs present are generic tooling

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