Robust node detection and tracking in fruit-vegetable crops using deep learning and multi-view imaging

Abstract

Obtaining high-quality phenotypic data that can be used to study the relationship between genotype, phenotype and environment is still labour-intensive. Digital plant phenotyping can assist in collecting these data by replacing human vision by computer vision. However, for complex traits, such as plant architecture, robust and generic digital phenotyping methods have not yet been developed. This study focusses on internode length in cucumber plants. A method for estimating internode length and internode development over time is proposed. The proposed method firstly applies a robust node-detection algorithm based on a deep convolutional neural network. In tests, the algorithm had a precision of 0.95 and a recall of 0.92. The nodes are detected in images from multiple viewpoints around the plant in order to deal with the complex and cluttered plant environment and to solve the occlusion of nodes by other plant parts. The nodes detected in the multiple viewpoint images are then clustered using affinity propagation. The predicted clusters had a homogeneity of 0.98 and a completeness of 0.99. Finally, a linear function is fitted, which allows to study internode development over time. The presented method was able to measure internode length in cucumber plants with a higher accuracy and a larger temporal resolution than other methods proposed in literature and without the time investment needed to obtain the measurements manually. The relative error of our complete method was 5.8%. The proposed method provides many opportunities for robust phenotyping of fruit-vegetable crops grown under greenhouse conditions.

Plant phenotyping relevance

深層学習とマルチビュー画像を用いてキュウリの節検出・追跡および節間長推定法を開発し、精度検証まで行っており、植物表現型取得が研究の中心である。

Code and data availability

The supplied blocks describe the authors' node-detection/training pipeline (annotated cucumber images, YOLO v3/Darknet, affinity propagation) but contain no public deposit of the paper's own dataset, images, annotations, code, or trained model. All URLs present are third-party tools, frameworks, or cited references (e.

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