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.