Nondestructive detection of key phenotypes for the canopy of the watermelon plug seedlings based on deep learning

Abstract

Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.

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