Extraction of Corn Plant Phenotypic Parameters with Keypoint Detection and Stereo Images

Abstract

Corn is a global crop that requires the breeding of superior varieties. A crucial aspect of the breeding process is the accurate extraction of phenotypic parameters from corn plants. The existing challenges in phenotypic parameter extraction include low precision, excessive manual involvement, prolonged processing time, and equipment complexity. This study addresses these challenges by opting for binocular cameras as the data acquisition equipment. The proposed stereo corn phenotype extraction algorithm (SCPE) leverages binocular images for phenotypic parameter extraction. The SCPE consists of two modules: the YOLOv7-SlimPose model and the phenotypic parameter extraction module. The YOLOv7-SlimPose model was developed by optimizing the neck component, refining the loss function, and pruning the model based on YOLOv7-Pose. This model can better detect bounding boxes and keypoints with fewer parameters. The phenotypic parameter extraction module can construct the skeleton of the corn plant and extract phenotypic parameters based on the coordinates of the keypoints detected. The results showed the effectiveness of the approach, with the YOLOv7-SlimPose model achieving a keypoint mean average precision (mAP) of 96.8% with 65.1 million parameters and a speed of 0.09 s/item. The phenotypic parameter extraction module processed one corn plant in approximately 0.2 s, resulting in a total time cost of 0.38 s for the entire SCPE algorithm to construct the skeleton and extract the phenotypic parameters. The SCPE algorithm is economical and effective for extracting phenotypic parameters from corn plants, and the skeleton of corn plants can be constructed to evaluate the growth of corn as a reference. This proposal can also serve as a valuable reference for similar functions in other crops such as sorghum, rice, and wheat.