Deep learning model enhances maize phenotype detection and crop management

A research team has developed the Point-Line Net, a deep learning method based on the Mask R-CNN framework, to automatically recognize maize field images and determine the number and growth trajectory of leaves and stalks. The model achieved an object detection accuracy (mAP50) of 81.5% and introduced a new lightweight keypoint detection branch. This innovative method promises to enhance the efficiency of plant breeding and phenotype detection in complex field environments, paving the way for more accurate crop management and yield prediction.