关键词: 图像分割, 深度学习, 全卷积神经网络, 形态参数解析

Abstract: Efficient and accurate extraction of plant phenotypic parameters is an important guide for the management of plants during all growth stages. To improve the efficiency and accuracy of morphological parameter extraction in plant leaf images, a model framework and key functions were optimized based on a fullconvolutional neural network. Additionally, the plant leaf image segmentation effect was achieved through supervised learning methods. The mean recall of the model on the test set was 0.95 for r and 0.94 for MIoU. The morphological parameters of extracted plant leaves in the segmentation results were highly correlated with the manual extraction results whereby r2>0.96. In this study, high throughput segmentation of plant leaf images was achieved, and the plant leaf morphological parameters extracted from the segmentation results can be used in related studies such as crop growth monitoring.

Key words: image segmentation, deep learning, fully neural network, morphological parameter analysis