A Quantitative Analysis Method of Soybean Leaf Based on Key Point Detection

Fast and accurate measuring and assessing phenotypic traits of soybean leaf play a key role in the soybean breeding. The manual analysis of soybean leaf is time-consuming and prone to human errors. In this paper, an automatic quantitative approach is proposed which can obtain the vital geometric parameters (e.g., major axis, minor axis, and tip angle) of soybean leaf based on key point detection model. Specifically, the proposed method utilizes the Stacked Hourglass Network to find and accurate localize the four key points of each soybean leaf. Once those interest points are identified, then the distances between them and the tip angle are computed automatically. By performing such strategy, the quantitative analysis of soybean leaf can be handled effectively. The extensive experimental results obtained on the collected soybean leaf dataset verify that the proposed method has better convergence accuracy and robustness than other classical methods.