Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu - Chen, Chengcheng; Wang, Xianchang; Heidari, Ali Asghar; Yu, Helong; Chen, Huiling (Frontiers in Plant Science, 2021)
Maize is a major global food crop, and as one of the most productive grain crops, it can be eaten, it is also a good feed for the development of animal husbandry and an important raw material for light industry, chemical industry, medicine and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and effective identification is an important link to reduce the damage of diseases, and accurate segmentation of disease spots is one of the key techniques for disease identification. However, one single method cannot achieve good segmentation effect to meet the diversity and complexity of disease spots. In order to solving the shortcomings of noise interference and over-segmentation in Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images, and a new elite strategy improved comprehensive particle swarm algorithm optimization method was used to find the optimal segmentation threshold of objective function in this paper. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms, and it has better convergence and stability.