ArticleFrontiers in plant science2021
Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches.Journal of imaging · 2025Review
- A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning.Scientific reports · 2024Article
- Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques.PloS one · 2024Article
- Emergy-based evaluation of production efficiency and sustainability of diversified multi-cropping systems in the Yangtze River Basin.Frontiers in plant science · 2024Article
- Dual-branch collaborative learning network for crop disease identification.Frontiers in plant science · 2023Article
- Hybrid improved capuchin search algorithm for plant image thresholding.Frontiers in plant science · 2023Article
- Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images.Computers in biology and medicine · 2022Article
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Abstract
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 essential 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 practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the 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 optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. 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.
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