Crop Disease Leaf Image Segmentation Based on Genetic Algorithm and Maximum Entropy

Author:

Du Ming Gang1,Zhang Shan Wen2

Affiliation:

1. Shanxi Normal University

2. XiJing University

Abstract

Crop disease leaf image segmentation is a key step in crop disease recognition. In the paper, a segmentation method of crop disease leaf image is proposed to segment leaf image with non-uniform illumination based on maximum entropy and genetic algorithm (GA). The information entropy is regarded as the fitness function of GA, the maximum entropy as convergence criterion of GA. After genetic operation, the optimal threshold is obtained to segment the image of disease leaf. The experimental results of the maize disease leaf image show that the proposed method can select the threshold automatically and efficiently, and has an advantage over the other three algorithms, and also can reserve the main spot features of the original disease leaf image.

Publisher

Trans Tech Publications, Ltd.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prototype of chili pathogen early detection system by using multispectral NIR/NUV;IOP Conference Series: Earth and Environmental Science;2024-08-01

2. Enhancing Plant Health: Deep Learning Approaches for Precise Disease Detection;2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC);2023-12-07

3. A Comprehensive Review on Crop Disease Prediction Based on Machine Learning and Deep Learning Techniques;Third Congress on Intelligent Systems;2023

4. A Crop Leaf Disease Image Recognition Method Based on Bilinear Residual Networks;Mathematical Problems in Engineering;2022-04-25

5. Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning;Sustainable Computing: Informatics and Systems;2021-06

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