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

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.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 1670-1674
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.1670
• Online since: January 2015
• Authors: Ming Gang Du*, Shan Wen Zhang
• Keywords: Disease Leaf Spots, Genetic Algorithm (GA), Image Segmentation, Maximum Entropy

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