Abstract
According to the characteristics of maize disease spot performance in the image, this paper designs two-histogram segmentation method based on evolutionary algorithm, which combined with the analysis of image of maize diseases and insect pests, with full consideration of color and texture characteristic of the lesion of pests and diseases, the chroma and gray image, composed of two tuples to build a two-dimensional histogram, solves the problem of one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution and improved the traditional two-dimensional histogram application in pest damage lesion extraction. The chromosome coding suitable for the characteristics of lesion image is designed based on second segmentation of the genetic algorithm Otsu. Determining initial population with analysis results of lesion image, parallel selection, optimal preservation strategy, and adaptive mutation operator are used to improve the search efficiency. Finally, by setting the fluctuation threshold, we continue to search for the best threshold in the range of fluctuations for implementation of global search and local search.
Highlights
In recent years, as the country pays more attention to the problem of “agriculture, countryside, and farmers,” “precision agriculture” concept is gradually on the rise
The image test is focused on the image of corn diseases at 200 × 200 pixels, which includes the common leaf blight, Cochliobolus heterostrophus, and gray spot disease
According to the analysis of plant diseases and insect pests image, gray image and the HSI color space of color image are composed of two tuples to build a two-dimensional histogram, to better describe the distribution of pixel, to solve the one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution situation, and to improve the use of traditional two-dimensional histogram in pest damage lesion extraction
Summary
As the country pays more attention to the problem of “agriculture, countryside, and farmers,” “precision agriculture” concept is gradually on the rise. Gassoumi studied the identification of insects in cotton field and applied the computer image processing technology based on feature extraction to identify the pests. The image of rice pests and diseases was collected by Wang and Zhou and others [9], who take borer as object based on neural network to complete the identification of the experimental results, with accuracy of up to 90%. Experimental results show that the proposed algorithm has better recognition accuracy than other support vector machines in the field of cucumber and show that the algorithm has better classification accuracy for small sample data sets. In addition to the study of the classification of pests, the image segmentation technology is applied to the study They take the aphids as the target; the experimental results show that the algorithm is effective, and the recognition accuracy is up to 90.7%
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