Improvement of Pest Detection Using Histogram Adjustment Method and Gabor Wavelet

Plant diseases reduce production and cause huge economic losses in the agricultural section. At present, plant diseases are diagnosed by specialists and with the naked eyes. Presenting a simple, fast, cheap and accurate way for the diagnosis of plant diseases is necessary. In this paper, by using image processing and machine vision techniques, four methods for the diagnosis and classification of the diseases of corn leaf are presented. In the first method, the affected parts were separated from the healthy parts using the histogram adjustment; subsequently, a two-layer Perceptron Neural Network was used to categorize the final results and diagnose the disease type. The results indicate that Neural Network with an average of 65.15% is able to correctly diagnose the disease of the corn leaf. In the second method, different types of Laplacian filters, Canny and Sobel were applied on the leaves; after the separation of the affected parts, the classification and diagnosis phase were implemented. The results revealed that the algorithm with the accuracy of 67.94%, can correctly diagnose the disease. In the third method, using the analysis method of principal components, data dimension was reduced, and then was sent to the Support Vector Machine classifier for the diagnosis of the disease. This algorithm is able to correctly diagnose the disease with an accuracy of 75.28%. Furthermore, the algorithm is able to diagnose Class 5 and 4 diseases in a more accurate way. Finally, in the fourth method, a combination of Gabor filter and visual features was used in order to diagnose the type of disease. In this method, the proposed algorithm can correctly diagnose the disease with the accuracy of 90.04%.