Use of Computer Vision Techniques for Recognition of Diseases and Pests in Tomato Plants

Abstract

Computer vision, for decades, has been involved in solving problems in everyday life, under the implementation of different computational methods, that have evolved over time. Feature extraction, along with other computer techniques, is considered a way to develop computer vision systems; currently, plays an important role, considered a complex task, allowing to obtain essential descriptors of the segmented images, differentiating particular characteristics between different classes, even when they share similarity with each other, guaranteeing the delivery of information not redundant to classification algorithms. Likewise, in this work, a computer vision system has been developed for the recognition of foliar damage caused by diseases and pests in tomato plants. The methodology implemented is based on four modules: preprocessing, segmentation, feature extraction, and classification; in the first module, the image is preprocessed of a color space RGB to L*a*b*; in the second module, the area interest was segmented, under the implementation of the algorithm principal component analysis PCA; in the third module, features are extracted from the area of interest, obtaining texture descriptors with the Haralick algorithm, and chromatic features through Contrast descriptors, Hu moments, Gabor characteristics, Fourier descriptors, and discrete cosine transform DCT; in the fourth module, the performance of the classification algorithms were tested, with the characteristics obtained from the previous stage, considering: SVM, Backpropagation, Logistic Regression, KNN, and Random Forests.