Disease Feature Recognition of Hydroponic Lettuce Images Based on Support Vector Machine

Abstract

To achieve early recognition of lettuce diseases, this paper combines the technology of image processing and the classifier of support vector machine (SVM) to identify and classify two common diseases of hydroponic lettuce: leafroll and brown blotch disease (BBD). Specifically, the authors designed programs for the acquisition and preprocessing, segmentation, and feature extraction of hydroponic lettuce images, and developed an identification program for hydroponic lettuce diseases based on the SVM. On this basis, the color, shape, and texture features were extracted from these images, and adopted as the training set of the SVM. Then, the identification model for hydroponic lettuce diseases was trained with the radial kernel function as the core, and applied to identify the different types of diseases. In total, 1,800 images were selected as samples, and subjected to denoising, enhancement, segmentation, and feature extraction. The leaf features of hydroponic lettuce were extracted, and used to establish the SVM-based disease identification model. The experimental results on the test set show that the identification model could recognize 93% of hydroponic lettuce diseases, achieving an excellent identification effect.