ESMO-based Plant Leaf Disease Identification: A Machine Learning Approach

Abstract

Plant leaf disease detection and identification is a tedious and time-consuming task. Moreover, plant disease detection can be performed early to prevent it from spreading and limiting plant growth. The current study of machine learning and artificial intelligence technology on plant image data has been used to identify the plant’s diseases and prevent them from spreading. Analysis of these plant image datasets enables farmers and companies to improve crop quality and productivity. This chapter proposes a methodology for disease detection on the rice and cotton plant leaf image dataset. To formulate the proposed methodology, a subtractive pixel adjacency matrix (SPAM) method is used for feature extraction. On the other hand, the exponential spider monkey optimization technique (ESMO) has been constructed to select optimum features from extracted features. The proposed system effectively detects and classifies input plant leaf data as healthy or diseased using SVM and kNN classifier, where SVM gives better accuracy of 93.67%. The obtained results indicate that the proposed methodology outperforms the other algorithms in obtaining good classification accuracy.