Diseased Leaf Segmentation from Complex Background Using Indices Based Histogram

Abstract

Early detection of plant diseases is a key to avoid losses in agriculture products' quality and quantity. The study of plant diseases entails the examination of visually discernible patterns on the plant. Plant health monitoring and disease identification are important for long-term agriculture. Manually monitoring plant diseases is extremely difficult that requires a great deal of effort, knowledge of plant diseases, and an inordinate amount of time consumed. As a result, image processing methods are employed to identify plant diseases. In the disease detection process, various phases of digital image processing are incorporated. The proposed work adapted a different kind of segmentation for getting the plant disease attributes based on the leaves images. The dataset used is collected from Plant Village, Kaggle, and Mendeley datasets with different plant leave images that include different shape, margin, and texture features for identifying the disease attacked to it. The collected dataset is sliced with train and test data classifiers and processed. Segmentation performance by an indices-based histogram approach resulted in 92.06% accuracy.