Plant Disease Classification Using Texture-Based Methods through Leaf Images

Plant products have been a major source of food for animals, raw materials for industry and source of revenues to governments. In view of this, careful attention is needed for quality and quantity of plant products. Biotic and abiotic factors contribute immensely in hampering agricultural produce. In this research, computer vision techniques such as texture-based algorithms namely Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Binarized Statistical Image Features (BSIF) are employed in plant disease identification and classification. Nine different popular plant species are used with symptoms on leaf images to extract features to develop a novel system. We propose an approach that employs Decision-Level Fusion which is used to incorporate different algorithms’ strengths for a robust and more accurate system. The proposed method is also compared with Scale Invariant Feature Transform (SIFT) and its derivatives such as Dense Scale Invariant Feature Transform (DSIFT) and Pyramid Histogram of Visual Words (PHOW). The experiments are conducted on PlantVillage database that includes healthy and infected plant leaf images of tomato, apple, cherry, corn, grape, peach, pepper, potato and strawberry plants. Consequently, the diverse nature of the database and the high accuracy of the proposed system show that Decision-Level Fusion of texture-based features extracted from plant leaves are good in detecting and classifying plant diseases. Keywords: Plant disease identification, Computer vision, Texture-based features, Decision-Level Fusion