Leaf disease segmentation and classification of Jatropha Curcas L. and Pongamia Pinnata L. biofuel plants using computer vision based approaches

Abstract Several efforts have been made in finding alternate sources of energy. The production of bio-fuel from the extracts of plants like Jatropha Curcas L. and Pongamia Pinnata L. is most favored among all. But, due to certain biotic factors, the growth of these plants get affected, therefore reducing the overall production. To formulate the demand and automate the disease diagnosis system a Computer vision methodology is proposed in this work. For disease region segmentation, a Hybrid Neural Network incorporated with Superpixel clustering is proposed. Color, shape, and texture features are evaluated using different algorithms. Finally, seven different Machine Learning techniques were used to classify the images among three categories. Segmentation results with average Specificity = 0.9534, 0.9795, Sensitivity = 0.9637, 0.9805 and average Classification accuracy = 0.9857 ± 0.0285, 0.9095 ± 0.0688 and 0.9607 ± 0.0256 for both the plants when evaluated separately proved the supremacy of the proposed work.

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