Abstract
Early diagnosis of plant leaf disease, i.e., detection in the initial development stage, is a promising area of research focusing on smart agriculture involving computer vision. Automatic detection can significantly minimize human labor employment due to regular supervision. Terminalia Arjuna is a multi-purpose tree primarily found on the Indian subcontinent. The various chemical compounds of the Arjuna leaf are frequently used in medicine. Also, Terminalia Arjuna leaf is utilized in sericulture as a food source for moths. Leaf spot disease of Arjuna is common, and it is necessary to initiate treatment as soon as the disease appears on the leaves to prevent further spread on other leaves and other trees. This study used a multi-objective optimized simple linear iterative clustering (SLIC) algorithm to precisely locate leaf spots in affected areas. The entire leaf surface is segmented into two types of superpixels, healthy and unhealthy. The color moment features have been extracted for classification. The classification accuracy of two types of superpixels using four well-known classifiers has been reported, and SVM achieved the highest classification accuracy at \(99.60\%\). Based on the categorized superpixels, the severity score of the leaf spot disease has been computed for various sample leaves. The experimental findings demonstrate the applicability and robustness of the proposed method.
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Samanta, S., Pratihar, S., Chatterji, S. (2023). Leaf Spot Disease Severity Measurement in Terminalia Arjuna Using Optimized Superpixels. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_55
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