Abstract
Disease in a crop is a major factor which affects the growth of the plant and projects disease management a challenging area in agriculture. The identification of the disease and the estimation of its severity are the building blocks of effective disease management system. Disease identification can be possible by visual inspection but the estimation of severity is relatively difficult by visual inspection. In this paper, automatic severity estimation is done by calculating the infected area using Fuzzy logic. The recognition accuracy of the proposed fuzzy system is 87.5, 86.67 and 85.83% as compared to 79.16, 75.83 and 75% in the crisp method for black rot, black measles and leaf blight infected grape images respectively. The proposed technique will help to quantify the diseases accurately.
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Nagi, R., Tripathy, S.S. (2020). Infected Area Segmentation and Severity Estimation of Grapevine Using Fuzzy Logic. In: Sahana, S., Bhattacharjee, V. (eds) Advances in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 988. Springer, Singapore. https://doi.org/10.1007/978-981-13-8222-2_5
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DOI: https://doi.org/10.1007/978-981-13-8222-2_5
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