Abstract
Grapevine (Vitis vinifera L.) is a major fruit crop with commercial importance worldwide. It is highly susceptible to a large number of pathogens that cause diseases, resulting in loss of production. The timely estimation of symptom severity can become a major milestone in disease management. Due to the advancement in the field of computer vision, automatic estimation of symptom severity is made possible. This paper proposes a fuzzy logic-based method for the estimation of symptom severity in grapevine. The symptomatic area is segmented from the leaf images using a fuzzy membership function. Additionally, consideration of the lesion spots along with the symptomatic area is proposed to evaluate the symptom severity using the fuzzy inference system. The performances of the existing techniques are studied and compared and it is shown that the proposed automatic method for estimation of symptom severity yields the best results with an accuracy of 95.33%.
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