A Leaf Infection Detection Using Fuzzy Support Vector Machine


Pravinkumar Sonsare
Roshni Khedgaonkar
Praful Pardhi


Agriculture plays a very essential role in the food industry and a big economic source of clothing. Identication of
infection at a very initial stage can prevent a massive loss of yield and productivity. The infection can be recognized
with signs of colors on the leaf, stems. Leaves exhibit symptoms by changing color, showing spots on it. This
could also be done by manually inspecting each leaf but it can be time-consuming and prove to be expensive. This
paper aims to distinguish the infected part of the leaf. Identication of infection on leaf with modern automatic
techniques can be protable and resource-saving. We processed image which plays the important role used for
automatic detection and classication. We have used approximately 19000 images samples of bell paper, potato,
tomato to train our model. We proposed a model in which we are using k-mean, SVM, FSVM classier. We found
that FSVM performs better than other classier. We achieved 80.33


How to Cite
Sonsare, P., Khedgaonkar, R., & Pardhi, P. (2021). A Leaf Infection Detection Using Fuzzy Support Vector Machine. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.433


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