Vol. 11, Special Issue 10 (2022)

Author(s):
Kumar Sanjeev and Suneeta Paswan

Abstract:
In India, potatoes are the vegetable crop that is most grown. According to estimates, diseases played a major role in the yield loss of potatoes. It is crucial to find diseases in their early stages. These problems have been resolved using image processing techniques. These images are used to extract the 76 attributes related to colour, texture, and area. Finally, to categorise the disease, these features are fed into the Feed Forward Neural Network (FFNN) and K- Nearest Neighbors (KNN) models. The accuracy rates for the ANN and KNN classifiers were 84.76% and 63.33%, respectively. A comparison of ANN and KNN classifiers has been started, and the results demonstrate that ANN classifiers outperformed KNN for the identification of potato tuber diseases.