A Machine Learning Approach for Detection and Classification of Potato Disease

Gopesh Kumar Bharti; Mr. Mahadev Bag; Dr. Tanushree Chatterjee; Dr. Vivek Kumar Sinha

Authors

Gopesh Kumar Bharti Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur, Raipur, India
Mr. Mahadev Bag Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur, Raipur, India
Dr. Tanushree Chatterjee Department of Biotechnology and Engineering, Raipur Institute of Technology, Raipur, Raipur, India
Dr. Vivek Kumar Sinha Department of Computer Science and Engineering, Lovely Professional University , Punjab

Keywords:

Healthy, early blight, late blight disease classification

Abstract

Potato farming faces huge financial losses every year due to the prevention of diseases like early blight and late blight. A fungus causes early blight, and a specific microorganism is the most common culprit that causes late blight. The disease primarily affects leaves and stems, potentially causing defoliation and, under favorable conditions, increased susceptibility to tuber infection. Early detection and accurate identification of this disease are important to implement appropriate treatment and reduce economic losses. Fungi cause early blight, while a specific microorganism causes late blight. Prompt adoption of appropriate treatment can save resources and prevent crop waste. The objective of this project is to develop a classification system capable of identifying the type of disease present in potato plants. The three target classes are healthy, early blight, and late blight. By leveraging advanced techniques in image processing and machine learning, we aim to provide farmers with a reliable tool for early disease detection and accurate differentiation, allowing them to take timely and appropriate action to protect their potato crops and reduce financial losses.

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