Potato Leaf Disease Detection Using Machine Learning

Jayashree Pasalkar, Ganesh Gorde*, Chaitanya More, Sanket Memane and Vaishnavi Gaikwad

Department of Information Technology, AISSMS Institute of Information Technology, Pune, Maharashtra, India.

Corresponding Author E-mail: ganesharungorde202@gmail.com

DOI : http://dx.doi.org/10.12944/CARJ.11.3.23

Abstract:

Potato is one of the most important crops worldwide, and its productivity can be affected by various diseases, including leaf diseases. Early detection and accurate diagnosis of leaf diseases can help prevent their spread and minimize crop losses. In recent years, Convolutional Neural Networks (CNNs) have shown great potential in image classification tasks, including disease detection in plants. In this study, we propose a CNN-based approach for the prediction of potato leaf diseases. The proposed method uses a pre-trained CNN model, which is fine-tuned on a dataset of potato leaf images. The dataset includes images of healthy leaves and leaves infected with different diseases such as early blight and late blight. The trained model is then used to classify new images of potato leaves into healthy or diseased categories. The proposed approach achieves 97.4% accuracy in the classification of potato leaf diseases such as early blight potato leaf disease and late blight potato leaf disease, and can be used as an effective tool for early detection and management of these diseases in potato crops.

Keywords:

Convolutional Neural Networks (CNNs); Confusion Matrix; Deep Learning; Machine Learning

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