Potato Disease Detection Using CNN
Sunil Kumar, Vipul Sharma, Abhishek Kumar, Bhuvan H.C, B.P Chaitra
Convolutional neural networks, image processing, machine learning, potato disease detection, agricultural technology.
In this study, a Convolutional Neural Network (CNN) is used to classify potato leaf diseases using machine Learning. The proposed method involves preprocessing leaf image data, training a CNN model on this data, and evaluating the model's performance on a test set. Experimental results demonstrate that the CNN model achieves an impressive overall accuracy of 93% in identifying Early Blight, Late Blight, and Healthy potato leaves. This approach presents a reliable and effective solution for disease identification in potatoes, crucial for maintaining food security and reducing agricultural losses. Importantly, the model performs well even in cases of severe infections. This research underscores the potential of machine learning techniques for classifying potato diseases, offering a valuable tool for automated disease management in potato farming.
Article Details
Unique Paper ID: 164780

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2240 - 2244
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