Early Detection of Potato Leaf Diseases using Convolutional Neural Network
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Abstract
Potato is the most important tuber crop in the world, with over 125 countries farming it. Potato, after rice and wheat, is the crop consumed by a billion people worldwide virtually every day. However, due to different fungal and bacterial illnesses, the quality and quantity of the potato crop is deteriorating. Early disease detection is difficult due to differences in environmental conditions, plant type, and plant disease symptoms. Several machine learning algorithms have been developed in recent study to recognize potato leaf diseases. In this work, a multi-layer deep learning model for detecting potato leaf disease is constructed. The features of the potato leaves are recovered from the image of the potato plant in the first layer using the image segmentation approach. A new deep learning technique based on a convolutional neural network [CNN] was created at the second level to detect fungal and bacterial infections in potatoes. The dataset for leaf disease contains 12000 photos gathered in real time and from the database. The proposed deep learning approaches identified potato diseases with 99.76% accuracy
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