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Prediction of Potato Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System
Potato is one of the most used crops in the world and 2 nd most important crop in Bangladesh. Our economy is largely affected by the production of potato. But its production is hampered due to different diseases of potato leaves. These diseases decrease production and increase the price of potatoes. Our objective is to develop an automated system which will predict the potato disease and helps farmers to take necessary steps. In this work, we implemented a model based on Convolutional Neural Network (CNN) which provides 98.33% accurate result in predicting different diseases of potatoes. This is the maximum accuracy gained for only potato disease prediction to the best of our understanding. The system is cost effective, less time consuming and provides an efficient way of predicting potato diseases from leaves. This will help the farmers and lead our country towards a digital agricultural system.
Potato Disease Prediction, CNN, Deep Learning
Md. Al-Amin, Tasfia Anika Bushra, Md Nazmul Hoq
Journal or Conference Name
1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019
Publish Year