The project presents plant disease detection using image processing techniques for use in the agricultural field. In agriculture research of automatic plant disease detection is essential in monitoring large fields of crops, and thus automatically detects symptoms of disease when given an input image. For this approach, the automatic classifier CNN had been used for classification based on learning with some training samples of that fifteen category. Finally, the simulated result shows that a used network classifier provides minimum error during training and better accuracy in classification. The input is given in the form of an image where after prediction it reveals the output whether it is healthy or diseased and it also reveals the category it belongs to.
Ghanshyam89/Plant-Leaf-Disease-Detection-using-Deep-Learning
The project presents plant disease detection using image processing techniques for use in the agricultural field. In agriculture research of automatic plant disease detection is essential in monitoring large fields of crops, and thus automatically detects symptoms of disease when given an input image. For this approach, the automatic classifier CNN had been used for classification based on learning with some training samples of that fifteen category. Finally, the simulated result shows that a used network classifier provides minimum error during training and better accuracy in classification. The input is given in the form of an image where after prediction it reveals the output whether it is healthy or diseased and it also reveals the category it belongs to.
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