Plant Disease Detection using Deep Learning Approach
Satyajit Khandu Khot, Vivek Barme, Dr.S.D.Bharkad
Plant disease detection, Machine learning, Deep learning, Convolution neural network, Mobile application, Python, Google Cloud
The potential growth of developing countries like India depends on agriculture. The basic need of humans and animals is food. Disease plants directly affect the yield of crops and which leads to an imbalance in the economy of developing countries. So Plant Disease detection is very important. Traditionally diseases are detected by professionals or plant pathologists with an empty eye, but this approach is time-consuming and expensive also. Now in the digital era, machine learning and deep learning is widely used in various sector and agriculture is one of them. In this paper, we have created the model with the help of a convolution neural network for the detection of disease and deployed it on google cloud to use in the mobile app. The model can easily identify 11 different kinds of diseases which contain 13610 images of 3 plant species. For this, we have used the Kaggle dataset. with the Model, we have achieved 95% accuracy over different plants. This shows that the model achieved a good accuracy rate for plant disease detection.
Article Details
Unique Paper ID: 156260

Publication Volume & Issue: Volume 9, Issue 3

Page(s): 280 - 285
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