MHassaanButt/Rice-Disease-Classfication
In this project, I used Hybrid deep CNN transfer learning on rice plant images, perform classification and identification of various rice diseases. I employed Transfer Learning to generate our deep learning model using Rice Leaf Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently.
This tool helps rice farmers, agricultural scientists, and crop inspectors quickly identify common rice plant diseases. By analyzing images of rice plants, it can classify various diseases, helping you understand what's affecting your crops. The output is a clear identification of the specific disease present, allowing for timely intervention.
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Use this if you need an automated and efficient way to detect and classify diseases in rice plants using visual inspection.
Not ideal if you need to diagnose diseases in crops other than rice or require a highly detailed, laboratory-based pathological analysis.
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May 29, 2022
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