This IEEE project involves development of a chat-bot for agriculture which can assist farmers. Initial phase involves:
- Web app development
- Disease detection in crops
- Weed detection in crops
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Google Doc Link :- Leaf Detection IEEE
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Web app link :- Web App
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Expected outcome video :- Camera
This docs file has the links to the datasets found for leaf detection. It also contains details about different leaf diseases.
| Sr. No. | Code | Link to Model summary | Learning rate | Epochs | Accuracy (Train) | Accuracy (Validation) |
|---|---|---|---|---|---|---|
| 1 | Model 1 code | Model 1 | 0.01% | 1 | 8.44 % | 8.4375 % |
| 2.0 | Model 2_0 code | Model 2_0 | 0.01% | 5 | 85.69 % | 92.13 % |
| 2.1 | Model 2_1 code | Model 2_1 | 0.01% | 5 | 87.20 % | 92.40 % |
| 2.2 | Model 2_2 code | Model 2_1 | 0.1% | 5 | 88.52 % | 92.91 % |
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Plant Disease Classifiaction using CNN and Generative Adversial Networks
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Going Deeper with Convolutions
This might help to learn about CNN's mentioned in the previous paper i.e. Plant Disease Classification using CNN
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Plant Leaf Disease Detection and Classification Using Image Processing Techniques
- Linking the model to the web application to dynamically upload images and test for disease
- Improving the model for better accuracy