This is a web application based on machine and deep learning models for crop disease detection and fertilizer and crop recommendation. It is leveraging the power of AI, improve efficiency, and achieve maximum growth in Agriculture.
- Flask
- Tensorflow
- scikit-learn
- bootstrap
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Crop recommendation:
By leveraging soil data such as NPK ratios, moisture levels, temperature, and rainfall in a particular field region, AI models can provide valuable insights and recommendations for the best crops to grow.
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Fertilizer recommendation:
By utilizing soil data such as type, temperature, pH level, NPK ratios, and the specific crop type, an application can provide recommendations on the best fertilizer to use. This helps ensure the optimal health of crops and maximizes the overall yield of the field.
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Crop diseases detection:
By utilizing image recognition models, users can provide an image of a crop and its corresponding type, and the model can predict whether the plant is healthy or not.
The datasets used for this project are from kaggle: