This project helps users predict crop yield, suggest the best fertilizers, and recommend suitable crops for specific conditions using machine learning models.
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Crop Yield Prediction:
- Utilizes Linear Regression for basic predictions based on factors like rainfall, temperature, and soil nutrients.
- For more complex scenarios, a Random Forest model is employed to handle non-linear relationships between multiple variables.
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Fertilizer Recommendation:
- Recommends appropriate fertilizers by analyzing soil properties such as nitrogen, phosphorus, potassium (NPK), pH level, and moisture content.
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Crop Type Suggestion:
- Suggests the best crop to grow based on environmental data, soil type, weather, and historical crop performance using machine learning models.