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Explainable AI Crop Recommendation, Yield Forecasting and Rainfall Prediction in Smart Agriculture

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To develop an AI-based system for crop recommendation, yield forecasting, and rainfall prediction in agriculture.
Utilized machine learning models such as Random Forest, XG Boost, and Artificial Neural Networks.
Trained models on agricultural data including temperature, rainfall, humidity, and soil nutrients.
Incorporated Explainable AI (XAI) using SHAP for interpretation of predictions.
Achieved 98% accuracy for crop recommendation.
Reached 96% accuracy for rainfall prediction.
Enhanced user trust through interpretable AI predictions.

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Smart agriculture system improves crop yield and weather predictions, indicating enhanced farming decisions.

REDDY et al. (2026) studied this question.

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