Rainfall Prediction and Crop Recommendation in Smart Agriculture Using ANN And LIME

Abstract

In the domain of smart agriculture, predictive models are pivotal for optimizing crop management, yield forecasting, and rainfall prediction. This study examines the implementation of machine learning and deep learning algorithms, specifically XGBoost, Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN), to enhance the accuracy and interpretability of agricultural predictions. These models are utilized to predict crop performance based on environmental factors, historical data, and weather patterns. By incorporating Explainable AI (XAI) techniques, the study ensures transparent decision-making, allowing farmers to trust and interpret the model predictions. The XGBoost algorithm is employed for high-performance classification and regression tasks, while ANN is used to model complex, nonlinear relationships in agricultural data. Additionally, LSTM networks are leveraged for their ability to capture temporal dependencies and sequence patterns in time-series data, such as weather and crop growth over time. The integration of these models with XAI techniques provides a powerful framework for developing predictive tools that assist in optimizing crop recommendations, forecasting yields, and predicting rainfall. This approach contributes to more efficient, data-driven, and sustainable farming practices.