Explainable AI for crop Recommendation,yield Forecasting and Rainfall prediction in Smart Agriculture

Abstract

In the domain of smart agriculture, predictive models play a critical role in enhancing crop management, yield forecasting, and rainfall prediction. This study explores the application of several machine learning and deep learning algorithms, including Decision Tree, Random Forest, AdaBoost Classifier, XGBoost Classifier, Recurrent Neural Network (RNN) and Artificial Neural Network (ANN), for developing accurate and explainable AI solutions. These models are employed to predict crop performance based on environmental factors, historical data, and weather patterns. Explainable AI (XAI) techniques are integrated to provide transparent decision-making, ensuring that farmers can trust and interpret the predictions made by the models. The Decision Tree and Random Forest algorithms are leveraged for their interpretability and ability to handle large datasets, while XGBoost and AdaBoost are utilized for high-performance classification. The RNN models are explored for their potential to capture complex temporal and spatial patterns in agricultural data. Overall, the combination of these algorithms, along with XAI techniques, provides a robust framework for optimizing crop recommendations, predicting yields, and forecasting rainfall in smart agriculture systems, ultimately leading to more efficient and sustainable farming practices.

Keywords: Predictive Models,Environmental Factors,Weather Patterns,Data Interpretability,

Agricultural Data Analysis,Model Interpretability,High-performance Classification,

Sustainable Farming,Agricultural Decision Support Systems,Temporal and Spatial Patterns in Agriculture