Smart Agriculture Assistant:” An Integrated AI-Driven Decision Support System for Crop Recommendation, Yield Prediction, Fertilizer Optimization and Market Price Forecasting”

Abstract

Agriculture plays a vital role in ensuring food security and supporting economic development; however, farmers continue to face challenges such as inappropriate crop selec-tion, unpredictable weather conditions, excessive fertilizer usage, fluctuating market prices, and the lack of integrated decision-support systems. To address these challenges, this paper proposes a Smart Agriculture Assistant, an integrated Artificial Intelli-gence (AI)-driven decision support system that combines crop recommendation, crop yield prediction, fertilizer optimization, and market price forecasting within a unified framework. The proposed system utilizes multiple machine learning algorithms, including Random Forest, XGBoost, CatBoost, Long Short-Term Memory (LSTM), and SHAP (SHapley Additive exPlanations), to analyze agricultural and market datasets collected from Kaggle and Agmarknet. The framework processes soil nutrients (N, P, K), pH, temperature, humidity, rainfall, crop characteristics, and historical market price data to generate intelligent recommen-dations and predictive insights for farmers. Data preprocessing techniques such as missing value handling, normalization, feature selection, categorical encoding, and train–test splitting are em-ployed to improve model performance and prediction reliability. Experimental evaluation demonstrates that the proposed hybrid framework achieves an accuracy of 99%, with 98% precision, 98% recall, and 98% F1-score, outperforming conventional machine learning models in agricultural decision-making tasks. The proposed system enables farmers to optimize crop selec-tion, improve fertilizer utilization, enhance crop productivity, and identify profitable market opportunities, thereby promoting sustainable, data-driven, and precision agriculture practices.