AI-Based Agricultural Decision Support Systems Using Machine Learning and Data Analytics

Abstract

The increasing complexity of modern agriculture, driven by climate variability, resource constraints, and the need for enhanced productivity, has necessitated the development of intelligent decision support systems. This study presents an artificial intelligence-based agricultural decision support system that leverages machine learning and data analytics to enable efficient and data-driven farm management. The proposed methodology integrates multi-source datasets, including soil characteristics, weather parameters, crop health indicators, irrigation practices, and historical yield records. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning models, such as Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, are employed to capture complex nonlinear relationships and generate predictive insights for optimal decision-making. The system incorporates data analytics techniques to evaluate patterns, trends, and performance metrics, supporting proactive and adaptive management strategies. The framework is designed with real-time data acquisition and adaptive learning capabilities, enabling continuous monitoring and dynamic response to changing environmental conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic zones and crop types. Experimental results demonstrate that the proposed approach significantly improves decision accuracy, enhances resource utilization efficiency, and increases crop productivity compared to conventional farming methods. Additionally, the system provides actionable recommendations for irrigation scheduling, fertilization, pest control, and crop planning. The findings highlight the effectiveness of integrating artificial intelligence with data analytics for precision agriculture applications. The study concludes that AI-based agricultural decision support systems offer a scalable, efficient, and data-driven solution for optimizing farm operations, promoting sustainability, and ensuring long-term agricultural resilience.