AI-Based Agricultural Decision Systems Using Machine Learning and Predictive Analytics

Abstract

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