Development of Smart Agricultural Decision Support Systems Using Artificial Intelligence

Abstract

The increasing complexity of modern agriculture, driven by climate variability, resource constraints, and the demand for higher productivity, has necessitated the development of intelligent decision support systems. This study presents the development of a smart agricultural decision support system using artificial intelligence to enable data-driven and efficient farm management. The proposed methodology integrates multi-source datasets, including soil characteristics, weather conditions, crop health indicators, irrigation practices, and historical farm records. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure accuracy and consistency. Advanced machine learning and deep learning models, such as Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, are employed to analyze complex relationships and generate predictive insights for optimal decision-making. The system 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 practices. Additionally, the platform provides actionable recommendations for irrigation scheduling, fertilization, pest control, and crop planning, supporting proactive farm management. The findings highlight the effectiveness of integrating artificial intelligence into decision support systems for precision agriculture applications. The study concludes that smart agricultural decision support systems offer a scalable, efficient, and data-driven solution for optimizing agricultural operations, promoting sustainability, and ensuring long-term food security.