AI-Based Modeling of Agricultural Sustainability Using Multi-Source Data Integration

Abstract

The growing need to balance agricultural productivity with environmental conservation and resource efficiency has necessitated the development of advanced sustainability modeling frameworks. This study presents an artificial intelligence-based approach for modeling agricultural sustainability using multi-source data integration to support data-driven and environmentally responsible farming practices. The proposed methodology incorporates diverse datasets, including soil health indicators, water usage patterns, energy consumption, crop productivity records, greenhouse gas emissions, and land management practices, along with climatic variables. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning and deep learning models, such as Random Forest, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, are employed to capture complex nonlinear relationships among sustainability factors and to generate predictive insights and composite sustainability indices. The system is designed with real-time data processing and adaptive learning capabilities, enabling continuous monitoring and dynamic evaluation of sustainability performance across different agricultural systems. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic conditions. Experimental results demonstrate that the proposed approach significantly improves the accuracy of sustainability assessment and provides deeper insights compared to conventional evaluation methods. Additionally, the system offers actionable recommendations for optimizing resource use, reducing environmental impact, and enhancing long-term productivity. The findings highlight the effectiveness of integrating artificial intelligence with multi-source data for precision agriculture applications. The study concludes that AI-based agricultural sustainability modeling systems provide a scalable, efficient, and data-driven solution for promoting sustainable farming practices, improving resource efficiency, and ensuring long-term agricultural resilience.