EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN AGRICULTURE

Abstract

Artificial Intelligence (AI) is redefining the landscape of modern agriculture by offering innovative solutions to long-standing challenges such as low productivity, environmental stress, and inefficient resource utilization. As the global population continues to rise and arable land becomes scarcer, the integration of AI has emerged as a critical strategy to ensure food security, sustainability, and precision in agricultural practices. This study investigates the application of AI technologies in key agricultural domains including crop management, livestock monitoring, and supply chain optimization. A mixed-methods approach was employed, combining empirical data analysis, predictive modeling, and real-world case studies. The methodology involved developing AI models based on satellite imagery, sensor data, and time-series analytics to evaluate yield prediction, pest detection, and logistics planning. The results demonstrate that AI models significantly improve accuracy in crop yield forecasting, optimize irrigation and fertilization practices, and enable early detection of livestock diseases through wearable sensor data. Furthermore, AI-driven supply chain models were effective in reducing post-harvest losses and improving routing efficiency, leading to measurable gains in productivity and resource savings.The findings suggest that AI not only enhances operational efficiency but also contributes to environmental sustainability by reducing chemical input and water usage. However, the study also highlights challenges such as technological accessibility, data governance, and farmer training that must be addressed for equitable adoption. In conclusion, the integration of AI in agriculture presents transformative potential, provided it is supported by inclusive policy frameworks and capacity-building initiatives to ensure its benefits reach all stakeholders across the agricultural ecosystem.