AI-Powered Decision Support Systems for Precision Agriculture: A Machine Learning Perspective
Keywords:
AI-powered decision support systems, precision agriculture, machine learning, crop yield optimization, resource management, IoT sensors, deep learning, sustainable farming, pest control, real-time agricultural insights.Abstract
The increasing demand for sustainable agriculture amidst growing global food requirements necessitates the integration of advanced technological solutions. AI-powered decision support systems (DSS) are emerging as pivotal tools in enhancing precision agriculture, enabling farmers to make data-driven decisions that optimize crop yield, resource management, and sustainability. This paper delves into the application of machine learning (ML) in precision agriculture, exploring various algorithms such as support vector machines, deep learning, and ensemble methods. These ML techniques offer real-time insights into soil health, pest control, irrigation needs, and crop growth predictions. By processing large datasets from IoT sensors, satellite imagery, and climate models, AI-powered DSS helps farmers minimize risks and improve productivity. Furthermore, this study discusses the challenges in adopting ML-based DSS, including data quality, computational demands, and the need for farmer education. The potential of AI in transforming agricultural practices is vast, and its successful implementation can lead to more resilient and sustainable farming systems
