From Traditional to Intelligent Agriculture: A Vision for the Future

Abstract

The transition from traditional agriculture to intelligent, data-driven farming systems is increasingly critical for addressing challenges related to climate change, resource limitations, and food security. This study presents a comprehensive framework for intelligent agriculture by integrating Internet of Things technologies, machine learning techniques, and decision support systems to enhance agricultural productivity and sustainability. The proposed approach follows a structured methodology involving data acquisition, preprocessing, feature selection, intelligent modeling, and performance evaluation. Experimental results indicate that intelligent agriculture improves water-use efficiency by approximately 28%, reduces fertilizer usage by 22%, and enhances crop yield prediction accuracy from 62% to 88% when compared with traditional farming practices. Early pest and disease detection capabilities are improved by nearly 35%, enabling timely intervention and reduced crop losses. These findings demonstrate that intelligent agriculture significantly outperforms conventional methods while promoting sustainable resource management. Despite challenges related to infrastructure and adoption, the study confirms that intelligent agriculture represents a promising and resilient solution for future agricultural systems.