Harnessing AI Technologies for Sustainable Agricultural Practices: Innovations in Soil Analysis and Crop Management

Abstract

Challenges in the agriculture sector are becoming significant, with a rapidly growing population and declining agricultural productivity. Despite farmers' rigorous efforts to cultivate crops, they encounter numerous obstacles stemming from insufficient knowledge about soil characteristics, key influencing factors, and unpredictable weather patterns, compounded by inadequate access to financial resources from banks. This research highlights the transformative potential of advanced technologies in addressing these challenges, specifically Machine Learning (ML) and Computer Vision (CV). By implementing real-time soil analysis and predictive weather forecasting, farmers can gain valuable insights into soil health, nutrient composition, and moisture levels, enhancing their decision-making processes. Moreover, this study explores the development of Artificial Intelligence (AI) systems, including Unmanned Aerial Vehicles (UAVs), which automate labour-intensive agricultural tasks and significantly increase crop yield and sustainability. By integrating data-driven approaches, this research aims to create an autonomous agricultural framework that reduces human labour while optimizing resource utilization. Ultimately, this work contributes to advancing agricultural practices, promoting food security, and fostering sustainable farming techniques in the face of evolving environmental and economic challenges.