Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, and future prospects

The integration of Internet of Things (IoT) sensors and Machine Learning (ML) technologies has transformed precision agriculture by enabling data-driven, adaptive, and efficient farming practices. IoT sensors provide continuous, high-resolution monitoring of critical agricultural parameters, including soil health, crop growth, and environmental conditions. Coupled with advanced ML algorithms, this data facilitates predictive analytics and real-time decision-making, optimizing resource utilization for irrigation, pest control, and yield prediction. Recent innovations, such as edge computing, Reinforcement Learning (RL), and Transfer Learning, have further enhanced the scalability and adaptability of IoT-ML systems, enabling dynamic responses to complex agricultural challenges. This review synthesizes evidence from case studies and emerging applications to illustrate the potential of IoT-ML integration in driving sustainable agricultural development. Highlighted examples include IoT-enabled irrigation systems achieving over 30% water savings and RL-driven automation for efficient pest and disease management. Despite significant progress, barriers such as high initial investment costs, connectivity limitations, and data integration challenges hinder widespread adoption, particularly in resource-constrained regions. The study provides a comprehensive evaluation of IoT-ML applications, explores strategies to address implementation hurdles, and outlines a roadmap for scaling these technologies globally. By advancing precision agriculture through technological innovation, this integration represents a critical pathway toward enhancing food security, reducing environmental footprints, and promoting resilient agricultural systems.