Smart Farming Technologies: AI-Driven Crop Monitoring and Precision Agronomy

Abstract

The integration of artificial intelligence (AI) into agriculture marks a significant advancement in addressing the global challenges of food security, resource efficiency, and climate resilience. This narrative review explores the role of AI-driven technologies in crop monitoring and precision agronomy, focusing on their applications, benefits, and challenges. AI-powered systems, such as machine learning models and computer vision algorithms, are increasingly used to analyze data from remote sensing, drones, and IoT-based soil sensors for early detection of crop stress, disease, and environmental fluctuations. These insights enable site-specific interventions and real-time decision-making, contributing to higher yields and more sustainable resource use. The review highlights case studies from both developed and developing regions, illustrating the practical impact of AI platforms in optimizing sowing dates, irrigation, fertilization, and pest control. Despite their transformative potential, challenges persist, including limited data quality, high infrastructure costs, low technological literacy among farmers, and concerns about data ownership and privacy. Furthermore, the environmental footprint of digital agriculture and issues of interoperability remain pressing concerns. Future directions emphasize the development of advanced AI models, autonomous machinery, and the integration of genomics and AI for accelerated crop improvement. Equally important are supportive policy frameworks and inclusive digital strategies to ensure equitable access to smart farming technologies. Overall, AI stands as a pivotal tool for reshaping agriculture into a more intelligent, sustainable, and resilient system.