Sustainable Crop Monitoring and Management for Enhanced Agricultural Productivity Through IoT, AI&ML: Case Studies and Innovations

The advent of the Internet of Things (IoT) technology has sparked a transformative revolution in various sectors, including agriculture. This paper delves into the application of IoT in agriculture, particularly focusing on precision or smart farming. IoT-based smart farming integrates advanced sensors, communication technology, and data analytics to optimize agricultural processes, enhance resource efficiency, and increase overall output. Key elements include sensor networks deployed in fields, data connectivity infrastructure, and cloud-based platforms for real-time data analysis. The gathered data is transmitted to centralized systems through communication networks, empowering farmers to access information on pest management, fertilization, and irrigation through intuitive interfaces. The integration of Global Positioning System (GPS) technology enables accurate farm mapping, improving crop monitoring and management. As IoT-based smart farming evolves, the transformation of conventional agricultural processes into more effective, sustainable, and technologically sophisticated systems is anticipated. To realize the full potential of IoT in agriculture, overcoming obstacles and encouraging widespread adoption are crucial. This study proposes an IoT-based smart farming system coupled with an effective machine learning-based prediction method to forecast crop productivity and drought, providing accurate decision support to agriculturalists. The results demonstrate a significant increase in crop productivity, with a yield of 64.240 tonnes achieved using medium technology compared to the conventional method’s 35 to 40 tonnes, showcasing a remarkable 46.875% increase.