Published Paper Details:

In order to increase production, sustainability, and efficiency, modern agriculture depends more and more on autonomous systems that combine AI, robotics, IoT, and GPS. With the least amount of human intervention, these systems maximize production while optimizing inputs like labor, fertilizer, and water. Optimizing resources through precision agriculture, to precisely regulate irrigation, fertilizers, and pesticide application while reducing environmental runoff, artificial intelligence (AI) and machine learning (ML) are used to analyze hyperspectral imagery, soil data, and weather forecasts. Autonomous tractors such as Kubota Agri Robo and John Deere 8R Use GPS guidance, AI navigation, LIDAR, GNSS, and computer vision to autonomously plough, seed, and harvest. Edge computing lowers latency and bandwidth needs by enabling real-time decision-making in the field. Smart farming equipment is powered by specialized AI chips that process sensor data locally. Precision farming is built on data collecting. Soil data, weather forecasts, crop health, nutritional levels, and stress factors may all be thoroughly analyzed through hyperspectral imaging, which records data over a broad range of the electromagnetic spectrum. This enables targeted responses and reduces resource use by assisting farmers in identifying issues like disease or nutritional deficiencies before they become apparent. Real-time insights into crop health, soil health, and environmental conditions are provided by sophisticated sensors and imaging technologies. Agriculture is undergoing a change thanks to autonomous systems that improve precision, sustainability, and efficiency. Modern farms can function more accurately and waste fewer resources because of the integration of AI, robotics, IoT, and machine learning, ushering in a new era of data-driven, intelligent agriculture.