Empowering agricultural security with IoT and deep learning driven real-time alert systems

In the realm of smart agriculture, our primary objective is to enhance security in the agriculture field by combining IoT and computer vision technologies. By leveraging these advanced tools, we strive to protect field crops from unauthorized access, aligning with the core principles of smart agriculture that prioritize the safeguarding of agricultural assets. Various governmental initiatives have been innovated to tackle the issue of ensuring food security. However, the majority of farmers face limitation due to insufficient knowledge and financial means, to effectively manage issues such as field monitoring and object detection. Hence, we aim to create an energy-efficient real time system that intelligently detects objects and sends alerts to farmers specifically designed for agricultural purposes. By integrating ultrasonic sensors, Raspberry Pi edge, and real-time trained object detectors like Embed-YOLOv3 and EmbedT-YOLOv3, we achieve superior accuracy, faster processing, and enhanced speed. This system efficiently identifies and categorizes objects, instantly notifying farmers through a real time android application. Identification of objects followed by immediate action can preserve yields, significantly reducing crop damage and strengthening the country’s economy. This proactive approach can alleviate distress among numerous farmers. Achieving an exceptional precision rate of 97%, recall rate of 96%, and accuracy of 95.86%, our system harnesses low-power IoT devices coupled with deep learning to deliver robust crop protection in the agricultural field.