Published Paper Details:

The security of farmers' fields has turned into a main pressing issue because of expanding episodes of burglary and intercession by reprobates. Customary safety efforts, for example, safety officers, fencing, and CCTV cameras have not had the option to forestall these occurrences. In this task, we propose a security framework that utilizes IoT and AI procedures to forestall burglary and recognize criminals in the field region. The proposed security framework comprises of an organization of sensors, cameras, and savvy gadgets that are associated with a focal center point. Any intrusion in the field area is detected by the sensors, which transmit a signal to the central hub. The cameras catch the picture of the gatecrasher and send it to the focal center. The central hub then analyzes the image and determines the intruder using machine learning algorithms. The proposed system's machine learning algorithms are based on deep learning methods like the Global System for Mobile Communication (GSM) Module, the Arduino Uno board, and the light-dependent resistor Sensor. An extensive set of images of intruders in the field are used to train these algorithms. By taking pictures of intruders in various lighting and weather conditions, the dataset is created. The proposed security framework likewise remembers a versatile warning that cautions the farmer for constant in the event of any interruption by settling on a decision alert. The farmer receives a call alert in the field area via mobile notification. The farmer can then make a fitting move to forestall the robbery and interruption of animals and inform the specialists if vital. The proposed security system is an efficient method for identifying intruders in the field area and preventing theft. The system is a smart, proactive security system that makes use of IoT and machine learning to detect any intrusion and notify the farmer immediately. To provide farmers with enhanced security, the system can be easily installed and integrated with existing security measures.