Novel Proportionate Scrutiny On Crop Protection From Creatures By Deep Learning

The main objective of this paper is to protect the crop from animal attacks. The conventional techniques have the same kind of security applied to all the types of animals detected based on a Passive IR sensor, and only single-stage protection is applied. The images were captured and identified with the help of machine learning and deep learning techniques. The project was designed with a rectangular farm area. On each side of the entrance, the device was installed to capture the image for processing to identify the animals, based on the animal identification, different levels of security were applied, and that will produce different sounds with different Db levels and variety of dazzling light. This work provides a comprehensive description of the design, development, and assessment of an intelligent animal repelling system that allows for to detection and recognition of the animals. The enhancement is done by different levels of protection and different types of protection based on the classified animals. In initial level protection, making the noise and lightning from the opposite side send the animal out of the farm. If the animals are still on the farm, initiating the next stage that the image will send to the owner. The accuracy of all the methods discussed will be compared based on the complexity of the technique, implementation cost, reciprocating time, and accuracy of animal detection. In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to ending devices, thereby reducing latency, improving response time, and ensuring secure data exchange