Addressing Crop Damage from Animals with Faster R-CNN and YOLO Models

Deforestation leads to big problem where wild animals are entering into villages. It creates a great loss of property and life of wild animals. To protect animal from human being and vice versa, we can design a system to help farmers by reducing crop vandalization as well as diverting the animal without any harm. So, here we are trying to reduce crop vandalization by wild animals. The goal of this project is to detect wildlife using the TensorFlow object recognition API on a live feed from a camera. Object recognition is a commonly employed method in a variety of applications, including face recognition, driverless cars, and identifying sharp things such as knives and arrows. A tragic incident occurred when a pregnant elephant stepped into a nearby town in looking for food and died after eating a pineapple stuffed with crackers in Kerala’s valley forest. We can make a system which will detect animals on a farm, protect animal from human being and reduce the crop from damage caused by animal using convolutional neural networks (CNNs) algorithm, deep learning and some more new technologies. In this project, we will get live video from camera, we will apply TensorFlow object detection API to incoming video and we will try to find wild animal in that video. If an animal is discovered, a warning message should be sent to the farmer, and if a man attempts to kill the animal, a message should be sent to the nearest forest office.