One of the greatest dangers to agricultural productivity is animal damage to agriculture. Crop raiding has become one of the most antagonistic humanwildlife conflicts as cultivated land has expanded into previous wildlife habitat. Farmers in India endures major risks from pests, natural disasters, and animal damage, all of which result in lesser yields. Traditional farming methods are unsuccessful and hiring guards to watch crops and keep animals at bay is not a practical solution. It is critical to protect crops from animal damage while also redirecting the animal without injuring it, as the safety of both animals and people is essential. To get over these obstacles and accomplish our goal, we employ the deep learning concept of convolutional neural networks, a subfield of computer vision, to identify animals as they enter our farm. The primary goal of this project is to constantly monitor the entire farm using a camera that records the surroundings at all hours of the day. We identify animal infiltration using a CNN algorithm and Xgboost and notify farmers when this occurs.

Keywords : CNN, XGBoost, Computer vision.