Animal Intrusion Detection System: Protected Crops and Promoted Safety using Machine Learning

Animal assaults on crops were one of the main causes of the precipitous drop in agricultural output. The expansion of farmland into once wild areas has led to an increase in crop raiding, one of the most acrimonious human-animal clashes. The harvests of Indian farmers were significantly impacted by pests, natural disasters, and animal damage. Since it was too expensive to hire guards to keep an eye on crops and keep wild animals away, farmers stuck to ineffective old ways. To prevent agricultural damage by animals and to reroute animals without harming them, it was essential to ensure everyone's safety. Animal identification using machine learning helps us solve the aforementioned challenges and reach our objectives. In this study, pictures of the whole farm were taken at regular intervals. The photographs were broken down using the Watershed technique. The features of the training set are retrieved by use of a 2D Gabor filter bank. Classification is performed with the Support Vector Machines method. The percentage of correct responses for each test image is determined.