Deep Learning Based Animal Intrusion Detection System

With the increasing instances of human-wildlife conflicts and the need for sustainable agricultural practices, there is a growing demand for effective and non-invasive methods to mitigate crop damage caused by wild animals. In this study, an innovative method employing Convolutional Neural Networks (CNNs) is introduced for wild animal detection in agricultural fields, with the goal of improving crop protection strategies. The proposed system employs a deep learning model based on CNN architecture to analyze images captured by surveillance cameras installed in agricultural landscapes. The CNN is trained on a diverse dataset of images containing both crop and wild animal instances, allowing it to learn distinctive features for accurate detection. From the lecture study, we identified that only few studies attempted to detect tiger intervention. So we have applied CNN model to detect tiger intervention in agriculture field. The model’s ability to generalize to various species and environmental conditions is crucial for its applicability in real-world scenarios. Additionally, it achieved an accuracy of 96%, highlighting its effectiveness in accurately detecting tiger intervention.