A Study on Wild and Domestic Animal Detection for Farm Protection by using Computer Vision
Abstract
Crop protection against wild animal intrusion has become a pressing challenge with significant social and economic implications, particularly in agricultural-dependent nations like India. In response, an innovative AI-driven surveillance system is designed to detect and determine potential threats from animals to farm environments. The system accurately identifies and classifies animals in farm images by leveraging advanced computer vision techniques and machine learning algorithms, including Support Vector Machines, K-Means clustering, Random Forest, Decision Trees, and Logistic Regression. The model generalizes effectively by analyzing a diverse dataset comprising various animal species. Key features such as accuracy, precision, recall, F1-score, and confusion matrices are employed to assess model performance comprehensively. The results showcase high accuracy across multiple algorithms. The proposed system offers a promising solution to protect crops, minimize losses, and foster harmonious coexistence between farming and wildlife. The results demonstrate good accuracy for various algorithms: 92.75% for Logistic Regression, 86.47% for Decision Trees, 95.65% for Random Forests, and 94.20% for Support Vector Machines. This highlights how reliable the system is in classifying animals, providing a viable way to safeguard crops, reduce losses, and promote peaceful cohabitation between agriculture and wildlife.
Keywords
Animal Detection, Farm Protection, SIFT, Animal Intrusion, K Means
Thanks
We would like to thank Vishwakarma Institute of Technology, Pune, for the motivation to work on this project. We are also thankful to all who have helped directly or indirectly to carry out this work.
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