Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest

Abstract

Crop damage caused by animals is a significant challenge faced by farmers worldwide. Traditional methods for crop protection are often ineffective and labor-intensive. This paper explores the use of deep learning for real-time animal detection in agricultural settings. A deep learning model is trained on a dataset of images containing various animal species commonly found in agricultural environments. The model is then deployed on a camera-based system to detect and classify animals in real-time, providing farmers with timely alerts and enabling proactive measures to protect their crops. The proposed system offers a promising solution for improving crop protection efficiency and reducing losses due to animal damage. Results demonstrate a 95% accuracy in detecting animals, significantly outperforming traditional methods.