Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest
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 train...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2025
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2150/ http://eprints.intimal.edu.my/2150/1/jods2025_08.pdf http://eprints.intimal.edu.my/2150/2/693 |
| Summary: | 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. |
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