G., Ramya and J., Sreeja and K., Jyothi and J., Radhika and R., Vigneshwari (2025) Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest. Journal of Data Science, 2025 (08). pp. 1-13. ISSN 2805-5160
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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.
Item Type: | Article |
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Uncontrolled Keywords: | Deep Learning, Object Detection, Animal Detection, Crop Protection, Agriculture |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software S Agriculture > SF Animal culture |
Depositing User: | Unnamed user with email masilah.mansor@newinti.edu.my |
Date Deposited: | 03 Jul 2025 05:28 |
Last Modified: | 03 Jul 2025 05:28 |
URI: | http://eprints.intimal.edu.my/id/eprint/2150 |
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