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

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
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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