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

	Text jods2025_08.pdf - Published Version Available under License Creative Commons Attribution. Download (235kB)
	Text 693 - Published Version Available under License Creative Commons Attribution. Download (22kB)

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

Actions (login required)

View Item