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: | , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2025
|
| 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 |
| _version_ | 1848766934012657664 |
|---|---|
| author | G., Ramya J., Sreeja K., Jyothi J., Radhika R., Vigneshwari |
| author_facet | G., Ramya J., Sreeja K., Jyothi J., Radhika R., Vigneshwari |
| author_sort | G., Ramya |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:59:01Z |
| format | Article |
| id | intimal-2150 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:01Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21502025-07-03T05:28:01Z http://eprints.intimal.edu.my/2150/ Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest G., Ramya J., Sreeja K., Jyothi J., Radhika R., Vigneshwari QA75 Electronic computers. Computer science QA76 Computer software SF Animal culture 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. INTI International University 2025-06 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2150/1/jods2025_08.pdf text en cc_by_4 http://eprints.intimal.edu.my/2150/2/693 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 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software SF Animal culture G., Ramya J., Sreeja K., Jyothi J., Radhika R., Vigneshwari Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest |
| title | Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest |
| title_full | Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest |
| title_fullStr | Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest |
| title_full_unstemmed | Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest |
| title_short | Animal Detection for Crop Protection Using Deep Learning: Insights from YOLO V3, R-CNN, Random Forest |
| title_sort | animal detection for crop protection using deep learning: insights from yolo v3, r-cnn, random forest |
| topic | QA75 Electronic computers. Computer science QA76 Computer software SF Animal culture |
| url | http://eprints.intimal.edu.my/2150/ http://eprints.intimal.edu.my/2150/ http://eprints.intimal.edu.my/2150/1/jods2025_08.pdf http://eprints.intimal.edu.my/2150/2/693 |