Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{182273,
        author = {Shreeja M},
        title = {Smart Farmland Defense: YOLOv8-Powered Animal Detection and Alert System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1966-1969},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182273},
        abstract = {Farmlands are frequently vulnerable to animal intrusions, which can result in substantial crop damage and financial losses for farmers. This paper vision—particularly the YOLOv8 object introduces "Smart Farmland Defense: YOLOv8-Powered Animal Detection and Alert System", a system developed to address these challenges through smart surveillance and timely alert mechanisms. Leveraging advancements in computer detection model—and with the potential integration of drone technology, the system is designed to enable real-time identification of animals in agricultural environments. This study focuses on the design, implementation, and prospective application of such a system, emphasizing the role of deep learning and image processing techniques in enhancing agricultural security.},
        keywords = {Farmland Protection, Animal Intrusion Detection, YOLOv8, Computer Vision},
        month = {July},
        }