Intelligent IoT-Enabled Crop Defense System for Preventing Animal and Bird Intrusion

Animal and bird intrusions in modern agricultural fields are persistent problems that may cause serious damage to crops and financial loss. AI-powered intelligent surveillance systems are imbued with the power of machine learning for effective and reliable solutions. Based on this, this paper proposes a real-time wildlife detection and monitoring system that will help farmers in effectively and efficiently detecting and handling intruding animals and birds. The YOLOv8 algorithm, which is a high-end deep learning framework for fast and accurate object detection, is used to implement the proposed system. A camera captures continuous images of the farm environment, then pre-processing of the images using OpenCV could be done, including noise reduction, resizing, and normalization, for increased accuracy in object detection. After detection, the images are sent to the remote server and deleted automatically after processing to save storage. Other steps necessary to provide real-time efficient performances are dimensionality reduction, feature extraction, and image compression. After detecting the intrusion, multiple automated responses from the system include sending an email to the farmer with a detected species and timestamp, switching the buzzer on for immediate notification, and showing the detection details on the LCD display. When nighttime falls, LED floodlights automatically turn on to improve visibility and keep nighttime wildlife away. Continuous improvement of the YOLOv8 model will enable it to recognize a wide range of species, and with changing environmental conditions, update its model accordingly.

AI in Agriculture, YOLO V8, Animal Detection, Real-Time Surveillance

1. P., Devi, R., , S., & , P. (2020). IoT BASED CROP PROTECTION SYSTEM AGAINST BIRDS AND WILD ANIMAL ATTACKS. , 6, 138-143. [Google Scholar] [Crossref]

2. Krishna, K., , S., Mukesh, K., Harshavardhan, M., Chandra, D., Chandra, D., & Roy, A. (2025). Solar based Smart Cropping Defence System using IoT based on Birds and Animals. 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), 965-972. [Google Scholar] [Crossref]

3. K, S., S, S., S, S., & S, S. (2025). Intelligent IoT System for Animal Detection and Crop Defense Using Acoustic and Visual Repellents. 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), 1-5. [Google Scholar] [Crossref]

4. Shirsat,, Y. (2025). Smart Farmland for Crop Prevention & Animal Intrusion Detection Using IOT. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. [Google Scholar] [Crossref]

5. D, A., & C, B. (2025). IoT based Bird Repelling System. 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 1-3. [Google Scholar] [Crossref]

6. Prabu, J., Kumar, G., Rajasoundaran, S., Malla, P., Routray, S., & Mukherjee, A. (2021). Internet of things‐based deeply proficient monitoring and protection system for crop field. Expert Systems, 39. [Google Scholar] [Crossref]

7. Sudheer, C., , K., , M., Nath, G., Javeed, P., & Kumar, B. (2023). CROP PROTECTION AND MONITORING FROM ANIMALS ATTACKS BY USING IOT SOLUTIONS. [Google Scholar] [Crossref]

8. Sathe, J., Sarkar,, A., Urade, A., & Somkuwar, R. (2025). Solar and IOT-Driven Bird, Animal and Insect Repellent Using Radar Technology. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT [Google Scholar] [Crossref]

9. M., R., N., G., R., S., M., P., & M., R. (2025). Intelligent Motion based Animal Repulsion System for Agriculture. Journal of Artificial Intelligence and Capsule Networks. [Google Scholar] [Crossref]

10. Bapat, V., Kale, P., Shinde, V., Neha, D., & Shaligram, A. (2017). WSN application for crop protection to divert animal intrusions in the agricultural land. Comput. Electron. Agric., 133, 88-96. [Google Scholar] [Crossref]

11. Deotale, P., & Lokulwar, P. (2021). Smart Crop Protection System from Wild Animals Using IoT. 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 1-4. [Google Scholar] [Crossref]

12. Kamanuru, N., , E., , G., & , I. (2024). SMART IRRIGATION AND CROP PROTECTION FROM WILD ANIMALS USING IOT. Journal of Nonlinear Analysis and Optimization. [Google Scholar] [Crossref]

13. Miao, J., Rajasekhar, D., Mishra, S., Nayak, S., & Yadav, R. (2024). A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge. Future Internet, 16, 296. [Google Scholar] [Crossref]

14. Dhital, P. (2025). Smart Farming for Farm Security: Mitigate Wild-Birds Intrusion in Agriculture Farms. OCEM Journal of Management, Technology & Social Sciences. [Google Scholar] [Crossref]

15. Venkateswarlu, K., , R., Reddy, B., Goutham, V., Member, I., Palanisamy, G., Golla, M., Purushothaman, S., Reddy, K., Ramkumar, V., & Goutham, V. (2024). Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats. IEEE Access, 12, 77707-77723. [Google Scholar] [Crossref]

16. Prasath, H., Suji, N., Jeykrishnan, M., Thiagarajan, G., & Janani, R. (2025). AI-Powered IoT-Based Wildlife Threat Detection and Emergency Communication System. International Research Journal on Advanced Engineering Hub (IRJAEH). [Google Scholar] [Crossref]

17. K, H., & K, A. (2025). Protection of Crops from the Intrusion of Wild and Domestic Animals Using Deep Learning and IOT. 2025 International Conference on Computing and Communication Technologies (ICCCT), 1-6. [Google Scholar] [Crossref]

18. Giordano, S., Seitanidis, I., Ojo, M., Adami, D., & Vignoli, F. (2018). IoT solutions for crop protection against wild animal attacks. 2018 IEEE International Conference on Environmental Engineering (EE), 1-5 [Google Scholar] [Crossref]

19. Girish, D., Rohith, S., Poojitha, J., Varsha, S., Krishna, B., & Sreenivasulu, K. (2025). AI Based Smart Crop Monitoring for Animal Intrusions Detection. 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS), [Google Scholar] [Crossref]

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