Smart Ricefield: Development of an Automated Bird Pest Repellent System in Rice Fields Based on IOT and Artificial Intelligence
Keywords:
Smart Ricefield, Bird Pest Repellent, Internet of Things (IoT), YOLO11m Object Detection, Artificial IntelligenceAbstract
Bird pest attacks are one of the main causes of declining rice productivity in Deli Serdang Regency, especially during the grain ripening phase. This study develops the smartRicefield innovation, namely an automated bird pest repellent system based on the Internet of Things (IoT) and Artificial Intelligence (AI) using the YOLO11m image detection model. This study begins with collecting bird image datasets in rice fields collected from field image capture or dare sources, then labeled using the YOLO format, and wrapped with augmentation techniques to increase shape diversity. The YOLO11m model consisting of 125 image layers and 20,030,803 parameters with a complexity of 67.6 GFLOPs drilled for the next 100 epochs. The best model in the 86th epoch achieved 100% precision, 83.2% recall, mAP@0.5 of 86.3%, and mAP@0.5–0.95 of 69.3%. The Confusion Matrix Analysis showed good bird detection performance, but a high false positive rate in the background of the trigger image caused false triggers in the object testing. The system was tested in Deli Serdang rice fields with a detection latency of less than 1 second and an expulsion effectiveness of 90% at an effective distance of 10 meters. These results indicate that the integration of AI and IoT in Smart Ricefield is able to provide an effective real-time solution for bird pest mitigation, although improvements are still needed in dataset variations and expulsion mechanisms to increase the system's long-term resilience to all types of bird pests in the rice field environment.
References
Afif, M. H., Sanjaya, R., Sauri, S., & Prasetyo, S. M. (2023). Sistem perangkat pengusir hama burung emprit atau pipit berbasis sensor PIR dan IoT. LOGIC: Jurnal Ilmu Komputer dan Pendidikan, 1(3), 496–503.
Alwi, A. S., Pratama, R. A., Ikawanty, B. A., & Budi, E. S. (2023). Implementasi sistem pengusir hama burung berbasis Arduino untuk optimalisasi pertanian: Kajian monitoring. Journal of Applied Smart Electrical Network and Systems (JASENS).
Andi Taufiq, A. T. A., Latief Arda, A., & Taufiq, I. (2022). Alat pengusir burung pada tanaman padi berbasis IoT. Jurnal Ilmiah Ilmu Komputer, 8(2), 101–107. https://doi.org/10.35329/jiik.v8i2.234
Chen, L., Zhang, Y., & Li, H. (2022). AIoT-enabled smart rice farming system: Design and field experiments. Computers and Electronics in Agriculture, 196, 106878.
Firgiawan, G., Seina, N. L., & Rosyani, P. (2024). Implementasi metode You Only Look Once (YOLO) untuk pendeteksi objek dengan tools OpenCV. Jurnal Artificial Intelligent dan Sistem, 2(2), 137–141.
Haq Al Rasyid, A., & Wardani, A. L. (2024). Rancang bangun pengusir hama burung dan belalang pada padi menggunakan gelombang ultrasonik berbasis LoRa. ELPOSYS: Jurnal Sistem Kelistrikan, 11(3).
Hidayatullah, D., & Sulistiyanto, S. (2022). Perancang alat pengusir hama burung pipit pada tanaman padi menggunakan gelombang kejut otomatis berbasis Internet of Things (IoT). JEECOM Journal of Electrical Engineering and Computer, 4(2), 74–78. https://doi.org/10.33650/jeecom.v4i2.4464
Kumar, A., Patel, R., & Singh, M. (2021). IoT-based smart agriculture: A review on applications, technologies, and challenges. Journal of Agricultural Informatics, 12(2), 1–12.
Lokhande, H., & Ganorkar, S. R. (2023). Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices. Bulletin of Electrical Engineering and Informatics, XX(XX).
Luo, H., Wei, J., Wang, Y., Chen, J., & Li, W. (2024). An improved lightweight object detection algorithm for YOLOv5. PeerJ Computer Science. https://peerj.com/computer-science/
Martikha, K. G., Hermawan, A. C., Ariwibowo, W., & Rahmadian, R. (2022). Rancang bangun alat pengusir hama tenaga surya menggunakan sinar ultraviolet dan suara pada pertanian padi. Jurnal Teknik Elektro Vokasi Universitas Negeri Surabaya, 73–78.
More, B., & Bhosale, S. (2023). A comprehensive survey on object detection using deep learning. Revue d’Intelligence Artificielle, 37(2), 407–414.
Namana, M. S. K., & Kumar, B. U. (2024). An efficient and robust night-time surveillance object detection system using YOLOv8 and high-performance computing. International Journal of Safety and Security Engineering, 14(6), 1763–1773.
Noor, I. M., Fitriyah, H., & Maulana, R. (2019). Sistem pengusir hama burung pada sawah dengan menggunakan sensor PIR dan metode Naïve Bayes. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(9), 9328–9333. http://j-ptiik.ub.ac.id
Rahman, M., Ahmed, T., & Chowdhury, S. (2022). Automated bird repelling system using computer vision and sound actuation for rice fields. International Journal of Smart Agriculture, 5(1), 45–53.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779–788).
Saputra, D. H., Imran, B., & Rekayasa Sistem Komputer Universitas Teknologi Mataram. (2023). Object detection untuk mendeteksi citra buah-buahan. Jurnal Teknologi Informatika, 2(2), 70–80.
Sufaat, I., & Juliandri, J. (2024). Rancang bangun alat pengusir hama burung pada padi sawah petani berbasis Internet of Things (IoT). Journal of Computer System and Informatics (JoSYC), 5(2), 306–314. https://doi.org/10.47065/josyc.v5i2.4921
Virgiawan, I., Maulana, F., Putra, M. A., Kurnia, D. D., & Sinduningrum, E. (2024). Deteksi dan tracking objek secara real-time berbasis computer vision menggunakan metode YOLO v3. Jurnal Ilmiah Multidisiplin Indonesia, 3(3). https://journal.ikopin.ac.id/index.php/humantech
Zophie, J., & Triharminto, H. H. (2020). Implementasi algoritma You Only Look Once (YOLO) menggunakan web camera untuk mendeteksi objek statis dan dinamis. Jurnal Teknologi Informatika, 1(1), 98–109.
