Implementation of YOLOv4 and Laser Sensors for Brazil Vineyard Security
DOI:
https://doi.org/10.31848/justise.v4i1.4318Keywords:
YOLOv4, Laser sensors, IoT, Availability, Agricultural securityAbstract
Agricultural security is crucial for maintaining productivity and business sustainability, particularly for high-value commodities such as grapes. The threat of theft and disturbance by wild animals, such as wild boar, often causes significant losses in vineyards. This study proposes an intelligent surveillance system integrating laser sensors with the YOLOv4 (You Only Look Once) algorithm to enhance agricultural security through automated, real-time detection. The laser sensor functions as an early motion-detection mechanism, while the ESP32-CAM module captures visual imagery for subsequent analysis using the YOLOv4 model to achieve precise object classification. The system also incorporates Internet of Things (IoT) connectivity via the Blynk platform for real-time remote monitoring. Test results show that the system achieves 85% detection accuracy during daytime and 78% at night, with an average response time of one second from detection to notification.
References
R. Rahmadian and M. Widyartono, “Penerapan Machine Vision untuk Sistem Panen Otomatis di Robot Agrikultur,” Journal of Information Engineering and Educational Technology, vol. 4, no. 1, pp. 47–52, Jun. 2020, doi: 10.26740/jieet.v4n1.p47-52.
A. JOSHI, N. PANDEY, M. DIWAKAR, P. SINGH, A. SHANKAR, and F. ALQAHTANI, “WBD-YOLO-AM: YOLOv8 with attention module and IoT-based wild boar detection and deterrence system for safeguarding small millets,” Turkish Journal of Agriculture and Forestry, vol. 49, no. 4, pp. 769–786, Aug. 2025, doi: 10.55730/1300-011X.3303.
E. A. Aldakheel, M. Zakariah, and A. H. Alabdalall, “Detection and identification of plant leaf diseases using YOLOv4,” Front. Plant Sci., vol. 15, Apr. 2024, doi: 10.3389/fpls.2024.1355941.
Z. He, S. R. Khana, X. Zhang, M. Karkee, and Q. Zhang, “Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment,” ArXiv, vol. abs/2308.03998, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:260704632
Riyadhul Fajri, Z. Zulkifli, and D. Prananda, “Theft Detection System Using Laser Based on Arduino Uno,” Sisfo: Jurnal Ilmiah Sistem Informasi, vol. 10, no. 1, pp. 120–124, May 2026, doi: 10.29103/sisfo.v10i1.26938.
Sujan Banerjee, Priyanka Dutta, Sudip Kr. Bid, Debabrata Sarkhel, and Kailash Bauri, “Low-Cost Laser Security System with Intrusion Detection and Alert Mechanism,” International Journal of Current Research and Techniques, vol. 14, no. 4, pp. 50241–50245, Nov. 2024, doi: 10.61359/2024050027.
J. Nannung, A. Sa’ban Miru, Muliadi, and Gunawan, “Development of an IoT-Based Home Security System Prototype Using Multisensors and ESP32-CAM,” Journal of Embedded Systems, Security and Intelligent Systems, pp. 168–177, Jun. 2025, doi: 10.59562/jessi.v6i2.8456.
B. Das et al., “Designing and development of agricultural rovers for vegetable harvesting and soil analysis,” PLoS One, vol. 19, no. 6, p. e0304657, Jun. 2024, doi: 10.1371/journal.pone.0304657.
A. Fikri, “Simple IoT-Based Home Security System Using ESP32 and Blynk,” Data Science: Journal of Computing and Applied Informatics, vol. 9, no. 2, Aug. 2025, doi: 10.32734/jocai.v9.i2-22595.
A. P. Muksin and B. Tjahjono, “IoT-Based Remote Electricity Control and Management Monitoring System Using Blynk Application,” Global Insights in Management and Economic Research, vol. 1, no. 02, pp. 65–70, Jun. 2025, doi: 10.53905/Gimer.v1i02.11.
K. Noh, S. Ki Hong, S. Makonin, and Y. Lee, “Enhancing Object Detection in Dense Images: Adjustable Non-Maximum Suppression for Single-Class Detection,” IEEE Access, vol. 12, pp. 130253–130263, 2024, doi: 10.1109/ACCESS.2024.3459629.
M. Sozzi, S. Cantalamessa, A. Cogato, A. Kayad, and F. Marinello, “22. Grape yield spatial variability assessment using YOLOv4 object detection algorithm,” in Precision agriculture ’21, Brill | Wageningen Academic, 2021, pp. 193–198. doi: 10.3920/978-90-8686-916-9_22.
P. Shevantikar, A. Udata, and A. Korachagao, “Laser-Enabled Intruder Detection System,” Journal of Electrical Engineering and Electronics Design, vol. 2, no. 1, pp. 1–4, Jan. 2024, doi: 10.48001/joeeed.2024.211-4.
Q. H. Hidayah, R. P. Laksana, A. Prabowo, and D. S. Simatupang, “IoT-Based Home Security System: ESP32-CAM Integration and Real-Time Notification,” International Transactions on Education Technology (ITEE), vol. 4, no. 2, pp. 149–161, May 2026, doi: 10.33050/itee.v4i2.956.
F. Humam and M. A. Triawan, “Sistem Keamanan Ruangan Menggunakan ESP32CAM dan Sensor Gerak Berbasis IoT,” Infotek: Jurnal Informatika dan Teknologi, vol. 7, no. 2, pp. 575–584, Jul. 2024, doi: 10.29408/jit.v7i2.26109.
I. M. Abdulkareem, F. K. AL-Shammri, N. A. A. Khalid, and N. A. Omran, “Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation,” International Journal of Online and Biomedical Engineering (iJOE), vol. 20, no. 05, pp. 31–43, Mar. 2024, doi: 10.3991/ijoe.v20i05.47171.
W. Zeng, “Image data augmentation techniques based on deep learning: A survey,” Mathematical Biosciences and Engineering, vol. 21, no. 6, pp. 6190–6224, 2024, doi: 10.3934/mbe.2024272.
T. Chen, H. Zheng, J. Chen, Z. Zhang, and X. Huang, “Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring,” Comput. Electron. Agric., vol. 219, p. 108807, Apr. 2024, doi: 10.1016/j.compag.2024.108807.
A. Arifin and M. Rizal, “Implementasi Sistem Otomatisasi Perawatan Tanaman indoor berbasis Internet of Things (IoT),” remik, vol. 7, no. 2, pp. 935–945, Apr. 2023, doi: 10.33395/remik.v7i2.12277.
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-NMS — Improving Object Detection with One Line of Code,” 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5562–5570, 2017, [Online]. Available: https://api.semanticscholar.org/CorpusID:15155826
J. A. J. Alsayaydeh, M. F. bin Yusof, C. K. Hern, M. R. AHMAD, V. Shkarupylo, and S. G. Herawan, “Greenhouse Horticulture Automation with Crops Protection by using Arduino,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 10, 2023, doi: 10.14569/IJACSA.2023.0141013.
M. S. Mahmud, A. Zahid, and A. K. Das, “Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects,” Sensors, vol. 23, no. 4, p. 1818, Feb. 2023, doi: 10.3390/s23041818.
P. Palkar, “IoT-Based Home Monitoring System Using ESP32CAM Surveillance,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 13, no. 4, pp. 4378–4383, Apr. 2025, doi: 10.22214/ijraset.2025.69014.
K. Kanna S, K. Ramalingam, P. P, J. R, and P. P.C., “YOLO deep learning algorithm for object detection in agriculture: a review,” Journal of Agricultural Engineering, vol. 55, no. 4, Dec. 2024, doi: 10.4081/jae.2024.1641.
J. J. Choong and K. S. Chia, “IoT-Based Industrial Wastewater Monitoring System using ESP32 and Blynk,” Journal of Engineering Technology and Applied Physics, vol. 8, no. 1, p. 106, Mar. 2026, doi: 10.33093/jetap.2026.8.1.15.
A. Chakraborty, A. Mallick, P. Saha, S. R. K. Vadali, S. Aruchamy, and V. S. Nageli, “Design and IoT Implementation of a Long Range laser Based Intrusion Detection System for Perimeter Surveillance,” in 2022 International Interdisciplinary Conference on Mathematics, Engineering and Science (MESIICON), IEEE, Nov. 2022, pp. 1–6. doi: 10.1109/MESIICON55227.2022.10093464.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rizky Muharam, Yasri, Ocsar Hadikaryana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
