Citation :

Ahmad Taufiq Iqbal Khalid, Muhammad Syakir Anwar Azahari, Ili Najaa Aimi Mohd Nordin, Ahmad ‘Athif Mohd Faudzi, Nurulaqilla Khamis, Amar Faiz Zainal Abidin, "Edge-Based IoT Pest Monitoring System for Chili Farms Using YOLOv8 and Real-Time Alert Notifications," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 185-194, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P112

Abstract

Infestation by pests is a key issue in growing Chili. It is prone to lead to low yields and increased pesticide needs. Visual inspections are the foundation of conventional monitoring methods, which are labor-intensive and might cause delays along the way. This research suggests a pest detection system utilizing IoT based on image acquisition on sticky traps combined with the YOLOv8 object detection algorithm and Telegram notifications. The model was also trained for whitefly and fruit fly detection. It achieved a maximum mAP@0.5 of 0.45 and mAP@0.5:0.95 of 0.15 to 0.25. While these are modest numbers, they are the first to show the presence of the pests. Notification tests show that the alerts are usually provided within one minute of capturing photos. Variations in detection counts were detected due to changes in light conditions throughout the day and slight position adjustments of the camera frames. These results indicate that the practical use of this pest detection method has the potential to lessen dependence on manual inspection and provide assistive capabilities to manage pests on a timely basis. Areas of improvement have also been identified, such as enhancing dataset diversity, image stability, and system reliability.

Keywords

Chili Farming, Edge-Based Pest Monitoring, IoT-Based Pest Detection, Precision Agriculture, Real-Time Alert Notifications, YOLOv8 Object Detection.

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