<![CDATA[Purpose - This study develops and evaluates an Internet of Things (IoT)-based monitoring system for automatically identifying oil palm harvest readiness through loose fruit detection using the YOLOv8 algorithm. The system addresses the subjectivity, labor intensity, and inefficiency of conventional manual observation in large-scale plantations.Methods - An experimental design was applied using 2,000 images of oil palm loose fruits collected from the Indonesian Institute of Palm Oil Technology, North Sumatra. Images were captured using an ESP32-CAM, annotated through Roboflow, and used to train the YOLOv8x model for 120 epochs at a resolution of 640 × 640 pixels.Findings - The prototype performed image acquisition, loose fruit detection, and dashboard-based monitoring in near real time. On the independent testing dataset, the model achieved a precision of 0.933, recall of 0.954, F1-score of 0.944, mAP@0.5 of 0.943, and mAP@0.5:0.95 of 0.495. These results demonstrate the system’s feasibility as a prototype for supporting harvest-readiness monitoring, although broader field validation is still required.Research Implications - The dataset was obtained from a single plantation site and may not represent highly variable environmental conditions. Future studies should use larger and more diverse datasets and assess cloud-based deployment for improved scalability.Originality - This study integrates ESP32-CAM, YOLOv8x, IoT communication, and web-based monitoring into a prototype architecture for data-driven oil palm harvest-readiness assessment through loose fruit detection.]]>
