MycoTrack: An Integrated Web and YOLOv5-Based Intelligent System for Monitoring and Predicting Wood Ear Mushroom Maturity - Wuliddah Tamsil Barokah; Dwi Putra Kunto Anggoro; Nabil Kurnia Rozano; Ariel Mughnika Beers; Inna Novianty; Dodik Ariyanto; Lathifunnisa Fathonah (Journal of Applied Science, Technology & Humanities | JASTH, 2026)
Wood ear mushroom (Auricularia auricula-judae) cultivation requires strict environmental control and accurate harvest monitoring. To overcome the shortcomings of labor-intensive and error-prone manual inspection, this research developed MycoTrack, an intelligent system integrating rail-based robotics, YOLOv5 computer vision, and IoT sensors. MycoTrack utilizes a rail-based robot powered by a Raspberry Pi 4. The robot carries a Pi Camera for visual data acquisition and DHT-22 sensors to measure environmental temperature and humidity. This environmental data is continuously monitored and transmitted to a web-based dashboard for real-time visualization, providing instantaneous decision support to farmers. The YOLOv5 model is specifically trained to detect three critical growth phases—incubation, pinning, and fruiting—which enables the prediction of optimal harvest timing. System validation showed DHT-22 sensor accuracy of 96.4% and the YOLOv5 model achieved a mAP@50 of 0.782 with inference speeds suitable for edge devices. The rail robot demonstrated minimal positional deviation (less than 2.3 cm). MycoTrack offers an accessible, automated solution, representing an advancement in precision agriculture for mushroom cultivation. The system is modularly designed for easy adaptation to other mushroom environments and species.