MycoTrack: An Integrated Web and YOLOv5-Based Intelligent System for Monitoring and Predicting Wood Ear Mushroom Maturity
DOI:
https://doi.org/10.62535/kfkect89Keywords:
precision agriculture, computer vision, YOLOv5, Internet of Things, mushroom cultivation, automated monitoring, Raspberry PiAbstract
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.
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Baranwal, T., Nitika, Pateriya, P. K., & Sahu, R. (2016). Tantangan pengembangan robot cerdas berbasis IoT untuk sektor pertanian.
International Journal of Engineering and Advanced Technology, 6(2), 23-29.
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Adewusi, A. O., Ibrahim, S. A., & Oladele, O. I. (2024). Memanfaatkan data science untuk pertanian berkelanjutan: Tinjauan komprehensif precision agriculture. International Journal of Science and Research Archive, 11(2), 1256-1278. https://doi.org/10.30574/ijsra.2024.11.2.1256
Ullah, I., Youn, H. Y., & Han, Y. H. (2025). Integrasi data science dengan IoT cerdas (IIoT): Tantangan dan perspektif masa depan. Digital Communications and Networks, 11(2), 358-387. https://doi.org/10.1016/j.dcan.2024.02.003
Shilpashree, S., Raghavendra, B. K., & Kumar, A. (2025). Pertanian cerdas berbasis IoT: Meningkatkan budidaya jamur dengan deteksi penyakit terintegrasi. International Journal of Science and Research Archive, 16(3), 396-401. https://doi.org/10.30574/ijsra.2025.16.3.2565
Yulizar, D., Wibowo, S. B., & Nugroho, A. (2023). Analisis perbandingan performa sensor suhu DHT11, DHT22 dan DS18B20. Journal of Physics: Conference Series, 2622(1), Article 012041. https://doi.org/10.1088/1742-6596/2622/1/012041
Lee, J., Kim, H., & Park, S. (2022). YOLO dengan adaptive frame control untuk real-time object detection pada embedded systems terbatas resources. Multimedia Tools and Applications, 81(15), 21375-21396. https://doi.org/10.1007/s11042-021-11480-0
Haq, M. A., Ahmed, A., Khan, I., Gyani, J., Mohamed, A., Attia, E. A., & Mangan, P. (2024). Meningkatkan performa deteksi objek YOLO pada komputer single-board melalui teknik optimasi. Engineering, Technology & Applied Science Research, 14(3), 14208-14215. https://doi.org/10.48084/etasr.7213
Charisis, C., Avgoustaki, D. D., & Xydis, G. (2025). Pipeline tracking berbasis Mask R-CNN novel untuk monitoring pertumbuhan kluster jamur tiram. Computers and Electronics in Agriculture, 231, Article 109523. https://doi.org/10.1016/j.compag.2025.109523
Zhang, D. Y., Luo, H. S., Wang, D. J., Zhou, X. G., Li, W. F., Gu, C. Y., Zhang, G., & He, F. (2023). Deteksi spora fungi scab gandum menggunakan algoritma Yolov5-ECA-ASFF. Computers and Electronics in Agriculture, 210, Article 107896. https://doi.org/10.1016/j.compag.2023.107896
Rakesh, M. D., Kumar, S. P., & Shivakumar, B. L. (2025). Implementasi sistem klasifikasi daun tanaman akar real time menggunakan deep learning teroptimasi pada Raspberry Pi 4B. Smart Agricultural Technology, 7, Article 100425. https://doi.org/10.1016/j.atech.2024.100425
