Implementation of yolov8 nano in an iot-based oyster mushroom cultivation monitoring system

Abstract

Oyster mushrooms are one of the agricultural commodities with high economic value and are widely cultivated in Indonesia. However, the conventional process of monitoring their growth is still carried out manually, which requires considerable time and labor while also being prone to errors in decision-making. To address this issue, this study developed an automatic oyster mushroom growth monitoring system using Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The system uses a DHT22 sensor to measure temperature and humidity, a BH1750 sensor to measure light intensity, and an ESP32-CAM module to capture mushroom images. The data is transmitted through the ESP32 and analyzed using Python, while the images are processed by a YOLOv8 Nano model to classify mushroom growth stages into baglog, young mushrooms, and ready-to-harvest mushrooms. The monitoring results are displayed in real time on a dashboard and stored in a MySQL database. The model training results show fairly good performance, with an average precision of 0.69, recall of 0.78, and a mean Average Precision (mAP@0.5) of 0.71. Further testing was conducted on 15 test images for each mushroom stage, and all images were successfully detected according to their actual classes. Additionally, tests conducted on 10 negative images (without mushroom objects) also supported the system’s reliability. The success of the system is further supported by stable network connectivity for data transmission, adequate lighting in the cultivation room during image capture, and automatic adjustment of temperature and humidity according to the mushroom growth phase. This system demonstrates its capability to monitor mushroom growth conditions automatically and accurately, offering a practical solution for supporting more modern and efficient mushroom cultivation practices.