Monitoring Mushroom Growth with Machine Learning

Mushrooms contain valuable nutrients, proteins, minerals, and vitamins, it is suggested to include them in our diet. Many farmers grow mushrooms restricted environments with specific atmospheric parameters greenhouses. In addition, recent technologies of the Internet things intend give solutions agriculture area. this paper, we evaluate effectiveness machine learning for mushroom growth monitoring genus Pleurotus. We use YOLOv5 detect mushrooms’ growing stage indicate those ready harvest. The results show that can greenhouse an F1-score up 76.5%. classification final gives accuracy 70%, which acceptable considering complexity photos used. propose a method based on Detectron2. Our shows average period 5.22 days. Moreover, also adequate harvesting day. evaluation could improve time harvest 14.04% mushrooms.