Abstract: Lentinus edodes can benefit from artificial intelligence (AI) and internet of things (IoT) technologies in indoor farming. The mushroom growth can be cultivated by hand-free robots in greenhouses. It is necessary to evaluate the growth status in such next-generation scenarios of agricultural production. The cultivation environment can be regulated to take the optimal management. Since the fruiting bodies of Lentinus edodes cannot change significantly in the growth period, some mature ones cannot be detected by machine vision in the automatic machine harvesting. Specifically, Lentinus edodes bodies are distributed randomly and crowded. Moreover, the target detection of such mushrooms is also interfered from the image background in the texture of nutrient sticks on planting stands. In this study, a rapid and accurate detection was proposed to identify the growth period of Lentinus edodes using improved YOLOv5l. Firstly, the lightweight upsampling prediction and feature reorganization modules were replaced in the YOLOv5l model. Secondly, the detection layer of small target was added into the YOLOv5l model. The characteristic information was enhanced to distinguish the growth period and small of fruiting Lentinus edodes. Finally, the experimental results show that the better detection was achieved in the improved YOLOv5l model. A data set was collected using edge devices with inexpensive camera probes and an IoT platform. 3470 pictures were selected to train the improved model. The average precision rate of the proposed algorithm can be 92.70%. The improved YOLOv5l model was 0.6 percentages higher than the highest average accuracy of mushroom recognition in the shape stage, 2.9 percentages higher than the highest average accuracy of mushroom recognition in the mature stage, 0.2 percentages higher than the highest average precision rate, and 0.2 percentages higher than the highest average recall rate, compared with the original. The rate also increased by 1.7 percentage points. However, the average frame rate of the improved YOLOv5l model decreased to 45.25 frames/s, compared with the original (57.47 frames/s). This improved model can meet the accuracy and speed requirements for the identification of different growth stages of Lentinus edodes fruiting bodies. Real-time growth assessment can be used to predict the mushroom yield in intelligent production. The existing data set of the growth period of mushroom fruiting bodies should be expanded to construct the national standards for mushroom classification at the mature grades. Maturity identification and yield prediction are planned to further explore the collaborative control of environmental facilities, robot picking, and harvest logistics.