Abstract: Accurate detection of pleurotus ostreatus is highly required in modern mushroom houses, particularly with the ever-increasing automation of intelligence and informatization. However, manual harvesting cannot fully meet the large-scale factory cultivation in the edible mushroom industry. Fortunately, a harvesting robot can be expected to increase productivity with the reduced labor intensity at the industrial cultivation and harvesting stage. In this article, an improved OMM-YOLO(ostreatus measure modle-you only look once) target detection model was proposed to conduct comparative experiments on the mushroom detection dataset. 1353 images of mushrooms were collected from the mushroom houses and then classified into the mature, immature, and ungrown at the growth stages. An OMM-YOLO target detection and classification model was proposed using the improved YOLOv5 model. The ECA (effective channel attention) attention module was also introduced into the Backbone layer of the original YOLOv5 model. The features of input mushroom images were dynamically weighted to obtain more feature information. At the same time, a weighted bidirectional feature pyramid network (BiFPN) was used in the Neck layer. Multi-scale features were repeatedly fused from the top to the bottom and then bottom-up, thereby improving the accuracy of mushroom target detection. In addition, the EIoU (enhanced intersection over union) Loss function was utilized to improve the accuracy of the model and the rate of convergence of the bounding box aspect ratio, instead of the original Loss function CIoU (Complete IoU). The better performance was achieved in terms of the average accuracy mAP, and the rate of convergence of the bounding box aspect ratio. The experimental results show that the improved OMM-YOLO model shared an average accuracy mAP of 91.4%, 87.1%, and 95.1% for the mature, immature, and ungrown mushrooms, respectively, which was improved by 0.4, 4.5, and 1.1 percentage points, respectively, compared with the original. Better performance was achieved in the improved OMM-YOLO model, in terms of the accuracy, recall, detection accuracy, and detection speed, compared with the current mainstream models, such as Resnet50, VGG16, YOLOv3, YOLOv4, YOLOv5m, and YOLOv7. Therefore, this improved model was very suitable for the detection of mushrooms in modern mushroom houses. Mushroom features were collected to effectively avoid the occurrence of false detection caused by the mutual occlusion of mushrooms. An attention mechanism was introduced to weigh the BiFPN using a more appropriate Loss function. The accuracy and rate of convergence of the improved model were significantly enhanced for the mushroom target detection. A more effective technical means can be expected in the detection of mushroom targets in modern mushroom houses at present. As such, mushroom target detection can automatically detect the quantity and growth condition of the mushroom in the mushroom room. Environmental parameters (such as temperature and humidity) can be adjusted in a timely manner. The production efficiency can be promoted to control the quality of the mushroom for better uniformity and quality stability of the products. At the same time, it can be used to reduce the dependence on labor and costs. Sustainable development can be realized through the positive impact of the construction of intelligent mushroom houses.