A Fast and Real‐Time Machine Vision Evaluation System for Size Grading of <i>Agaricus bisporus</i> Based on Video

Automatic grading in factory production is a key component for improving production efficiency and ensuring product quality. As one of the most widely consumed edible mushrooms in the world, the cap diameter of <i>Agaricus bisporus</i> is a key factor affecting the market selling price. In addition to the high labor cost, manual grading is subjective and prone to human assessment errors. Therefore, this study proposes a video-based fast real-time machine vision evaluation system for size grading of <i>Agaricus bisporus</i> mushrooms. The designed grading system consists of a conveyor belt to achieve transportation of <i>Agaricus bisporus</i> and an overhead color camera to perform size quality assessment. Considering deployment on edge devices, we have optimized the YOLOv5 model for lightweight performance by adopting ShuffleNetV2 as the backbone network, significantly improving the model's lightweight performance and processing speed. At the same time, we investigated the balance between transport speed and recognition accuracy to ensure that the system maintains a high recognition rate while producing efficiently. The results show that the size of the improved model is reduced by 86.1%, FPS is increased by 9.8%, and mAP is 97.1%. The experimental field results show that the grading can still be performed at a high speed with a transmission speed of 130.17 mm/s. The grading speed can reach 1066.67 mushrooms/min with an accuracy of 97.87%, and it also has strong adaptability and stability in low resolution and low light conditions. The video-based model can detect <i>Agaricus bisporus</i> mushrooms that are always in motion, which is more suitable for practical detection than a single image-based detection model.