RECOGNITION OF AGARICUS BISPORUS BASED ON IMPROVED MASK-RCNN MODEL

The recognition of Agaricus bisporus is a key step in the intelligent picking of Agaricus bisporus. Given the complex background and limited computing resources of edge devices in actual planting scenarios, an improved Mask-RCNN model for Agaricus bisporus recognition was proposed. In this method, the backbone feature extraction network of the baseline Mask-RCNN model was replaced with the lightweight MobileNetV3 network to reduce the model complexity. Meanwhile, the BiFPN network was used to replace the original FPN feature fusion network, thereby strengthening feature fusion and enhancing the model's ability to learn image features and acquire contextual information. Experimental results showed that the improved Mask-RCNN model’s parameters and floating-point operations were 24.46 M and 173 G, respectively, which were 44.4% and 24.5% lower than those of the baseline Mask-RCNN model, and the frame rate increased by 3.55 FPS, indicating a better prospect for deployment on edge devices. This method can provide technical support for the development of the visual system of Agaricus bisporus picking robots.