Advances in Utilization of Agricultural Waste Based on Production of Edible Fungi

Along with the rapid development of edible fungus industry in China,the traditional mode of production giving priority to wood chips will be severely limited,and using agricultural waste distributed widely,having large yield,and containing high content of organic matter to produce edible fungi has good economic and ecological benefit. In this paper,based on the analysis of characteristics of agricultural waste in China,the present situation of application of agricultural waste in the production of edible fungi at home and abroad was introduced,and the main problems existing in production of edible fungi by using agricultural waste in China at the present stage were pointed out. Finally,the development direction of using agricultural waste to cultivate edible fungi was discussed,and some suggestions were put forward,such as improving the theoretical system for using agricultural waste to produce edible fungi,and establishing the standardized technical system for using agricultural waste to produce edible fungi.

Spatial-channel transformer network based on mask-RCNN for efficient mushroom instance segmentation

Edible mushrooms are rich in nutrients;however,harvesting mainly relies on manual labor.Coarse localization of each mushroom is necessary to enable a robotic arm to accurately pick edible mushrooms.Previous studies used detection algorithms that did not consider mushroom pixel-level information.When these algorithms are combined with a depth map,the information is lost.Moreover,in instance segmentation algorithms,convolutional neural network(CNN)-based methods are lightweight,and the extracted features are not correlated.To guarantee real-time location detection and improve the accuracy of mushroom segmentation,this study proposed a new spatial-channel transformer network model based on Mask-CNN(SCT-Mask-RCNN).The fusion of Mask-RCNN with the self-attention mechanism extracts the global correlation outcomes of image features from the channel and spatial dimensions.Subsequently,Mask-RCNN was used to maintain a lightweight structure and extract local features using a spatial pooling pyramidal structure to achieve multiscale local feature fusion and improve detection accuracy.The results showed that the SCT-Mask-RCNN method achieved a segmentation accuracy of 0.750 on segm_Precision_mAP and detection accuracy of 0.638 on Bbox_Precision_mAP.Compared to existing methods,the proposed method improved the accuracy of the evaluation metrics Bbox_Precision_mAP and segm_Precision_mAP by over 2%and 5%,respectively.