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As one of the most common fungal food, Flammulina Velutipes (FV) is an important source of food in China. At the same time, the popularization of industrialization has greatly improved the yield of FV. However, in its industrialized production, there are still many artificial factors in the selection and classification of the FV. It will bring about mistakes after the workers’ long hours working, which will increase the classification error rate, low production efficiency, thus resulting in low production of FV and damage to the factory interests. In order to solve these problems, we use machine instead of workers to complete the classification of FV via its heads by adopting the currently popular Deep Learning (DL) of computers. And the corresponding methods are as follows: (1) Collect the data of FV heads and then make a dataset according to the classification standard proposed by the "FV Factory" in this paper. (2) Preprocess the image, augment and normalize the dataset. (3) Retrain dataset of the FV heads respectively in the following three convolution neural network models as in Alexnet, Vgg-16, Resnet-50 as well as an improved Resnet-50 one by using the Transfer Learning method. (4) By analyzing and comparing the three network training models, this paper comes to a conclusion that the results obtained by Data Augmentation in the improved Resnet-50 model with a test accuracy of 79.9%, are superior to that of the other neural networks.

Supercapacitors possess an essential application in storing intermittent energy and powering electric vehicles or wearable electronics. It should be a promising approach by combining carbon nanomaterials with metal oxides to promote energy density and maintain high power density and durable stability. Herein, carbon nanotubes (CNTs) were firstly organized on the surface of nickel foam (NF) by a facile and mild (room temperature) metal-induced self-assembly process. Afterward, NiCoO2 nanosheets are bonded by CNTs films via hydrothermal method, and then the integrated NiCoO2@CNTs@NF electrodes were successfully fabricated. The kinds of self-supporting electrodes have some advantages of hierarchical three-dimensional (3D) network structure, strong binding force between NiCoO2 nanosheets and Ni foam substrate, excellent conductivity tunnel for ions and electrons transport, and abundant active sites for ions adsorption and fast faradic redox reaction. Accordingly, the NiCoO2@CNTs@NF integrated electrode present the superior capacitive properties and stabilities because of outstanding synergistic effect between CNTs as electric double-layer capacitance (EDLC) materials and NiCoO2 nanosheets as Faradic pseudocapacitance materials. Asymmetric supercapacitors (ASCs) were also fabricated based on NiCoO2@CNTs@NF electrodes and active carbon supported on Ni foam electrodes. The ASCs show prominent performances of a high special capacitance (151 F g−1 at 5 mA cm−2), an outstanding rate capability (83.8% when current densities changed from 5 to 50 mA cm−2), durable stability (above 90% after 5000 cycles), and a high energy density (56.0 Wh kg−1). Such three ASCs devices connected in series can light up four blue light-emitting diodes (LEDs) that operates at a minimum voltage of 2 V. The presented work provides an efficient approach to design an outstanding electrode by combining active materials with a metal substrate.