Application of Identification and Evaluation Techniques for Edible Mushrooms: A Review

Complete mitochondrial genomes of edible mushrooms: features, evolution, and phylogeny

Edible mushrooms are an important food source with high nutritional and medicinal value. They are a useful source for studying phylogenetic evolution and species divergence. The exploration of the evolutionary relationships among these species conventionally involves analyzing sequence variations within their complete mitochondrial genomes, which range from 31,854 bp (Cordyceps militaris) to 197,486 bp (Grifolia frondosa). The study of the complete mitochondrial genomes of edible mushrooms has emerged as a critical field of research, providing important insights into fungal genetic makeup, evolution, and phylogenetic relationships. This review explores the mitochondrial genome structures of various edible mushroom species, highlighting their unique features and evolutionary adaptations. By analyzing these genomes, robust phylogenetic frameworks are constructed to elucidate mushrooms lineage relationships. Furthermore, the exploration of different variations of mitochondrial DNA presents novel opportunities for enhancing mushroom cultivation biotechnology and medicinal applications. The mitochondrial genomic features are essential for improving agricultural practices and ensuring food security through improved crop productivity, disease resistance, and nutritional qualities. The current knowledge about the mitochondrial genomes of edible mushrooms is summarized in this review, emphasising their significance in both scientific research and practical applications in bioinformatics and medicine.

Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species

Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine.

A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network

The automatic identification of disease types of edible mushroom crops and poisonous crops is of great significance for improving crop yield and quality. Based on the graph convolutional neural network theory, this paper constructs a graph convolutional network model for the identification of poisonous crops and edible fungi. By constructing 6 graph convolutional networks with different depths, the model uses the training mechanism of graph convolutional networks to analyze the results of disease identification and completes the automatic extraction of the disease characteristics of the poisonous crops by overfitting problem. During the simulation, firstly, the relevant PlantVillage dataset is used to obtain the pretrained model, and the parameters are adjusted to fit the dataset. The network framework is trained and parameterized with prior knowledge learned from large datasets and finally synthesized by training multiple neural network models and using direct averaging and weighting to synthesize their predictions. The experimental results show that the graph convolutional neural network model that integrates multi-scale category relationships and dense links can use dense connection technology to improve the representation ability and generalization ability of the model, and the accuracy rate generally increases by 1%-10%. The average recognition rate is about 91%, which greatly promotes the ability to identify the diseases of poisonous crops.