Architecture of the intelligent system for risk management and recognition of mushroom species

The article presents the development of an intelligent system for recognising mushroom species that provides high accuracy and ease of use. To train the model, a large dataset ‘Mushrooms classification’ from the Kaggle platform was used, which provided the necessary diversity of images and achieved a classification accuracy of 85%. Data pre-processing included image quality checks, standardisation, and division into training, validation, and test samples, which contributed to efficient model training. The recognition algorithm is based on the ResNet convolutional neural network, which has demonstrated an accuracy advantage over other architectures.