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The aim of this project is to compare the effectiveness of different neural network architectures and pre-trained models for the classification of forest flora and fauna. Experiments were conducted on a dataset containing images of selected plants and animals found in the forest. The project covers the basics of convolutional neural networks, and compares the architectures of the networks used in the study. The results of the experiments compare the effectiveness of different convolutional neural network architect...

This article presents the concept of using neural networks in the recognition of fungi for use in a mobile forest ecosystem inspection robot. There are many dependencies regarding the occurrence of fungi in the vicinity of specific tree species. The presence of some fungi may be the result of a developing tree disease. The possibility of quick recognition of the fungus species using an autonomous mobile robot will allow for faster detection and prevention of the disease in entire ecosystems. An attempt was made to...