Deep Learning Based Approach for Classification of Mushrooms

Deep learning algorithms have produced amazing results in recent years when used to identify items in digital photographs. A deep learning technique is suggested in this work to classify mushrooms in their natural habitat. The study's objective is to identify the most effective method for categorizing mushroom images produced by well-known CNN models. This study will be helpful for the field of pharmacology, mushroom hunters who gather mushrooms in the wild, and it will help to lower the number of people who are at risk of becoming ill from poisonous mushrooms. The pictures are from data that specialists from INaturalist tagged. The photographs show mushrooms in their natural environment and feature a variety of backgrounds. The "Mobilenetv2_GAP_flatten_fc" model, which was the study's top performer, had a training data set accuracy of 99.99%. It was 97.20% accurate in the categorization that was done using the validation data. Using the test data set, the classification accuracy was 97.89%. In this study, we report the findings of our comparison between the performance of the best performing model and numerous state-of-the-art models that have already undergone training. The top model greatly outperformed the trained models, according to the findings. This illustrates how the basic training process of the suggested model can be applied to enhance feature extraction and learning.