The Employment of AI and Machine Learning methods for Mushroom Images Classification using Sequential CNN Model

The employment of AI and machine learning methods for mushroom classification has several potential uses and applications in the area of AI and related disciplines. It is possible to learn more about biodiversity and ecological health with the use of mushroom taxonomy. The use of AI models for mushroom identification and classification may help us better understand the ecology of a specific area. The work of mushroom picture classification may be both difficult and fascinating. Classifying mushrooms is crucial for a number of reasons, including determining which species are edible and which are dangerous, researching biodiversity, and ecological issues. This study uses a proposed Sequential Convolutional Neural Network (CNN) model for mushroom image classification. Pre-processing of data is done using the graphics processing unit (GPU). The Loss and Accuracy curves are then analysed to provide a visual representation of the Deep Learning model. The proposed model has an expected accuracy of 80%, which would facilitate further study into the categorization of mushrooms.