<![CDATA[One promising advancement in the field of food agriculture is the cultivation of mushrooms. Mushrooms can be broadly classified into two groups: edible mushrooms and non-edible mushrooms. Edible mushrooms serve various purposes, including as food, medicine, and other applications, while non-edible ones can lead to poisoning. However, distinguishing between edible and non-edible mushrooms is a complex task. Even a slight error in selecting suitable mushrooms for consumption can have health repercussions for consumers. The progress in science and technology, particularly in digital image processing, aids in the classification of mushrooms. Image classification using Convolutional Neural Networks (CNNs) presents an alternative to address this issue. This research primarily focuses on identifying potential toxins in mushrooms using CNNs, aiming to contribute to a more efficient and accurate approach in classifying mushrooms fit for consumption. The results demonstrate that the model trained with data augmentation achieved the highest accuracy, with 96.53% for training data and 93.22% for validation data, accompanied by lower loss rates. This underscores that CNNs are an efficient and accurate approach in classifying mushrooms based on their genus. Furthermore, this study also discovered that parameters such as the number of epochs, batch size, optimizer, image size, and image augmentation influence the model training process.]]>
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