Mushroom Classification Using Convolutional Neural Network MobileNetV2 Architecture for Overfitting Mitigation and Enhanced Model Generalization

Fungal identification is a significant challenge due to the morphological similarities among different species. Previous studies using Convolutional Neural Networks (CNNs) for mushroom classification still face overfitting issues, which lead to poor performance on new data. Therefore, this research develops a MobileNetV2-based Convolutional Neural Network (CNN) model capable of classifying three mushroom species (Amanita, Boletus, and Lactarius) with a primary focus on mitigating overfitting. The dataset consists of 3,210 RGB images, divided into 1,979 training data, 493 validation data, and 738 testing data. The model is developed using transfer learning with MobileNetV2, combined with additional layers such as Conv2D, pooling, and Dense, along with Dropout for regularization. The training process employs the Adam optimizer with a learning rate of 1.0×10⁻⁵ and is monitored with EarlyStopping and ModelCheckpoint. The model successfully addresses overfitting, achieving a minimal generalization gap of 1.33%, compared to 7% in previous studies. The evaluation results show a training accuracy of 77.35%, validation accuracy of 78.79%, and testing accuracy of 76.02%, with precision of 80.6% and recall of 68.1%. The consistent performance, with a maximum difference of only 2.77% across the three datasets, demonstrates superior generalization ability and provides a strong foundation for the implementation of a reliable automatic mushroom identification system.