Detailed Comparative Analysis of Transfer Learning Based Ensemble Models for Mushroom Classification

Abstract

Identifying mushroom species accurately is often challenging due to the vast diversity and visual differences among species at different stages of growth. This study investigates the effectiveness of stacking ensemble technique using a combination of 3–4 transfer learning models as Base classifiers with simple average and weighted average method, to enhance mushroom classification accuracy. Our research focuses on two primary aspects: the performance of individual transfer learning models and the impact of stacking ensemble techniques on transfer learning models for mushroom classification. The results of each ensemble of transfer learning model exhibited strong performance, achieving high accuracy in the range of 90.54% − 98.42% for Weighted Average models, with the best performance using three transfer learning modes: ResNet50V2-MobileNet–VGG16. The weighted average approach assigned weights to each model based on their class wise performance during model construction and henceforth results in better accuracy compared to simple average method.