Comparative Study Between Vision Transformer and EfficientNet on Marsh Grass Classification

Due to rapidly changing ecosystems, effective environmental protection often calls for the monitoring of vegetation any changes. Vegetation is essential in assessing changes and impacts environmentally valuable ecosystems such as marshlands. While marsh grasses crucial maintenance marshlands, it a tedious time-consuming task that involves careful examination individual pixels within large resolution images. In this study we compare use Vision Transformers (ViT) two different EfficientNet models on automated grass identification using GTMNERR Marsh Grass Species data set. Our results show ViT allowed an increase accuracy identification. The Transformer was also able better distinguish between 6 classes set provided competitive training time smaller tested study.