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Automated plant species identification for the datasets (images) collected from the natural environment is a challenging task. This study investigates the development and application of ensemble deep learning models for fine-grained plant species identification. Two different types of plant species datasets have been used in this study. The first dataset (UBD_45) consists of 45 medicinal plant species from the natural environment with the imbalanced distribution of classes and the second dataset (VP_200) has 200 medicinal plant species with balanced classes from the natural environment. Six popular deep learning models (InceptionResNetV2, ResNet50, Xception, InceptionV3, MobileNetV2, and GoogleNet) were trained on both datasets and heterogeneous ensembles with various ensemble techniques (mean, weighted mean, voting, and stacked generalization) were performed. The validation and testing …