MFDP-LeafNet: A few-shot learning method for plant species classification using multi-feature and historical dynamic prototypical network

This paper proposes MFDP-LeafNet, a novel framework designed to address the challenges associated with leaf classification using few-shot learning (FSL) with limited labelled data. Existing FSL models often struggle with insufficient training data, rely on additional learning strategies, suffer from limited feature diversity, and lack adaptability during inference. These issues limit the generalisation capability, particularly when dealing with variations in leaf shape, texture, and lighting conditions. To overcome these challenges, MFDP-LeafNet introduces a multi-backbone feature integration (MFI) module and a novel historical dynamic prototypical network (HDPN) module. Firstly, the MFI module leverages multiple pre-trained models to capture diverse feature representations, improving the model’s generalisation across diverse leaf categories. To address the computational overhead of this approach, a sparse autoencoder is incorporated with the MFI module, which selects the most discriminative features, thereby optimising computational efficiency. Secondly, the HDPN module enables adaptive learning during inference without the need for additional training. To validate the robustness of MFDP-LeafNet, we have conducted experiments across a diverse set of datasets, including three plant leaf datasets (Swedish Leaf, Flavia Leaf, Egyptian Plant Leaf), two widely used FSL benchmark datasets (MiniImageNet and FC100), and one plant disease dataset (PlantVillage). The experimental results across all datasets demonstrate that MFDP-LeafNet achieves competitive or improved performance compared with state-of-the-art models. The code is available at the following link: https://github.com/ismailmmu/MFDP-LeafNet .