AELGNet: Attention-based Enhanced Local and Global Features Network for medicinal leaf and plant classification

Pharmaceutical companies increasingly use medicinal plants because they are cheaper and have fewer side effects than conventional drugs. Accurate identification and classification of medicinal plants is critical for guaranteeing scientific evidence-based usage of herbal treatments in traditional medicine, upholding pharmaceutical safety requirements, and contributing to biodiversity conservation efforts. However, conventional manual classification methods are time-consuming, error-prone, and necessitate specialized knowledge. As a result, many researchers are very interested in studying the automatic classification of therapeutic plants. Current state-of-the-art techniques rely primarily on leaf or plant imagery, restricting their application to certain scenarios. This study combines a large dataset of medicinal plants and their accompanying leaves to create a more generalizable approach for classifying medicinal plants efficiently. The first phase uses contrast-limited adaptive histogram equalization (CLAHE) to highlight important features in medicinal plant and leaf images. The proposed deep learning architecture, Attention-based Enhanced Local and Global Features Network (AELGNet), utilizes these images to extract and classify prominent features. Three MBConv modules in the AELGNet extract base features, subsequently dividing them into four non-overlapping patches for local feature extraction. Additionally, the AELGNet examines base features for global feature extraction. We simultaneously apply residual channel-wise and spatial attention to each patch and global feature to extract more conspicuous information pertinent to the medicinal plant or leaves. The experiment employs a dataset of Indian medicinal plants to assess the efficacy of ALEGNet. AELGNet has a 99.71% accuracy, a 99.80% precision, a 99.75% recall, and a 99.77% F1 score. The suggested AELGNet outperforms 14 current methods with an accuracy range of 2%-10%. The findings confirm AELGNet in medical and industrial settings, providing a strong tool for accurately and quickly identifying medicinal plants and leaves.