Abstract
Plants play a vital role in numerous domains, including medicine, agriculture, and environmental balance. Furthermore, they contribute to the production of oxygen and the retention of carbon dioxide, both of which are necessary for living beings. Numerous researchers have conducted thorough research in the classification of plant species where certain studies have focused on limited numbers of classes, while others have employed conventional machine-learning and deep-learning models to classify them. To address these limitations, this paper introduces a novel dual-stream neural architecture embedded with a soft-attention mechanism specifically developed for accurately classifying plant species. The proposed model utilizes residual and inception blocks enhanced with dilated convolutional layers for acquiring both local and global information. Following the extraction of features, both streams are combined, and a soft-attention technique is used to improve the distinct characteristics. The efficacy of the model is shown via extensive experimentation on varied datasets, including several plant species. Moreover, we have contributed a novel dataset that comprises 48 classes of different plant species. The results demonstrate a higher level of performance when compared to current models, emphasizing the capability of the dual-stream design in improving accuracy and model generalization. The integration of a dual-stream architecture, dilated convolutions, and soft attention provides a strong and reliable foundation for the botanical community, supporting advancement in the field of plant species classification.