Khan Imran Ullah, Khan Haseeb Ali, Lee Jong Weon
Mixed Reality and Interaction Laboratory, Department of Software, Sejong University, Seoul 05006, Republic of Korea.
Plants (Basel). 2024 Sep 22;13(18):2655. doi: 10.3390/plants13182655.
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.
植物在众多领域发挥着至关重要的作用，包括医学、农业和环境平衡。此外，它们有助于氧气的产生和二氧化碳的留存，而这两者对生物来说都是必不可少的。许多研究人员对植物物种分类进行了深入研究，其中一些研究专注于有限数量的类别，而另一些研究则采用传统的机器学习和深度学习模型对其进行分类。为了解决这些局限性，本文引入了一种新颖的双流神经架构，该架构嵌入了专门为准确分类植物物种而开发的软注意力机制。所提出的模型利用通过扩张卷积层增强的残差块和初始块来获取局部和全局信息。在提取特征之后，将两个流进行合并，并使用软注意力技术来增强独特特征。通过在包括多种植物物种的不同数据集上进行广泛实验，展示了该模型的有效性。此外，我们贡献了一个包含48类不同植物物种的新颖数据集。结果表明，与当前模型相比，该模型具有更高的性能水平，强调了双流设计在提高准确性和模型泛化能力方面的作用。双流架构、扩张卷积和软注意力的整合为植物学界提供了一个强大而可靠的基础，支持植物物种分类领域的进步。
