{"title":"herbiify:一个集成了卷积神经网络和视觉转换器的集成深度学习框架,用于精确的草药识别。","authors":"Farhan Sheth, Ishika Chatter, Manvendra Jasra, Gireesh Kumar, Richa Sharma","doi":"10.1186/s13007-025-01421-5","DOIUrl":null,"url":null,"abstract":"<p><p>Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently accompanied by adverse side effects. In addition, the growing dependence on synthetic pharmaceuticals has raised concerns regarding affordability, thereby fostering a renewed interest in herbal medicine as a cost-effective and holistic alternative. In response to this need, the current study introduces a computer vision framework for accurate herb identification. A novel dataset, Herbify, was compiled from two different herb datasets and refined through rigorous cleaning, preprocessing, and quality control procedures. The resulting dataset underwent standardization via the Preprocessing Algorithm for Herb Detection (PAHD), producing a refined dataset of 6104 images, representing 91 distinct herb species, with an average of about 67 images per species. Utilizing transfer learning, the research harnessed pre-trained Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), then integrated these models into an ensemble framework that leverages the unique strengths of each architecture. Experimental results indicate that EfficientNet v2-Large achieved a noteworthy F₁-score of 99.13%, while the ensemble of EfficientNet v2-Large and ViT-Large/16, termed EfficientL-ViTL, attained an even higher F₁-score of 99.56%. Additionally, the research also introduces 'Herbify' application, an AI-driven framework designed to identify herbs using the developed model. 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引用次数: 0
摘要
草药在历史上一直是医疗实践的中心,代表了最早的治疗干预形式之一。虽然合成药物在治疗急性疾病方面往往非常有效,但它们的使用往往伴随着不良副作用。此外,对合成药物的日益依赖引起了人们对负担能力的关注,从而促进了对草药作为一种具有成本效益和整体替代品的重新兴趣。针对这一需求,本研究引入了一种用于准确识别草药的计算机视觉框架。一个新的数据集,herbiify,从两个不同的草药数据集编译,并通过严格的清洗,预处理和质量控制程序进行提炼。结果数据集通过草本检测预处理算法(PAHD)进行标准化,产生了6104张图像的精细化数据集,代表了91种不同的草本物种,平均每个物种约67张图像。利用迁移学习,该研究利用了预训练的卷积神经网络(cnn)和视觉转换器(ViTs),然后将这些模型集成到一个集成框架中,利用每个架构的独特优势。实验结果表明,效率网v2-Large获得了99.13%的显著F₁得分,而效率网v2-Large和viti - large /16的集合,称为效率- vitl,获得了更高的F₁得分99.56%。此外,该研究还介绍了“herbiify”应用程序,这是一个人工智能驱动的框架,旨在使用开发的模型识别草药。通过直接解决草药鉴定中的主要障碍,该系统实现了一个高度准确和操作可行的分类机制。实验结果展示了草药鉴定的顶级性能,并强调了基于人工智能的解决方案在支持植物应用方面的变革潜力。
Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification.
Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently accompanied by adverse side effects. In addition, the growing dependence on synthetic pharmaceuticals has raised concerns regarding affordability, thereby fostering a renewed interest in herbal medicine as a cost-effective and holistic alternative. In response to this need, the current study introduces a computer vision framework for accurate herb identification. A novel dataset, Herbify, was compiled from two different herb datasets and refined through rigorous cleaning, preprocessing, and quality control procedures. The resulting dataset underwent standardization via the Preprocessing Algorithm for Herb Detection (PAHD), producing a refined dataset of 6104 images, representing 91 distinct herb species, with an average of about 67 images per species. Utilizing transfer learning, the research harnessed pre-trained Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), then integrated these models into an ensemble framework that leverages the unique strengths of each architecture. Experimental results indicate that EfficientNet v2-Large achieved a noteworthy F₁-score of 99.13%, while the ensemble of EfficientNet v2-Large and ViT-Large/16, termed EfficientL-ViTL, attained an even higher F₁-score of 99.56%. Additionally, the research also introduces 'Herbify' application, an AI-driven framework designed to identify herbs using the developed model. By directly tackling the principal obstacles in herb identification, the proposed system achieves a highly accurate and operationally viable classification mechanism. The experimental outcomes showcase top-tier performance in herb identification and emphasize the transformative potential of AI-based solutions in supporting botanical applications.
期刊介绍:
Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences.
There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics.
Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.