Treffer: A Novel Multistage Approach for Medicinal Plant Classification with Deep Learning Techniques.

Accurate classification of medicinal plant images into high-level categories and specific sub-groups is essential for various applications, including agriculture, plant research, and conservation. This paper proposes a multi-stage deep learning approach to enhance the precision of medicinal plant image classification. In the first stage, known as Broad Classification, CNN and pre-trained models such as VGG16, ResNet50 and EfficientNetB0 are utilized to categorize images into high-level groups, including "Medicinal Plants," "Fruit-Related Plants," and "Flower-Related Plants." The model is fine-tuned using data augmentation techniques to ensure robust learning and generalization. In the second stage, referred to as Detailed Classification, separate models are trained for each high-level group to classify images into specific sub-groups within that category. The architecture of these models is adjusted to accommodate the unique number of classes in each sub-group. Each model undergoes training with optimized hyperparameters and is evaluated based on precision, recall, F1-score, and accuracy. The proposed multi-stage method demonstrates the ability to handle both broad and fine-grained medicinal plant classifications effectively, showcasing an improvement in classification performance over traditional single-stage models. This approach highlights the potential for deep learning to contribute to more precise and practical medicinal plant image classification solutions. [ABSTRACT FROM AUTHOR]

Copyright of International Research Journal of Multidisciplinary Technovation (IRJMT) is the property of Asian Research Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)