Exploring Transfer Learning and Convolutional Networks in The High-Throughput Analysis of Medicinal Plant Morphology
DOI:
https://doi.org/10.63682/jns.v14i29S.5574Abstract
Automated image-based medicinal plant research utilizes deep learning methods to provide innovative classification methods together with evaluation procedures for medicinal properties. The identification technique delivers superior results than traditional plant recognition because it depends on specialized expertise and human verification yet produces lengthy processes along with incorrect interpretations. This report demonstrates an automated photo analysis approach for medicinal plant classification through CNN execution. The method used recognizes color, texture, and form, among other basic leaf features, thanks to extensive training of deep learning models.. This enables precise species classification. Transfer learning technologies based on pre-trained networks support accurate outcomes in addition to needing smaller datasets for network development. transfer learning improves the model's accuracy while minimizing the demand for intensive data collection. This method has the potential to significantly improve plant research by facilitating the quicker and more precise identification of therapeutic plants in field research as well as the herbal medicine industry. Additionally, by providing a non-invasive way to track plant species and their therapeutic qualities, the system contributes to biodiversity conservation. All things considered, this study establishes the foundation for incorporating AI into botanical research, enabling the effective and scalable examination of plants for medicinal purposes
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