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Role of Deep Learning and Machine Learning Algorithms in Identifying and Classifying Secondary Metabolites in Ashwagandha Plant
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Author(s): K. Kavitha (Velammal College of Engineering and Technology, Madurai, India), S. Rajkumar (Velammal College of Engineering and Technology, Madurai, India), N. Vijay (Kalasalingam Academy of Research and Education, India), S. Gandhimathi Alais Usha (Velammal College of Engineering and Technology, Madurai, India)and P. Uma Maheswari (Velammal College of Engineering and Technology, Madurai, India)
Copyright: 2025
Pages: 16
Source title:
Secondary Metabolites and Their Applications in Various Diseases
Source Author(s)/Editor(s): Athanasios Alexiou (Novel Global Community Education Foundation, Australia), Saurabh Kumar Jha (Department of Zoology, Kalindi College, University of Delhi, India)and Roma Pandey (Department of Biotechnology, IILM University, Greater Noida, India)
DOI: 10.4018/979-8-3693-9112-9.ch002
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Abstract
Ashwagandha plant (Withania somnifera), celebrated for its therapeutic properties, is rich in secondary metabolites like withanolides, alkaloids, and flavonoids, which contribute to its medicinal value. Identifying these metabolites using traditional methods can be labor-intensive and costly. This chapter explores the transformative role of deep learning (DL) and machine learning (ML) algorithms in metabolite identification and classification, integrating them with advanced spectral imaging techniques such as multispectral and hyperspectral cameras. These approaches enable rapid, non-invasive, and precise analysis of metabolite profiles, addressing limitations of conventional techniques. The chapter presents a comprehensive review of recent advancements, highlighting applications of ML/DL in enhancing accuracy and efficiency in metabolomics. Future research directions are discussed, focusing on personalized herbal medicine, biodiversity conservation, and sustainable agriculture.
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