Digital Twin Modeling of Medicinal Plant Growth and Metabolite Prediction

@article{1713820,
      author = {Shubham Pradip Chavan, Dr. Manoj Dilip Patil, Jayshri Shivnath Borse},
      title = {Digital Twin Modeling of Medicinal Plant Growth and Metabolite Prediction},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {7},
      pages = {1968-1970},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713820.pdf},
      abstract = {Medicinal plants are valuable sources of bioactive secondary metabolites used in pharmaceutical, nutraceutical, and traditional medicine systems. However, variability in plant growth, environmental conditions, and metabolite production presents a major challenge for standardization and quality control. Digital twin modeling, an emerging concept integrating real-time data, computational models, and simulation, offers a promising solution for predicting plant growth and metabolite yield. This paper explores the concept, framework, methodologies, and applications of digital twin technology in medicinal plant research, with emphasis on growth dynamics and secondary metabolite prediction. The study highlights opportunities, limitations, and future prospects of digital twin systems in pharmacognosy and phytochemical research.},
      keywords = {Digital Twin, Medicinal Plants, Secondary Metabolites, Predictive Modeling, Pharmacognosy, Artificial Intelligence},
      month = {January},
      doi = {https://doi.org/10.64388/IREV9I7-1713820}
  }