STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Pharmaceutical Science Original Research

Flora-Vision: A Quality Assurance System For The Pharmaceutical Industry

  1. Sakeena Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  2. Rashika Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  3. Saanihaa Mariyam Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  4. Shireen Department of Computer Science and Engineering, P A College of Engineering, Mangalore

Abstract

In contemporary pharmaceutical production, the persistent challenges of manual labor, human error, and contamination risks pose significant obstacles to efficiency and product quality. Particularly in sectors such as Ayurvedic products, cosmetics, and medicines, the need for innovation is pressing. Revolution in production process can be addressed by introducing innovative solutions and thereby challenges could be overcome. Through the utilization of advanced technology, the proposed system streamlines sample management and quality control procedures, offering a timely response to the industry's most pressing concerns. With a focus on Real-time Recognition & Classification and Quality Checking, the system ensures precise identification and labeling of samples while detecting anomalies to uphold stringent quality standards. By automating critical processes, the system minimizes labor costs, increases accuracy, and ultimately enhances overall efficiency and customer satisfaction. This project represents a crucial step forward in pharmaceutical production, promising increased reliability and regulatory compliance in an ever-evolving industry landscape.

Keywords

References (15)

  1. Sameerchand Pudaruth S, Mahomoodally MF, Kissoon N, Chady F. MedicPlant: A mobile application for the recognition of medicinal plants from the Republic of Mauritius using deep learning in real-time. IAES International Journal of Artificial Intelligence. 2021 Dec 1;10(4):938.
  2. Abdollahi Jafer. Identification of medicinal plants in ardabil using deep learning: In 2022 27th International computer conference, computer society of Iran (CSICC) 2022 Feb 23 (pp. 1-6). IEEE.
  3. A. H. Vo, H. T. Dang, B. T. Nguyen and V. H. Pham, “Vietnamese Herbal plant recognition using deep convolutional features,” International Journal of Machine Learning and Computing, vol. 9, no. 3,
  4. pp.363-367,2019,doi:10.18178/ijmlc.2019.9.3.811.
  5. Xue, S. Fuentes, C. Poblete-Echeverria, C. G. Viejo, E. Tongson, H. Du and B. Su., “Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy,” International Journal of Agricultural and Biological Engineering, vol. 12, no. 2, pp. 123-
  6. Beikmohammadi A, Faez K. Leaf Classification for Plant Recognition with Deep Transfer Learning. 2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). 2018:21-26. doi:10.1109/icspis.2018.8700547
  7. S. Prasad and P. P. Singh, “Medicinal plant leaf information extraction using deep features,” TENCON 2017 – 2017 IEEE Region 10
  8. Conference, Penang, Malaysia, 2017, pp. 2722-2726, doi:10.1109/TENCON.2017.8228324
  9. S. Jeon and S. Y. Rhee, “Plant leaf recognition using a convolutional neural network,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 17, no. 1, pp. 26-34, 2017, doi:10.5391/IJFIS.2017.17.1.
  10. Jeon WS, Rhee SY. Plant Leaf Recognition Using a Convolution Neural Network. The International Journal of Fuzzy Logic and Intelligent Systems. 2017;17(1):26-34. doi:10.5391/ijfis.2017.17.1.26
  11. Lin S, Xiu Y, Kong J, Yang C, Zhao C. An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture. Agriculture. 2023;13(3):567. doi:10.3390/agriculture13030567
  12. Azlah MAF, Chua LS, Rahmad FR, Abdullah FI, Wan Alwi SR. Review on Techniques for Plant Leaf Classification and Recognition. Computers. 2019;8(4):77. doi:10.3390/computers8040077
  13. S. Rajani and M. N. Veena, “Study on Identification and Classification on Medicinal Plants,” International Journal of Advances in Science and Engineering, vol. 6, no. 2, pp. 13-17, 2018.
  14. Sabu A, Sreekumar K, Nair RR. Recognition of ayurvedic medicinal plants from leaves: A computer vision approach. 2017 Fourth International Conference on Image Information Processing (ICIIP). 2017:1-5. doi:10.1109/iciip.2017.8313782
  15. Jamshidi-Kia F, Lorigooini Z, Amini-Khoei H. Medicinal plants: Past history and future perspective. Journal of Herbmed Pharmacology. 2018;7(1):1-7. doi:10.15171/jhp.2018.01
Support