EconPapers    
Economics at your fingertips  
 

Applications of Artificial Intelligence (AI) in Cannabis Industries: In Vitro Plant Tissue Culture

Ravindra B. Malabadi, Nethravathi Tl, Kiran P. Kolkar, Raju K. Chalannavar, Bhagyavana S. Mudigoudra, Gholamreza Abdi and Himansu Baijnath
Additional contact information
Ravindra B. Malabadi: Department of Applied Botany, Mangalore University, Mangalagangotri-574199, Mangalore, Karnataka State, India
Nethravathi Tl: Department of Artificial Intelligence (AI) and Machine Learning (ML), SJC Institute of Technology, Chikkaballapur-5621010, Karnataka state, India
Kiran P. Kolkar: Department of Botany, Karnatak Science College, Dharwad-580003, Karnataka State, India
Raju K. Chalannavar: Department of Applied Botany, Mangalore University, Mangalagangotri-574199, Mangalore, Karnataka State, India
Bhagyavana S. Mudigoudra: Department of Computer Science, Maharani Cluster University, Bangalore- 560 001, Karnataka state, India
Gholamreza Abdi: Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr, 75169, Iran
Himansu Baijnath: Ward Herbarium, School of Life Sciences, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa

International Journal of Research and Innovation in Applied Science, 2023, vol. 8, issue 7, 21-40

Abstract: This review paper highlights the application of artificial intelligence (AI) in Cannabis industries. Growing Cannabis especially on a large scale can come with several complex challenges unique to the industry. Therefore, artificial intelligence (AI) has been implemented across all stages of the Cannabis supply chain. Artificial intelligence (AI) is a powerful tool that can be applied in all aspects of the Cannabis industry. However, developing an effective artificial intelligence (AI) model is a challenging task due to the dynamic nature and variation in real-world problems and data. In addition, a growing number of artificial intelligence (AI) -powered apps, Chatbots, and websites are launching to help medical Cannabis (marijuana) customers to find the products. Artificial intelligence (AI) and machine learning (ML) have become essential to Cannabis businesses that want to display the most relevant products and services to consumers when they visit companies websites. Digital medical Cannabis represents the combination of a Cannabis product and a second- generation Artificial intelligence (AI), system to create a new intellectual property (IP). With medicinal and recreational interests for Cannabis sativa L. growing, research related to the optimization of in vitro practices is needed to improve the current methods. Plant tissue culture experiments comprise a part of very complex studies with many problems. In plant tissue culture studies, optimization is highly desirable and the application of new computational approaches like artificial intelligence (AI) and machine learning (ML) algorithms using fewer inputs is on the rise in recent years. It has been shown that Generalized Regression Neural Network (GRNN) as one of the most powerful of ANNs has more accuracy than other artificial neural networks (ANNs) in modeling and forecasting in vitro culture procedures.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... -8-issue-7/21-40.pdf (application/pdf)
https://www.rsisinternational.org/virtual-library/ ... 051938702.1694191524 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:8:y:2023:i:7:p:21-40

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().

 
Page updated 2025-03-19
Handle: RePEc:bjf:journl:v:8:y:2023:i:7:p:21-40