Survey of Machine Learning and Optimization Algorithms in Plant Tissue Culture

• Published in: Engineering proceedings Vol. 56; no. 1; p. 122
• Main Authors: Nimat Ibrahim, Adedokun Micheal Adedamola, Balkisu Ibrahim, Rasheedat Taiwo Ahmed, Ismail Damilola Raji, Habeeb Bello-Salau
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.10.2023
• Subjects: breeding; embryogenesis; machine learning algorithm; optimization
• DOI: 10.3390/ASEC2023-15259

With the increasing global population and agriculture facing numerous challenges due to climate change, finding sustainable solutions to food insecurity is crucial, as hunger and undernutrition continue to be a global challenge. Plant tissue culture has emerged as a promising technology for improving and multiplying crops rapidly. However, this technique produces extensive data due to the intricate interactions between genetic and environmental components, challenging traditional statistical methods. To address this, researchers are now employing machine learning techniques which excel in handling large, intricate datasets. Thus, current machine learning applications in plant tissue culture research are presented in this mini review.