Mini Review - (2023) Volume 12, Issue 6
Received: 19-Oct-2023, Manuscript No. iem-23-122298;
Editor assigned: 21-Oct-2023, Pre QC No. P-122298;
Reviewed: 03-Nov-2023, QC No. Q-122298;
Revised: 08-Nov-2023, Manuscript No. R-122298;
Published:
15-Nov-2023
Citation: Langley, Rosalind. â??Using Machine Learning Algorithm
Method to Model Callus Induction and Regeneration in Hypocotyl Explant of
Fodder Pea (Pisum sativum var. arvense L.).â? Ind Eng Manag 12 (2023): 226.
Copyright: �© 2023 Langley R. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author
and source are credited.
Plant tissue culture techniques have revolutionized agricultural biotechnology, enabling the propagation of plants from cells and tissues. Understanding callus induction and regeneration in plant explants is crucial for crop improvement and genetic transformation. This article explores the application of machine learning algorithms to model callus induction and regeneration in Hypocotyl Explants of Fodder Pea (P. sativum var. arvense L.). Leveraging data-driven approaches, this study aims to predict and optimize the conditions necessary for efficient callus formation and subsequent plant regeneration, facilitating advancements in plant biotechnology and crop breeding.
Industrial Engineering & Management received 739 citations as per Google Scholar report
