Modeling Callus Induction and Regeneration in Hypocotyl Explant of Fodder Pea (Pisum sativum var. arvense L.) Using Machine Learning Algorithm Method

Abstract

A comprehensive understanding of genetic diversity and the categorization of germplasm is important to effectively identify appropriate parental candidates for the goal of breeding. It is necessary to have a technique of tissue culture that is both effective and reproducible to perform genetic engineering on fodder pea genotypes (Pisum sativum var. arvense L.). In this investigation, the genetic diversity of forty-two fodder pea genotypes was assessed based on their ability of callus induction (CI), the percentage of embryogenic callus by explant number (ECNEP), the percentage of responding embryogenic calluses by explant number (RECNEP), the number of somatic embryogenesis (NSE), the number of responding somatic embryogenesis (RSE), the regeneration efficiency (RE), and the number of regenerated plantlets (NRP). The findings of the ANOVA showed that there were significant differences (p < 0.001) between the genotypes for all in vitro parameters. The method of principal component analysis (PCA) was used to study the correlations that exist between the factors associated with tissue culture. While RE and NRP variables were most strongly associated with Doğruyol, Ovaçevirme-4, Doşeli-1, Yolgeçmez, and Incili-3 genotypes, RECNEP, NSE, RDE, and RECNEP variables were strongly associated with Avcılar, Ovaçevirme-3, and Ardahan Merkez-2 genotypes. The in vitro process is a complex multivariate process and more robust analyses are needed for linear and nonlinear parameters. Within the scope of this study, artificial neural network (ANN), random forest (RF), and multivariate adaptive regression spline (MARS) algorithms were used for RE estimation, and these algorithms were also compared. The results that we acquired from our research led us to the conclusion that the employed ANN-multilayer perceptron (ANN-MLP) model (R2 = 0.941) performs better than the RF model (R2 = 0.754) and the MARS model (R2 = 0.214). Despite this, it has been shown that the RF model is capable of accurately predicting RE in the early stages of the in vitro process. The current work is an inquiry regarding the use of RF, MARS, and ANN models in plant tissue culture, and it indicates the possibilities of application in a variety of economically important fodder peas.

Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

“…The results of the research demonstrated that the RF model demonstrated a higher level of accuracy in forecasting the output variable. Machine learning models, in contrast to more conventional approaches, provide a strategy that is both more effective and more precise for predicting the outcomes of complex and nonlinear biological processes in the fields of agriculture and applied sciences [25,47,48]. All the germination parameters that were observed in our research showed that the XGboost model had the greatest performance in terms of prediction.…”

“…These approaches are advantageous because they are easily accessible and can be implemented without requiring extensive resources or specialized equipment. However, the usefulness of each model may change depending on the data used and the way the experiment is set up [24,25]. As a result, for plants grown on a large scale, like forage peas, it is crucial to anticipate how the plant will react to its surroundings during the germination period [26].…”

Optimizing PGRs for in vitro shoot proliferation of pomegranate with bayesian-tuned ensemble stacking regression and NSGA-II: a comparative evaluation of machine learning models

“…ML approaches offer the advantage of autonomous learning and data transformation into useful information without being humanly programmed [17]. Recent studies have highlighted the superior predictive performance of MLs over traditional statistics in various in vitro culture systems, including optimizing culture conditions for shoot proliferation and rooting [10,18,19], androgenesis [20], seed germination [21], somatic embryogenesis [22], gene transformation [23], and enhancing of the secondary metabolite biosynthesis [24].…”

Multivariate Analysis for Evaluation of Morphological Diversity in Pisum sativum L

“…It is an essential food and feed legume grown particularly from Asia to Europe and North America and across many temperate regions globally. Pea is extensively used as commercial protein because of its high availability, low price and sufficient production (Türkoğlu et al, 2023). It has different functional properties particular water and oil-holding capacities, foaming, solubility, gelling and emulsifying, and various health amenities viz antihypertensive, modulating intestinal bacteria activities and antioxidant are occupied by pea protein and its hydrolysates (Jiao, 2020).…”