Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

Abstract

This study aimed to assess the susceptibility of three strawberry cultivars (“Festival”, “Fortuna”, and “Rubygem”) to drought stress induced by varying polyethylene glycol (PEG) concentrations in the culture medium. Plantlets were cultivated on a solid medium supplemented with 1 mg/L BAP, and PEG concentrations (0, 2, 4, and 6 mg/L) were introduced to simulate drought stress. Morphological changes were observed, and morphometric analysis was conducted. Additionally, artificial neural network (ANN) analysis and machine learning approaches were integrated into this study. The results showed significant effects of PEG concentrations on plant height and multiplication coefficients, highlighting genotype-specific responses. This study employed various machine learning models, with random forest consistently demonstrating superior performance. Our findings revealed the random forest model outperformed others with a remarkable global diagnostic accuracy of 91.164%, indicating its superior capability in detecting and predicting water stress effects in strawberries. Specifically, the RF model excelled in predicting root length and the number of roots for “Festival” and “Fortuna” cultivars, demonstrating its reliability across different genetic backgrounds. Meanwhile, for the “Rubygem” cultivar, the multi-layer perceptron (MLP) and Gaussian process (GP) models showed particular strengths in predicting proliferation and plant height, respectively. These findings highlight the potential of ML models, particularly RF, to enhance agricultural breeding and cultivation strategies through accurate phenotypic predictions, suggesting a promising direction for future research to improve these predictions further. This research contributes to understanding strawberry responses to drought stress and emphasizes the potential of machine learning in predicting plant characteristics.

Saffron In Vitro Propagation: An Innovative Method by Temporary Immersion System (TIS), Integrated with Machine Learning Analysis

“…Our research underscores the superior predictive performance of RF in the TIS culture, particularly in forecasting the number of microcorms, shoots, and roots. This finding is consistent with the literature such as Şimşek [35], which highlighted the robustness of RF in detecting water stress effects in strawberries, and Aasim et al [37], which identified the efficacy of RF in predicting germination and morphological traits in hemp seedlings. The ability of RF to In the general view of machine learning applications within the agricultural domain, particularly focusing on plant tissue culture, our study delves into the comparative effectiveness of the four models used in two distinct culture systems, namely the TIS and semisolid medium.…”

“…Our research underscores the superior predictive performance of RF in the TIS culture, particularly in forecasting the number of microcorms, shoots, and roots. This finding is consistent with the literature, such as Şimşek [35], which highlighted the robustness of RF in detecting water stress effects in strawberries, and Aasim et al [37], which identified the efficacy of RF in predicting germination and morphological traits in hemp seedlings. The ability of RF to…”

“…The ability of RF to In the general view of machine learning applications within the agricultural domain, particularly focusing on plant tissue culture, our study delves into the comparative effectiveness of the four models used in two distinct culture systems, namely the TIS and semisolid medium. This analysis is in line with other studies [35,37,[72][73][74], which have explored various dimensions of machine learning applicability in predicting plant growth parameters under different experimental conditions. Our research underscores the superior predictive performance of RF in the TIS culture, particularly in forecasting the number of microcorms, shoots, and roots.…”

“…It is a non-parametric classifier that calculates the likelihood that input samples belong to particular classes, particularly for binary datasets. One of its main features is its capacity to operate effectively with tiny datasets and produce reliable, accurate, and computationally efficient results [35]. Equation ( 5) presents the mathematical reasoning for each input (x) and matching output (y).…”

Effects of Protein Hydrolysate Derived from Anchovy By-Product on Plant Growth of Primrose and Root System Architecture Analysis with Machine Learning

“…The application of machine learning (ML) is prevalent in solving complex problems across diverse scientific disciplines [25]. Despite its widespread use in various fields, the adoption of ML methodologies in plant and agricultural science is comparatively limited [26].…”