A comparative and practical approach using quantum machine learning (QML) and support vector classifier (SVC) for Light emitting diodes mediated in vitro micropropagation of black mulberry (Morus nigra L.)

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

“…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.…”

“…The machine learning analysis section of our study reveals nuanced performances across different models and culture systems. For instance, in the semisolid medium, GP and MLP showed heightened effectiveness in predicting microcorms, aligning with observations of Aasim et al [72] on the promising applications of quantum computing-enhanced machine learning models in plant tissue culture. This suggests that while RF generally offers a robust performance, the intricate nature of biological data might sometimes favor other models or necessitate a hybrid approach for enhanced prediction accuracy.…”

“…This suggests that while RF generally offers a robust performance, the intricate nature of biological data might sometimes favor other models or necessitate a hybrid approach for enhanced prediction accuracy. Moreover, as explored by Aasim et al [72], the integration of quantum computing techniques in machine learning introduces a groundbreaking perspective on future agricultural research. In our study, the quantum-enhanced algorithms were not applied, but the reported success in other research indicates a potential way for elevating the predictive modeling capabilities in plant science, thereby enhancing the efficiency of plant tissue culture protocols.…”

Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation

“…The parallel success of RF in these diverse applications points to its strength in capturing the nuanced relationships between various growth parameters and environmental stressors. On the other hand, Aasim et al [ 66 ]’s exploration into quantum computing-enhanced ML models for optimizing black mulberry regeneration protocols introduces an innovative dimension to ML applications in plant tissue culture. While traditional ML models like SVC showed robust performance, incorporating quantum computing techniques opened new avenues for enhancing model predictions.…”

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“…The parallel success of RF in these diverse applications points to its strength in capturing the nuanced relationships between various growth parameters and environmental stressors. On the other hand, Aasim et al [66]'s exploration into quantum computingenhanced ML models for optimizing black mulberry regeneration protocols introduces an innovative dimension to ML applications in plant tissue culture. While traditional ML models like SVC showed robust performance, incorporating quantum computing techniques opened new avenues for enhancing model predictions.…”