ArticlePlant methods2022
Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- ANFIS-derived response surfaces reveal organ-specific optimal conditions for nutrient supply, light quality, plant developmental status, and culture configuration in Phalaenopsis amabilis in vitro propagation.BMC plant biology · 2026Article
- Integrating plant growth-promoting rhizobacteria and machine learning for drought tolerance assessment in green bean (Phaseolus vulgaris L.).BMC plant biology · 2026Article
- ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.Plant methods · 2026Article
- Optimizing biomass production and antioxidant dynamics in Ceratophyllum demersum L. using multi-walled carbon nanotubes and machine learning models.BMC plant biology · 2026Article
- Development and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study.JMIR aging · 2026Article
- Review
- Decoding the cannabis tissue culture puzzle: Machine learning analysis of cannabis in vitro morpho-physiological disorders expands the potential for precision micropropagation.Journal, genetic engineering & biotechnology · 2025Article
- Machine Learning-Aided Optimization of In Vitro Tetraploid Induction in Cannabis.International journal of molecular sciences · 2025Article
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- 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.Plant methods · 2024Article
- Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress.BMC plant biology · 2024Article
- Genotype-specific responses toPeerJ · 2024Article
- Identification and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms.Plant methods · 2023Article
- A novel selection model based on multivariate methods and arbitrary genetic parameters: a case study on tomato families.Plant methods · 2023Article
- Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis.PloS one · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Clonal Propagation of Walnuts (Plants (Basel, Switzerland) · 2022Review
- Intelligent grading method for walnut kernels based on deep learning and physiological indicators.Frontiers in nutrition · 2022Article
- Glutathione and copper ions as critical factors of green plant regeneration efficiency of triticaleFrontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOptimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R
resultsOur results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R
conclusionHere, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher's desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.
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