ArticleProtoplasma2019
Modeling callus induction and regeneration in an anther culture of tomato (Lycopersicon esculentum L.) using image processing and artificial neural network method.
Article in Protoplasma, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.Plant methods · 2026Article
- Recent advances in generation of doubled haploid plants for genetic improvement in solanaceous vegetable crops.Planta · 2026Review
- Machine Learning-Aided Optimization of In Vitro Tetraploid Induction in Cannabis.International journal of molecular sciences · 2025Article
- Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.BMC biotechnology · 2024Article
- 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
- Article
- Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.PloS one · 2024Article
- Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms.BMC biotechnology · 2023Article
- Application of Data Modeling, Instrument Engineering and Nanomaterials in Selected Medid the Scientific Recinal Plant Tissue Culture.Plants (Basel, Switzerland) · 2023Review
- Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis.PloS one · 2023Article
- Optimization ofFrontiers in plant science · 2023Article
- Article
- Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function.BMC biotechnology · 2022Article
- Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate.Scientific reports · 2022Article
- Morphological and anatomical characterization of yellow diploid potato flower for effective breeding program.Scientific reports · 2022Article
- Molecular Tools and Their Applications in Developing Salt-Tolerant Soybean (Bioengineering (Basel, Switzerland) · 2022Review
- Haploid Induction in Tomato (Plants (Basel, Switzerland) · 2022Review
- Article
- Review
- Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm.Applied microbiology and biotechnology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Doubled haploids, subsequent to haploid induction, have wide range of applications in basic and applied plant studies. Various parameters can affect the efficiency of haploid induction through an anther culture of tomato. The hybrid system of image processing-artificial neural network (ANN) was used to better understand callus induction and regeneration in an anther culture of tomato. The effect of parameters such as plant genotype, the concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D) and kinetin (Kin) plant growth regulators, the concentration of gum arabic (GA) additive, the cold pretreatment duration, and flower length on callus induction percentage and number of regenerated calli in an anther culture of tomato were studied using multiple linear regression (MLR) and ANN models. The precise flower bud length was measured using an image processing technique. The 4',6-diamidino-2-phenylindole (DAPI) analysis showed that the flowers with 5-6.9 mm length had the highest percentage of the mid- to late-uninucleate microspore stage. The best ANN model for both callus induction percentage and number of regenerated calli was a model with one hidden layer, 12-15 neurons in the first hidden layer, Levenberg-Marquardt learning algorithm, and Tan-Sigmoid transfer function in hidden layer, based on the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R
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