Cell Recognition by Image Processing : Recognition of Dead or Living Plant Cells by Neural Network

Shiotani, Shigetoshi; Fukuda, Toshio; Arai, Fumihito; Takeuchi, Norikazu; Sasaki, Kyosuke; Kinosita, Tatsuyuki

doi:10.1299/jsmec1993.37.202

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Artificial intelligence (AI) models can be considered as one method to develop and optimize in vitro shoot regeneration protocols. Although there are no reports to use AI models in in vitro culture of wheat, several studies have previously proved the reliability and accuracy of AI methodology to predict and optimize different in vitro culture processes such as in vitro sterilization [22,35], callogenesis [36][37][38], cell growth and protoplast culture [39,40], somatic embryogenesis [37,41,42], shoot regeneration [43][44][45][46], androgenesis [9], hairy root culture [47,48], and rhizogenesis [49] in other plants.…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

et al. 2020

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Optimizing in vitro shoot regeneration conditions in wheat is one of the important steps in successful micropropagation and gene transformation. Various factors such as genotypes, explants, and phytohormones affect in vitro regeneration of wheat, hindering the ability to tailor genotype-independent protocols. Novel computational approaches such as artificial neural networks (ANNs) can facilitate modeling and predicting outcomes of tissue culture experiments and thereby reduce large experimental treatments and combinations. In this study, generalized regression neural network (GRNN) were used to model and forecast in vitro shoot regeneration outcomes of wheat on the basis of 10 factors including genotypes, explants, and different concentrations of 6-benzylaminopurine (BAP), kinetin (Kin), 2,4-dichlorophenoxyacetic acid (2,4-D), indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), zeatin, and CuSO4. In addition, GRNN was linked to a genetic algorithm (GA) to identify an optimized solution for maximum shoot regeneration. Results indicated that GRNN could accurately predict the shoot regeneration frequency in the validation set with a coefficient determination of 0.78. Sensitivity analysis demonstrated that shoot regeneration frequency was more sensitive to variables in the order of 2,4-D > explant > genotype < zeatin < NAA. Results of this study suggest that GRNN-GA can be used as a tool, besides experimental approaches, to develop and optimize in vitro genotype-independent regeneration protocols.

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Section: Discussionmentioning

confidence: 99%

Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

et al. 2020

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“…AI models can be considered as a reliable strategy to develop and optimize gene transformation protocols. Although there are no reports to use AI models in genetic engineering and genome editing, several studies have previously proved the reliability and accuracy of AI methodology to predict and optimize different in vitro culture processes such as in vitro sterilization [ 45 , 46 ], callogenesis [ 34 , 47 , 48 ], cell growth and protoplast culture [ 49 , 50 ], somatic embryogenesis [ 34 , 51 , 52 ], shoot regeneration [ 12 , 53 – 55 ], androgenesis [ 33 ], hairy root culture [ 56 , 57 ], and rhizogenesis [ 58 ]. In the current study, MLP, RBF, ANFIS, and ensemble models, for the first time, were used to develop a suitable model for chrysanthemum gene transformation and compare their prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases

et al. 2020

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Optimizing the gene transformation factors can be considered as the first and foremost step in successful genetic engineering and genome editing studies. However, it is usually difficult to achieve an optimized gene transformation protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach such as machine learning models for analyzing gene transformation data. In the current study, three individual machine learning models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Radial Basis Function (RBF) were developed for forecasting Agrobacterium-mediated gene transformation in chrysanthemum based on eleven input variables including Agrobacterium strain, optical density (OD), co-culture period (CCP), and different antibiotics including kanamycin (K), vancomycin (VA), cefotaxime (CF), hygromycin (H), carbenicillin (CA), geneticin (G), ticarcillin (TI), and paromomycin (P). Consequently, best-obtained results were used in the fusion process by bagging method. Results showed that ensemble model with the highest R 2 (0.83) had superb performance in comparison with all other individual models (MLP:063, RBF:0.69, and ANFIS: 0.74) in the validation set. Also, ensemble model was linked to Fruit fly optimization algorithm (FOA) for optimizing gene transformation, and the results showed that the maximum gene transformation efficiency (37.54%) can be achieved from EHA105 strain with 0.9 OD 600 , for 3.8 days CCP, 46.43 mg/l P, 9.54 mg/l K, 18.62 mg/l H, and 4.79 mg/l G as selection antibiotics and 109.74 μg/ml VA, 287.63 μg/ml CF, 334.07 μg/ml CA and 87.36 μg/ml TI as antibiotics in the selection medium. Moreover, sensitivity analysis demonstrated that input variables have a different degree of importance in gene transformation system in the order of Agrobacterium strain > CCP > K > CF > VA > P > OD > CA > H > TI > G. Generally, the developed hybrid model in this study (ensemble model-FOA) can be employed as an accurate and reliable approach in future genetic engineering and genome editing studies.

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“…Several studies reported the accuracy and reliability of artificial intelligence methodology to optimize various processes such as callogenesis, protoplast and cell growth, somatic embryogenesis, androgenesis, shoot regeneration, rhizogenesis, hairy root cultures, sterilization, temperature inside the culture containers, plant virus detection, secondary metabolite production, microshoot length, in vitro physiological disorders, shoot organogenesis, in-vitro rooting and acclimatization in several crops [123,124]. Shiotani et al used Multilayer perceptron (MLP) of artificial neural networks (ANNs) to classify the alive or dead cell status of Arabidopsis thaliana cultured protoplasts using digitalized imaged shape and color of cells [125]. In the family Apiaceae, MLP was used for the classification of somatic embryos and non-embryogenic structures of Apium graveolens that can be selected for transfer to the next culture phase [126,127].…”

Section: The Impact Of Artificial Intelligence On Protoplast Technolo...mentioning

confidence: 99%

Protoplast Technology and Somatic Hybridisation in the Family Apiaceae

Ranaware

Kunchge

Lele

et al. 2023

Plants

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Species of the family Apiaceae occupy a major market share but are hitherto dependent on open pollinated cultivars. This results in a lack of production uniformity and reduced quality that has fostered hybrid seed production. The difficulty in flower emasculation led breeders to use biotechnology approaches including somatic hybridization. We discuss the use of protoplast technology for the development of somatic hybrids, cybrids and in-vitro breeding of commercial traits such as CMS (cytoplasmic male sterility), GMS (genetic male sterility) and EGMS (environment-sensitive genic male sterility). The molecular mechanism(s) underlying CMS and its candidate genes are also discussed. Cybridization strategies based on enucleation (Gamma rays, X-rays and UV rays) and metabolically arresting protoplasts with chemicals such as iodoacetamide or iodoacetate are reviewed. Differential fluorescence staining of fused protoplast as routinely used can be replaced by new tagging approaches using non-toxic proteins. Here, we focused on the initial plant materials and tissue sources for protoplast isolation, the various digestion enzyme mixtures tested, and on the understanding of cell wall re-generation, all of which intervene in somatic hybrids regeneration. Although there are no alternatives to somatic hybridization, various approaches also discussed are emerging, viz., robotic platforms, artificial intelligence, in recent breeding programs for trait identification and selection.

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Cell Recognition by Image Processing : Recognition of Dead or Living Plant Cells by Neural Network

Cited by 9 publications

References 3 publications

Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases

Protoplast Technology and Somatic Hybridisation in the Family Apiaceae

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