Application of image analysis with neural network for plant somatic embryo culture

Uozumi, Nobuyuki; Yoshino, Tomoyuki; Shiotani, Shigetoshi; Suehara, Ken Ichiro; Arai, Fumihito; Fukuda, Toshio; Kobayashi, Takeshi

doi:10.1016/0922-338x(93)90249-8

Cited by 44 publications

(17 citation statements)

References 11 publications

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“…ANN can be applied to plant tissue cultural practices for pattern recognition of somatic embryos, photosynthetic and photometric evaluation of regenerated plants and on-line evaluation of biomass and control of secondary metabolite production (Albiol et al 1995). An efficient automated system was established to enumerate and evaluate the developmental stages of celery embryos (Uozumia et al 1993). This attempt was made to classify the celery somatic embryos from non-embryos so that an appropriate time can be decided for the transfer to next culture stage.…”

Section: Resultsmentioning

confidence: 99%

“…Neural network technology deals with approximation of different complex mathematical functions to process and interpret various sets of unpredictable data. Earlier artificial neural network (ANN) technology found to be helpful in plant tissue culture practices during recognition of growth patterns of inoculated tissue or explant (Uozumia et al 1993) and biomass estimation in plant cell cultures (Albiol et al 1995). Besides, neural networks have also shown remarkable progress in the upcoming area of on-line control and evaluation of bioprocesses (Patnaik 1999).…”

Section: Introductionmentioning

confidence: 99%

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Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

Mehrotra¹,

Prakash²,

Mishra³

et al. 2008

Plant Cell Tiss Organ Cult

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This study represents an ANN based computational scheming of physical, chemical and biological parameters at flask level for mass multiplication of plants through micropropagation using bioreactors of larger volumes. The optimal culture environment at small scale for Glycyrrhiza plant was predicted by using neural network approach in terms of pH and volume of growth medium per culture flask, incubation room temperature and month of inoculation along with inoculum properties in terms of inoculum size, fresh weight and number of explant per flask. This kind of study could be a model system in commercial propagation of various economically important plants in bioreactors using tissue culture technique. In present course of study the ANN was trained by implementing MATLAB neural network. A feed-forward back propagation type network was created for input vector (seven input elements), with single hidden layer (seven nodes) and one output unit in output layer. The 'tansig' and 'purelin' transfer functions were adapted for hidden and output layers respectively. The four training functions viz. traingda, trainrp, traincgf, traincgb were randomly selected to train four networks which further examined with available dataset. The efficiency of neural networks was concluded by the comparison of results obtained from this study with that of empirical data obtained from the detailed tissue culture experiments and designated as Target set (mean fresh weight biomass per culture flask after 40 days of in vitro culture duration). Efficiency of networks for better training initialization was judged on the basis of comparative analysis of 'Mean Square Error at zero epoch' for each network trained in which the least error at initial point was observed with trainrp followed by traincgb and traincgf. A comparative assessment between experimental target data range obtained from wet lab practice and all trained network output range for the efficiency of trained networks for least deviation from target range revealed the output range of network 'trainrp' was closest to the empirical target range while least comparison was worked out from network 'traincgb' which had output range more than the target decided and ultimately showed meaningless result.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

Mehrotra¹,

Prakash²,

Mishra³

et al. 2008

Plant Cell Tiss Organ Cult

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“…Initial applications of artificial neural networks in plant science include optimization studies. Earlier studies used neural networks with image analysis to identify live or dead plant cells (Fukuda et al 1991), analyze developmental stages of somatic embryos (Uozumi et al 1993), etc. Some researchers used hybrid fuzzy neural model to predict the length of shoots regenerated from rice callus to be transferred from the growth medium to sugar-free medium for acclimatization.…”

Section: Applications Of Artificial Neural Network In Plant Biologymentioning

confidence: 99%

Machine Learning Techniques in Plant Biology

Osama

Mishra

Somvanshi

2015

PlantOmics: The Omics of Plant Science

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“…The situation is even more complex when Fourier descriptors are used as it is difficult to find a real physical significance to them. Data-mining tools such as Principal Component Analysis [90,135,157,158], Discriminant Analysis [82], fuzzy logic and Artificial Neural Nets [41,72,95,132,133,159] are useful for clarifying the situation.…”

Section: ) Pellet Convex Area -Core Convex Areamentioning

confidence: 99%

Biomass Quantification by Image Analysis

Pons

Vivier

1999

Bioanalysis and Biosensors for Bioprocess Monitoring

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Microbiologists have always rely on microscopy to examine microorganisms. When microscopy, either optical or electron-based, is coupled to quantitative image analysis, the spectrum of potential applications is widened: counting, sizing, shape characterization, physiology assessment, analysis of visual texture, motility studies are now easily available for obtaining information on biomass. In this chapter the main tools used for cell visualization as well as the basic steps of image treatment are presented. General shape descriptors can be used to characterize the cell morphology, but special descriptors have been defined for filamentous microorganisms. Physiology assessment is often based on the use of fluorescent dyes. The quantitative analysis of visual texture is still limited in bioengineering but the characterization of the surface of microbial colonies may open new prospects, especially for cultures on solid substrates. In many occasions, the number of parameters extracted from images is so large that data-mining tools, such as Principal Components Analysis, are useful for summarizing the key pieces of information.

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Application of image analysis with neural network for plant somatic embryo culture

Cited by 44 publications

References 11 publications

Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

Machine Learning Techniques in Plant Biology

Biomass Quantification by Image Analysis

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