Growth Simulation of Alfalfa Cuttings in Vitro by Kalman Filter Neural Network

Tani, Atsushi; Murase, Hiroki; Kiyota, Makoto; Honami, N.

doi:10.17660/actahortic.1992.319.108

Cited by 8 publications

(4 citation statements)

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“…The high learning potential of artificial neural networks provides this system the ability to identify and model complex non-linear relationships between the input and output of bioprocesses (Hashimota 1997). Earlier, the application of ANN in plant tissue culture systems has been described in few reports (Tani et al 1992;Hudson and Cohen 2000;Prasad and Dutta Gupta 2006). Large-scale cultivation of plants and plant cells in bioreactor using tissue culture approach has also been found effective for commercial plant production as well as production of high value natural compounds.…”

Section: Resultsmentioning

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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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%

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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“…on plant growth (Prasad and Gupta 2006). A growth model was developed for the study in effect for CO 2 and sucrose content on in vitro shoots of alfalfa (Tani et al 1992). Gago et al (2010c) developed a neural network model for in vitro rhizogenesis and acclimatization of micropropogated Vitis vinifera L. plants.…”

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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“…Early applications include modeling sensory color quality of tomato and peach [99], investigation of combined effects of CO 2 and sucrose on the growth of alfalfa cuttings using a Kalman filter neural network [100], classification of apple surface features [101], corn kernel breakage classification [102], and machine vision inspection of potatoes [103].…”

Section: Ann Applications In Agricultural and Biological Engineeringmentioning

confidence: 99%

Advances in Artificial Neural Networks – Methodological Development and Application

Huang

2009

Algorithms

157

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Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering.

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Growth Simulation of Alfalfa Cuttings in Vitro by Kalman Filter Neural Network

Cited by 8 publications

References 0 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

Advances in Artificial Neural Networks – Methodological Development and Application

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