The application of computer image analysis in life sciences and environmental engineering

The main aim of the article was to present research on the application of computer image analysis in Life Science and Environmental Engineering. The authors used different methods of computer image analysis in developing of an innovative biotest in modern biomonitoring of water quality. Created tools were based on live organisms such as bioindicators Lemna minor L. and Hydra vulgaris Pallas as well as computer image analysis method in the assessment of negatives reactions during the exposition of the organisms to selected water toxicants. All of these methods belong to acute toxicity tests and are particularly essential in ecotoxicological assessment of water pollutants. Developed bioassays can be used not only in scientific research but are also applicable in environmental engineering and agriculture in the study of adverse effects on water quality of various compounds used in agriculture and industry.

[1]  Piotr Boniecki,et al.  Computer image analysis in the quality in procedure for selected carrot varieties , 2013, Other Conferences.

[2]  Piotr Boniecki,et al.  Koncepcja wykorzystania sztucznych sieci neuronowych w procesie oceny jakości pomidorów , 2011 .

[3]  Hanna Piekarska-Boniecka,et al.  Artificial neural networks for modeling ammonia emissions released from sewage sludge composting , 2012 .

[4]  Piotr Boniecki,et al.  The application of the Kohonen neural network in the nonparametric-quality-based classification of tomatoes , 2012, Digital Image Processing.

[5]  Agnieszka Ludwiczak,et al.  Artificial neural network analysis of ultrasound image for the estimation of intramuscular fat content in lamb muscle , 2011 .

[6]  C. Blaise,et al.  Acute toxicity assessment of aqueous samples using a microplate‐based Hydra attenuata assay , 1997 .

[7]  Piotr Boniecki,et al.  Neural image analysis for estimating aerobic and anaerobic decomposition of organic matter based on the example of straw decomposition , 2012, Digital Image Processing.

[8]  Piotr Boniecki,et al.  The selected examples of the application of computer image analysis in the assessment of environmental quality , 2013, Other Conferences.

[9]  P. Boniecki,et al.  Use of artificial neural networks in the identification and classification of tomatoes , 2013, Other Conferences.

[10]  Piotr Boniecki,et al.  Identification process of corn and barley kernel damages using neural image analysis , 2011, International Conference on Digital Image Processing.

[11]  Piotr Boniecki,et al.  Image analysis and neural networks in the process of identifying of selected mechanical damage to maize caryopses. , 2011 .

[12]  Piotr Boniecki,et al.  Possibilities of neural image analysis implementation in monitoring of microalgae production as a substrate for biogas plant , 2012, Digital Image Processing.

[13]  Piotr Boniecki,et al.  Identification of selected apple pests based on selected graphical parameters , 2013, Other Conferences.

[14]  Piotr Boniecki,et al.  The Identification of Mechanical Damages of Kernels Basis on Neural Image Analysis , 2009, 2009 International Conference on Digital Image Processing.

[15]  Piotr Boniecki,et al.  Neural Image Analysis of Maturity Stage during Composting of Sewage Sludge , 2009, 2009 International Conference on Digital Image Processing.

[16]  P. Boniecki,et al.  Neural prediction of heat loss in the pig manure composting process , 2013 .

[17]  Krzysztof Koszela.  CLASSIFICATION OF DRIED PARSNIP USING ARTIFICIAL NEURAL NETWORKS , 2012 .

[18]  C. Blaise,et al.  Acute toxicity assessment of industrial effluents with a microplate-based Hydra attenuata assay , 1997 .

[19]  Piotr Boniecki,et al.  Identification of malting barley varieties using computer image analysis and artificial neural networks , 2012, Digital Image Processing.

[20]  Piotr Boniecki,et al.  Identification of physical parameters of cereal grain using computer image analysis and neural models , 2013, Other Conferences.

[21]  Piotr Boniecki,et al.  Neural networks type MLP in the process of identification chosen varieties of maize , 2011, International Conference on Digital Image Processing.