Jacek Przybył

The selected examples of the application of computer image analysis in the assessment of environmental quality

The environmental monitoring (EM) is an essential part of protection of the environment, most of the methods of environmental protection based on visual techniques or physico-chemical and biochemical measurements. The automation of traditional methods proceeds at an accelerating rate, modern laboratories prefer this type of tools to conduct a more comprehensive assessment and online monitoring. The application of computer image analysis methods in biomonitoring brings to this discipline the opportunity to develop innovative tools that allow for more precise sensitive and quantified assessment of monitored processes. The application of techniques based on computer image processing technology will dominate in the future and very comfortable and intuitive tool for researchers in the study of the components of the environment quality. The article presents some methods of automation the acute toxicity bioassay based on the application of computational methods.

Possibilities of neural image analysis implementation in monitoring of microalgae production as a substrate for biogas plant

The paper presents the possibilities of neural image analysis of microalgae content in the large-scale algae production for usage as a biomass. With the growing conflict between the culture produced both for feed and energetic purpose in Europe, the algae production seems to be very efficient way to produce the huge amount of biomass outside of conventional agronomy. However, for stable microalgae production the key point for culture management is the rapid estimation of algae population and assessment of its developmental stage. In traditional way the microalgae content is usually checked by the long microscopic analyses which cannot be used in large-scale industrial cultivation. Moreover, highly specialized personnel is required for algal determinations. So the main aim of this study is to estimate the possibility of usage of automatic image analysis of microalgae content made by artificial neural network. The preliminary results show that the selection of artificial neural network topology for the microalgae identification allowed for the selection and choice of teaching variables obtained by studying the image analysis. The selected neural model on the basis of data from computer image analysis allows to carry out the operations of algae identification and counting. On the basis of the obtained results of preliminary tests it is possible to count the algae on the photos. Additional information on their size and color allows to unlimited categorization.

Use of artificial neural networks in the identification and classification of tomatoes

The project aimed to produce a classification model of neural network that would allow automatic evaluate quality of greenhouse tomatoes. The project used computer image analysis and artificial neural networks. Authors based on the analysis of biological material selected set of features that are describing the physical parameters allowing the quality class identification. Image analysis of tomatoes digital photographs samples allowed to choose characteristics features. Obtained characteristics from the images were used as learning data for artificial neural network.

Computer image analysis in the quality in procedure for selected carrot varieties

In our daily lives we often assess our surroundings to classify the situations we encounter. We do so based on the observations we make of our surroundings and information we obtain from other sources, using our knowledge and abilities. While this process is natural to us, if we want to give a similar task to a computer system then we have to take various steps in order to enable our computers to partially emulate the human capacity for observation, learning and making final decisions based on knowledge. As information complexity increases, there is an increasing demand for systems which can recognize and classify the objects presented to them. Recently there has been an increase in interest in application of computer image analysis in various research areas. One of these applications is food quality assessment, which aims to replace traditional instrumental methods. A computer visual system was developed to assess carrot quality, based on a single variety. Characteristic qualities of the variety were chosen to describe a suitable root. In the course of the study, digital photographs of carrot roots were taken, which were used as input data for the assessment performed by a dedicated computer program created as a part of the study.

Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

Identification of selected apple pests based on selected graphical parameters

The aim of this work was a neural identification of selected apple tree orchard pests. The classification was conducted on the basis of graphical information coded in the form of selected geometric characteristics of agrofags, presented on digital images. A neural classification model is presented in this paper, optimized using learning sets acquired on the basis of information contained in digital photographs of pests. In particular, the problem of identifying 6 selected apple pests, the most commonly encountered in Polish orchards, has been addressed. In order to classify the agrofags, neural modelling methods were utilized, supported by digital analysis of image techniques.

Computer image analysis in caryopses quality evaluation as exemplified by malting barley

One of the purposes to employ modern technologies in agricultural and food industry is to increase the efficiency and automation of production processes, which helps improve productive effectiveness of business enterprises, thus making them more competitive. Nowadays, a challenge presents itself for this branch of economy, to produce agricultural and food products characterized by the best parameters in terms of quality, while maintaining optimum production and distribution costs of the processed biological material. Thus, several scientific centers seek to devise new and improved methods and technologies in this field, which will allow to meet the expectations. A new solution, under constant development, is to employ the so-called machine vision which is to replace human work in both quality and quantity evaluation processes. An indisputable advantage of employing the method is keeping the evaluation unbiased while improving its rate and, what is important, eliminating the fatigue factor of the expert. This paper elaborates on the topic of quality evaluation by marking the contamination in malting barley grains using computer image analysis and selected methods of artificial intelligence [4-5].

Neural image analysis in the process of quality assessment: domestic pig oocytes

The questions related to quality classification of animal oocytes are explored by numerous scientific and research centres. This research is important, particularly in the context of improving the breeding value of farm animals. The methods leading to the stimulation of normal development of a larger number of fertilised animal oocytes in extracorporeal conditions are of special importance. Growing interest in the techniques of supported reproduction resulted in searching for new, increasingly effective methods for quality assessment of mammalian gametes and embryos. Progress in the production of in vitro animal embryos in fact depends on proper classification of obtained oocytes. The aim of this paper was the development of an original method for quality assessment of oocytes, performed on the basis of their graphical presentation in the form of microscopic digital images. The classification process was implemented on the basis of the information coded in the form of microphotographic pictures of the oocytes of domestic pig, using the modern methods of neural image analysis.

Image acquisitions, processing and analysis in the process of obtaining characteristics of horse navicular bone

The aim of this study was investigate the possibility of using methods of computer image analysis for the assessment and classification of morphological variability and the state of health of horse navicular bone. Assumption was that the classification based on information contained in the graphical form two-dimensional digital images of navicular bone and information of horse health. The first step in the research was define the classes of analyzed bones, and then using methods of computer image analysis for obtaining characteristics from these images. This characteristics were correlated with data concerning the animal, such as: side of hooves, number of navicular syndrome (scale 0-3), type, sex, age, weight, information about lace, information about heel. This paper shows the introduction to the study of use the neural image analysis in the diagnosis of navicular bone syndrome. Prepared method can provide an introduction to the study of non-invasive way to assess the condition of the horse navicular bone.

Use of neural image analysis methods in the process to determine the dry matter content in the compost

The aim of this research was investigate the possibility of using methods of computer image analysis and artificial neural networks for to assess the amount of dry matter in the tested compost samples. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in the compost. Generated neural model may be the beginning of research into the use of neural image analysis assess the content of dry matter and other constituents of compost. The presented model RBF 19:19-2-1:1 characterized by test error 0.092189 may be more efficient.