Dawid Wojcieszak

The use of image analysis to investigate C:N ratio in the mixture of chicken manure and straw

The aim of the study was to determine the possibility of analysis of C:N ratio in the chicken manure and wheat straw mixture. This paper presents preliminary assumptions and parameters of extraction characteristics process. It also presents an introduction of digital image analysis of chicken manure and wheat straw mixture. This work is an introduction to the study on develop computer system that could replace chemical analysis. Good understanding the value of dependence C:N on the basis of image analysis will help in selection of optimal conditions for biological waste treatment.

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].

The recognition of potato varieties using of neural image analysis method

The aim of this paper was to extract the representative features and generate an appropriate neural model for classification of varieties of edible potato. Potatoes of variety the Vineta and the Denar were the empirical object of this thesis. The main concept of the project was to develop and prepare an image database using the computer image analysis software. The choice of appropriate neural model the one which will have the greatest abilities to identify the selected variety. The aim of this project is ultimately to conduct assistance and accelerate work of the expert, who classifies and keeps different varieties of potatoes in heaps.

Determination of dry matter content in composted material based on digital images of compost taken under mixed visible and UV-A light

The aim of the research was to investigate the possibility of using the methods of neural image analysis and neural modeling to determine the content of dry weight of compost based on photographs taken under mixed visible and UV-A light conditions. 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 RBF 30:30-8-1:1 characterized by RMS error 0,076378 and this networks is more effective than RBF 19:19-2:1:1 which works in visible light conditions.

Classification of dried vegetables using computer image analysis and artificial neural networks

In the recent years, there has been a continuously increasing demand for vegetables and dried vegetables. This trend affects the growth of the dehydration industry in Poland helping to exploit excess production. More and more often dried vegetables are used in various sectors of the food industry, both due to their high nutritional qualities and changes in consumers’ food preferences. As we observe an increase in consumer awareness regarding a healthy lifestyle and a boom in health food, there is also an increase in the consumption of such food, which means that the production and crop area can increase further. Among the dried vegetables, dried carrots play a strategic role due to their wide application range and high nutritional value. They contain high concentrations of carotene and sugar which is present in the form of crystals. Carrots are also the vegetables which are most often subjected to a wide range of dehydration processes; this makes it difficult to perform a reliable qualitative assessment and classification of this dried product. The many qualitative properties of dried carrots determining their positive or negative quality assessment include colour and shape. The aim of the research was to develop and implement the model of a computer system for the recognition and classification of freeze-dried, convection-dried and microwave vacuum dried products using the methods of computer image analysis and artificial neural networks.

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.

Neural classifier in the estimation process of maturity of selected varieties of apples

This paper seeks to present methods of neural image analysis aimed at estimating the maturity state of selected varieties of apples which are popular in Poland. An identification of the degree of maturity of selected varieties of apples has been conducted on the basis of information encoded in graphical form, presented in the digital photos. The above process involves the application of the BBCH scale, used to determine the maturity of apples. The aforementioned scale is widely used in the EU and has been developed for many species of monocotyledonous plants and dicotyledonous plants. It is also worth noticing that the given scale enables detailed determinations of development stage of a given plant. The purpose of this work is to identify maturity level of selected varieties of apples, which is supported by the use of image analysis methods and classification techniques represented by artificial neural networks. The analysis of graphical representative features based on image analysis method enabled the assessment of the maturity of apples. For the utilitarian purpose the ”JabVis 1.1” neural IT system was created, in accordance with requirements of the software engineering dedicated to support the decision-making processes occurring in broadly understood production process and processing of apples.

Marbling classification of lambs carcasses with the artificial neural image analysis

This paper describes a part of research, whose goal was to develop an effective method to determine marbling classes of lamb carcasses, with the neural image analysis techniques. Current methods for identifying the degree of intramuscular fat level content are time consuming, require specialized expertise and often rely on subjective assessment based on predefined patterns. In this paper, authors proposes the use of neural model developed as a tool to assist evaluation of marbling.

A computer method to analyse the impact of ultrasound frequency on the brightness of USG images of muscle cross-sections

A total number of 270 ultrasound images of m. longissimus behind the 13th thoracic vertebrae were obtained on 90 lamb carcasses. Three different scanning frequencies were used (5.0, 7.5 and 10.0 MHz) in order to analyse how the frequency of the ultrasound wave affects the changes of pixel brightness in the ultrasound image. In the images obtained with 10 MHz probe the brightness of the 1st region was higher by 27% and 25% (P≤0.01) compared to the same region of images obtained with 5 MHz and 7.5 MHz frequency probe. The 3rd region of muscle cross-sections obtained with 10 MHz frequency was very dark, with the brightness lower by 22,5% and 28,3% compared to the same region of images obtained with 5 MHz and 7.5 MHz frequency. In the images obtained with 10 MHz scanning frequency, the decrease of brightness from the 1st to the 3rd region of the image was very sharp. While in the images obtained by means of 5 and 7.5 MHz ultrasound frequency, the brightness changes between the regions were very fluent. To conclude, the results of the presented research reveal that high ultrasound frequency has a negative impact on ultrasound image brightness and may reduce the information value of the image.