Krzysztof Pilarski

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.

Neural image analysis for estimating aerobic and anaerobic decomposition of organic matter based on the example of straw decomposition

The purpose of the project was to identify the degree of organic matter decomposition by means of a neural model based on graphical information derived from image analysis. Empirical data (photographs of compost content at various stages of maturation) were used to generate an optimal neural classifier (Boniecki et al. 2009, Nowakowski et al. 2009). The best classification properties were found in an RBF (Radial Basis Function) artificial neural network, which demonstrates that the process is non-linear.

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.

Utilization of vegetable dumplings waste from industrial production by anaerobic digestion.

Abstract This paper provides the analysis of results of biogas and methane yield for vegetable dumplings waste: dough with fat, vegetable waste, and sludge from the clarifier. Anaerobic digestion of food waste used in the experiments was stable after combining the substrates with a digested pulp composed of maize silage and liquid manure (as inoculum), at suitable ratios. The study was carried out in a laboratory scale using anaerobic batch reactors, at controlled (mesophilic) temperature and pH conditions. The authors present the chemical reactions accompanying biodegradation of the substrates and indicate the chemical compounds which may lead to acidification during the anaerobic digestion. An anaerobic digestion process carried out with the use of a dough-and-fat mixture provided the highest biogas and methane yields. The following yields were obtained in terms of fresh matter: 242.89 m3 Mg−1 for methane and 384.38 m3 Mg−1 for biogas, and in terms of volatile solids: 450.73 m3 Mg−1 for methane and 742.40 m3 Mg−1 for biogas. Vegetables and sludge from the clarifier (as fresh matter) provided much lower yields.

Evaluation of the fertilizer value of digestate pulp from biogas plant and its composts

Streszczenie. Artykuł niniejszy stanowi kontynuację pracy tych samych autorów (Pilarska i in., 2015), w której przedstawiono charakterystykę składu chemicznego oraz aktywności enzymatycznej różnych pofermentów uzyskanych w wyniku fermentacji metanowej gnojowicy z różnymi dodatkami organicznymi i kompostów z tych pofermentów. Drugi etap doświadczenia, którego wyniki przedstawiono w obecnej publikacji, polegał na aplikacji uzyskanych materiałów oraz ocenie ich wartości nawozowej. W tym celu wiosną 2012 roku założono doświadczenie wazonowe, w którym wykorzystano jałową glebę pobraną z gruntów rolnych znajdujących się na terenie Wielkopolski. Wartość nawozową badanych materiałów organicznych oceniano na podstawie ich wpływu na właściwości gleb oraz plony suchej masy tymotki łąkowej (Phleum pratense L.). W wykonanym doświadczeniu wykazano korzystny wpływ stosowania przefermentowanych i kompostowanych odpadów na badane parametry gleb. Oddziaływanie użytych materiałów na

Directions of utilisation of waste from green areas

Abstract Each year communities generate large quantities of municipal waste, including green waste such as grass and leaves. According to the waste catalogue, they may be treated as belonging to the group 20 02 (garden and park waste), and more specifically, to the group 20 02 01 – biodegradable waste. The aim of the study is to characterise the development directions of the green waste generated in the municipalities. Skilful management of this waste is extremely important. The following work focuses on three different technologies that enable efficient management of the green waste: methane fermentation, pelletising and composting. According to many authors, the most common technologies are pelletising and composting. In contrast, biogas fermentation of green waste in the municipalities is performed on a much smaller scale than other technologies. This may be due to the fact that this technology requires a significant expertise and is more complicated in terms of technology

Classification of chosen orchard pests using the self- organizing feature maps neural network

The Kohonen neural networks are modelled on the topological properties of the human brain. These networks are also known as self-organizing feature maps (SOFM). One advantage of suggesting a procedure is the ability of the SOFM neural network to determine the degree of similarity occurring between classes. The SOFM network can also be used to detect regularities occurring in the obtained empirical data. If at the network input, a new unknown case appears which the network is unable to recognise, it means that it is different from all the classes known previously. The SOFM network taught in this way can serve as a detector signalling the appearance of a widely understood novelty. Such a network can also look for similarities between the known data and the noisy data. In this way, it is able to identify fragments of images presenting photographs of orchard pests, for example. The resulting model of the Kohonen neural turns to be effective without reference classifier. The average classification error SOFM network during its operation was 0.05532 for the learning set and 0.0762 for the validtion set.