J. Dach

Neural Image Analysis of Maturity Stage during Composting of Sewage Sludge

The paper presents the experiments of compost images analysis carried out with two types of digital cameras working in daylight and ultraviolet light. The data collected with two cameras were analysed with the usage of neural network model (using part of application Statistica v. 8.0). The results of image analysis were combined also with the results of chemical and physical analysis of composted material in different stage of the composting process.

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.

The Identification of Mechanical Damages of Kernels Basis on Neural Image Analysis

The aim of the study was to develop a neural model for the identification of mechanical damage in maize caryopses based on digital photographs. The author has selected a set of features that distinguish between damaged and healthy caryopses. The study has produced an artificial neural network of a multilayer perceptron type whose identification capacity approximates that of a human.

Maturity classification for sewage sludge composted with rapeseed straw using neural image analysis

Composting is one of the most appropriate methods to manage sewage sludge. In the composting process it is essential to ensure possibly rapid detection of the early maturity stage in the composted material. The aim of the study was to generate neural classification models for the identification of this stage in the composted mixture of sewage sludge and rapeseed straw. These models were constructed using the MLP network topology. The datasets used in the construction of neural models were based on information contained in images of composted material photographed under visible light. The input variables were values of 25 parameters concerning colour of images in the RGB, HSV models and the greyscale and converted to binary images, as well as values of 21 texture parameters. The neural models were constructed iteratively. A neural network developed in a given iteration did not contain inputs, which the sensitivity analysis from the preceding iteration showed to be potentially non-significant. The classification error for the generated models ranged from 2.44 to 3.05%. The optimal model in terms of the lowest value of the classification error and thus the lowest number of required input variables contained 23 neurons in the input layer, 50 neurons in the hidden layer and 2 neurons in the output layer.

Assessment of the influence of composts on microbiological and biochemical parameters of substrates and the morphological traits of scarlet sage / Ocena wpływu kompostów na parametry mikrobiologiczne i biochemiczne podłoży oraz cechy morfologiczne szałwii błysz czącej

Abstract The aim of the research was to assess the microbiological (number of heterotrophic bacteria, actinobacteria and moulds) and biochemical (urease and acid phosphatase activity) state of peat with the admixture of composts produced from sewage sludge. An additional aim of the research was to demonstrate the influence of those substrates on the morphological traits of scarlet sage (height, number and length of shoots, number of buds and inflorescences, greenness index (SPAD)). Composts produced from sewage sludge, wheat, maize and lupine straw were mixed with peat, where their percentage varied from 25% to 75%. The substrate which included the composts applied in the experiment had a higher number of heterotrophic bacteria and a higher acid phosphatase activity level than the control substrate (peat). The multiplication of moulds and actinobacteria was more intensive than in the peat only in the combinations with K3 (sewage sludge 50%+sawdust 20%+ lupine straw 30%) and K4 (sewage sludge 50%+sawdust 20%+fresh maize straw 30%) composts, whereas the highest urease activity level was observed in the soils produced from K1 (sewage sludge 50%+sawdust 20%+white straw 30%) compost. The most optimal development of plants was observed in the substrate with compost produced from wheat straw. Composts produced from municipal sewage sludge were found to be suitable for growing scarlet sage. However, their effect depends on the percentage of high peat in the substrate. Streszczenie Celem badań była ocena stanu mikrobiologicznego (liczba bakterii heterotrofi cznych, promieniowców, pleśni) i biochemicznego (aktywność ureazy i fosfatazy kwaśnej) torfu z domieszką kompostów wytworzonych na bazie osadu ściekowego. Ponadto celem badań było wykazanie wpływu niniejszych podłoży na cechy morfologiczne szałwii błyszczącej (wysokość, liczba i długości pędów, liczba pąków i kwiatostanów, indeks zazielenienia (SPAD)). Komposty wyprodukowane z osadu ściekowego, słomy pszennej, kukurydzianej i łubinowej zmieszano z torfem w różnym udziale procentowym, od 25% do 75%. Podłoża zawierające w swoim składzie komposty charakteryzowały się wyższą niż podłoże kontrolne (torf) liczbą bakterii heterotroficznych oraz wyższą aktywnością fosfatazy kwaśnej. Silniejsze namnażanie grzybów pleśniowych oraz promieniowców w stosunku do kombinacji kontrolnej odnotowano jedynie w przypadku w kombinacji K3 (50% osadu ściekowego + 20% trociny + 30% słomy łubinowej) i K4 (50% osadu ściekowego + 20% trociny + 30% świeżej słomy kukurydzianej). Natomiast najwyższy poziom aktywności ureazy zaobserwowano w obiekcie K1 (50% osadu ściekowego + 20% trociny + 30% słomy pszennej). Najbardziej optymalny rozwój roślin zaobserwowano na podłożach wyprodukowanych na bazie kompostu z dodatkiem słomy pszennej. Na podstawie uzyskanych wyników stwierdzono, że komposty wyprodukowane z komunalnych osadów ściekowych mogą być stosowane w uprawie szałwii błyszczącej. Ich działanie zależy od procentowego udziału kompostów w podłożu.

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.