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.
Systems type UAV / UAS enable acquisition of huge amounts of data, such as images. For their storage and analysis IT systems are necessary. Existing systems do not always allow you to perform such operations as researchers wish to . The purpose of the research is to automate the process of recognizing objects and phenomena occurring on grasslands. The basis for action are numerous collections of images taken from the oktokopter . For the purpose of the collection, management and analysis of image data and character acquired in the course of research, in accordance with the principles of software engineering several computer programs has been produced. The resulting software is different functionality and type. Applications were made using a number of popular technologies. The choice of so many technology was primarily dictated by the possibilities of their use for specific tasks and availability on different platforms and the ability to distribute open source. Applications presented by the authors, designed to assess the status of grassland based on aerial photography, show the complexity of the issues but at the same time tend to further research.
A complex research project was undertaken by the authors to develop a method for the automatic identification of grasslands using the neural analysis of aerial photographs made from relative low altitude. The development of such method requires the collection of large amount of various data. To control them and also to automate the process of their acquisition, an appropriate information system was developed in this study with the use of a variety of commercial and free technologies. Technologies for processing and storage of data in the form of raster and vector graphics were pivotal in the development of the research tool.
Composting is one of the best methods for management of sewage sludge. In a reasonably conducted composting process it is important to early identify the moment in which a material reaches the young compost stage. The objective of this study was to determine parameters contained in images of composted material’s samples that can be used for evaluation of the degree of compost maturity. The study focused on two types of compost: containing sewage sludge with corn straw and sewage sludge with rapeseed straw. The photographing of the samples was carried out on a prepared stand for the image acquisition using VIS, UV-A and mixed (VIS + UV-A) light. In the case of UV-A light, three values of the exposure time were assumed. The values of 46 parameters were estimated for each of the images extracted from the photographs of the composted material’s samples. Exemplary averaged values of selected parameters obtained from the images of the composted material in the following sampling days were presented. All of the parameters obtained from the composted material’s images are the basis for preparation of training, validation and test data sets necessary in development of neural models for classification of the young compost stage.
The subject of the project was the selection of neural models for the identification of physical parameters of grain
quality regarding to malting barley. Help in its implementation was the original computer system, "Hordeum v 2.0", in
which graphic data was gained from digital images of kernels obtained by acquisition. The principal aim was to verify
whether the artificial neural networks in combination with computer image analysis can become a practical tool used in
farming, and whether the proposed technology can be applied in analysing the quality of cereal grains.
Remote sensing is a very useful method for data collection in open spaces, especially in precision agriculture and has
been widely used over centennial. This paper presents the development of methodologies and identification of a surface
model grasslands and pastures based on of chosen guidelines and properties. The model will be used to automate the
process of monitoring the grasslands based on the analysis of spatial data and computer analysis of aerial photographs
During the adaptation process of the weights vector that occurs in the iterative presentation of the teaching vector, the the
MLP type artificial neural network (MultiLayer Perceptron) attempts to learn the structure of the data. Such a network
can learn to recognise aggregates of input data occurring in the input data set regardless of the assumed criteria of
similarity and the quantity of the data explored.
The MLP type neural network can be also used to detect regularities occurring in the obtained graphic empirical data.
The neuronal image analysis is then a new field of digital processing of signals. It is possible to use it to identify chosen
objects given in the form of bitmap. 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 MLP type artificial neural 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 presented in photographs of e.g. maze's grain.
The purpose of the research was to use the MLP neural networks in the process of identification of chosen varieties of
maize with the use of image analysis method. The neuronal classification shapes of grains was performed with the use of
the Johan Gielis super formula.
The subject of the study was to develop a neural model for the identification of mechanical damage in grain caryopses
based on digital photographs. The authors has selected a set of universal 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.