Sebastian Kujawa

Neural identification of selected apple pests

The study was based on neural network modeling methods, including image analysis.Artificial neural networks as a powerful tool to identify pests.The color of pests as the dominant input variable of a neural model.The best classification ability was achieved by MLP topology. The subject of this study was to investigate the possibility of using artificial neural networks as a tool for classification, designed to identify apple orchard pests. The paper presents a classification neural model using optimized learning sets acquired on the basis of the information encoded in the form of digital images of selected pests. This study predominantly deals with the problem of the identification of 6 selected apple pests which are most commonly found in Polish orchards. Neural modeling techniques, including digital image analysis, were used to classify the pests.The qualitative analysis of neural models produced, indicates that multi-layered perceptron (MLP) neural network topology achieve the best classification ability. Representative features, allowing for effective pest identification are 23 visual parameters in the form of 7 selected coefficients of shape and 16 color characteristic of pests. The dominant input variables of a neural model, determining the correct identification of the features, contain information about the color of pests.Our results support the hypothesis that artificial neural networks are an effective tool that supports the process of identification of pests in apple orchards. The resulting neural classifier has been created to assist in the decision-making processes that take place during the production of apples, in the context of protection against pests.

Identification of malting barley varieties using computer image analysis and artificial neural networks

The project aimed to produce an identification model that allows for automatic recognition of malting barley varieties. The project used computer image analysis and artificial neural networks. The authors based on the analysis of biological material selected set of features describing the physical parameters allowing the identification of varieties. Image analysis of samples of barley digital photographs allowed the extraction of the characteristics of varieties. Obtained characteristics from the images were used as learning data for artificial neural network. Trained a multilayer perceptron network is characterized by the identification abilities at the level of human abilities.

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.

A stand for the image acquisition of composted material based on the sewage sludge

Composting is one of the best methods for sewage sludge management. The early identification of the young compost stage in composted material is important. The method for determining the degree of maturity of composted material containing sewage sludge will use the selected topologies of artificial neural networks. The learning processes of these networks will be carried out with the use of the information contained in digital images of composted material. It is important that acquisition of these images was carried out under constant lighting and exposure conditions on a suitable acquisition stand. The objectives of presented study were: to develop a stand for image acquisition of composted material, to determine the spectral distribution for used light sources and illuminance distribution for visible light, to determine the parameters for image acquisition of composted material. A suitable stand, consisted of three photographic chambers illuminated with visible light, UV-A light and mixed light, was developed. The spectral distribution of the used light sources and the illuminance distribution for visible light were analyzed and considered satisfactory. Image acquisition parameters, such as focal length, ISO sensitivity, aperture and exposure time, were specified.

Image parameters for maturity determination of a composted material containing sewage sludge

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.

IT system supporting acquisition of image data used in the identification of grasslands

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.

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.

Software supporting definition and extraction of the quality parameters of potatoes by using image analysis

The aim of this paper was to design and implement an information technology (IT) system supporting the analysis and interpretation of image descriptors. The software is characterized by its versatility and speed in operating while processing series of digital images. The computer system can be expanded by new methods and is dedicated as a kernel of an expert system. The application seeks to extract the parameters of quality characteristics of agricultural crops - in this case, potatoes – in order to generate a set of data as a .csv file. The system helps to prepare the assessment of quality parameters of potatoes and generate mathematical models using Artificial Neural Network (ANN) simulators such as MATLAB ANN Toolbox or STATISTICA ANN toolbox in order to create a training dataset and information in that dataset.

IT system for the identification and classification of soil valuation classes

The soil classification aspect is a very modern item within the scope of property management, and is closely related to managing the land register according to geodetic and cartographic law. The identification and systematics related to the soils in Poland is based on criteria that considers soil development under the influence of the geological features of the soil formation process as well as permanent human operation and use. Soil quality assessment with regard to its use value is increasingly based on IT methods in combination with algorithms and artificial intelligence (AI) tools. The aim of this study is to develop suitable models and implement an IT system to identify and classify the soil valuation classes with use of AI methods.

An IT system for the simultaneous management of vector and raster images

In this paper the authors present a research tool in the form of an IT system used to analyse the status of agricultural crops with the simultaneous use of spatial data and raster images sourced from drones. The authors have designed and delivered their original IT system using the latest technologies, including SQL Server 2012, ADO.NET Entity Framework API, Google Maps API, HTML5 and the Visual Studio 2013 integrated development environment. The system comprises a set of applications that support the process of collecting and processing of interrelated geographic data and raster images.