The aim of the study was create a new, non-invasive method of assessing the quality of sugar beet topping using computer image analysis and artificial neural networks. In paper was carried out the analysis the methods used so far to topping assessment of roots and analysis of the possibilities of using the new proposed method. Classical methods allow an assessment only after harvest of roots (after pull out roots), and the proposed method enables the assessment before harvesting sugar beets. The study used 50 images of topped sugar beet roots, which have been subjected to computer analysis in order to improve the image contrast and brightness. The image was converted from color to images in grayscale, and was carried out segmentation and morphological transformations. Binary image was used to determine the surface area and root circuit and topping circiut. This information was used as input to the neural network, which was expanded to two features, ie. the ratio of the areas and circuits. On the output of the network was information about the topping in the form 0 and 1. Created neural network MLP 6:6-26-1:1 allowed for a sensitivity analysis, which returned information about two important features independent, ie. the surface area of the root and root surface area to topping. The analysis found that it is possible to use methods of computer image analysis for non-invasive assessment of the quality topping sugar beets.
This paper describes the research, whose goal was to develop an effective method based on the image analysis techniques for the evaluation of the slugs behaviour in the laboratory studies. The main task of the developed computer method is to assist in evaluating the degree of slugs’ acceptance of different plant species and varieties as food and to evaluate the effectiveness of active substances used against slugs. The laboratory tests are conducted in a climate chamber, into which are placed containers with grazing slugs, leaf circles and a hiding place. A video camera is installed in each container to monitor the slugs’ activity. The data from the cameras are stored on hard disks connected to a digital recording device. The task of the proposed computer algorithms is to perform automatic analysis of the stored video material. Video image analysis can be used to determine parameters relating to the slugs’ daily activity, the speed and trajectory of their movement, and the rate and extent of the damage done to the leaves. This task is performed in several stages including: movement detection, object recognition, object tracking and determination of the quantity of leaf damage.
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.
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.
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.
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.
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