In the universal computerization era important aspect is the possibility of man partial replacement in the identification process of the disease cases occurring in the plantations cultivation. This is very important when it comes to the process of early response to the disease and at a later stage for early treatment to help stop the disease or its elimination, which has its economic justification. Presented in this paper system may be the answer to these issues in the production of sugar beet
The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.
The Web application presented here supports plant production and works with the graph database Neo4j shell to support the assessment of the condition of crops on the basis of geospatial data, including raster and vector data. The adoption of a graph database as a tool to store and manage the data, including geospatial data, is completely justified in the case of those agricultural holdings that have a wide range of types and sizes of crops. In addition, the authors tested the option of using the technology of Microsoft Cognitive Services at the level of produced application that enables an image analysis using the services provided. The presented application was designed using ASP.NET MVC technology and a wide range of leading IT tools.
The aim of the research was made the dedicated application AOTK (pol. Analiza Obrazu Trzeszczki Kopytowej) for image processing and analysis of horse navicular bone. The application was produced by using specialized software like Visual Studio 2013 and the .NET platform. To implement algorithms of image processing and analysis were used libraries of Aforge.NET. Implemented algorithms enabling accurate extraction of the characteristics of navicular bones and saving data to external files. Implemented in AOTK modules allowing the calculations of distance selected by user, preliminary assessment of conservation of structure of the examined objects. The application interface is designed in a way that ensures user the best possible view of the analyzed images.
There have been noticed growing explorers' interest in drawing conclusions based on information of data coded in a graphic form. The neuronal identification of pictorial data, with special emphasis on both quantitative and qualitative analysis, is more frequently utilized to gain and deepen the empirical data knowledge. Extraction and then classification of selected picture features, such as color or surface structure, enables one to create computer tools in order to identify these objects presented as, for example, digital pictures. The work presents original computer system “Processing the image v.1.0” designed to digitalize pictures on the basis of color criterion. The system has been applied to generate a reference learning file for generating the Artificial Neural Network (ANN) to identify selected kinds of butterflies from the Papilionidae family.
In the recent years, there has been a continuously increasing demand for vegetables and dried vegetables. This trend affects the growth of the dehydration industry in Poland helping to exploit excess production. More and more often dried vegetables are used in various sectors of the food industry, both due to their high nutritional qualities and changes in consumers’ food preferences. As we observe an increase in consumer awareness regarding a healthy lifestyle and a boom in health food, there is also an increase in the consumption of such food, which means that the production and crop area can increase further. Among the dried vegetables, dried carrots play a strategic role due to their wide application range and high nutritional value. They contain high concentrations of carotene and sugar which is present in the form of crystals. Carrots are also the vegetables which are most often subjected to a wide range of dehydration processes; this makes it difficult to perform a reliable qualitative assessment and classification of this dried product. The many qualitative properties of dried carrots determining their positive or negative quality assessment include colour and shape. The aim of the research was to develop and implement the model of a computer system for the recognition and classification of freeze-dried, convection-dried and microwave vacuum dried products using the methods of computer image analysis and artificial neural networks.
P. Okoń, R. J. Kozłowski, M. Zaborowicz, K. Górna, A. Ludwiczak, P. Ślósarz, P. Janiszewski, P. Strzeliński, P. Jurek, K. Koszela, P. Boniecki, J. Przybył
A study was carried out to analyse the quality and usefulness of methods of edge identification in the case of images of winter rape leaves. For this purpose a five available methods that are implemented in Matlab were used. The methods such as: Sobel, Robert, Prewitt, Canny's algorithms and on the other hand Laplacian of Gaussian were compared. The study focused on the image characteristics extraction and based on the results select those methods that will best respond on the research problem, which was to found the relationship between the detected edges, and incidence spots of fungal diseases in the oilseed rape cultivation. The aim of the article was to present the possibilities of using Matlab function to compare two approaches to edge detection. The first approach was the transformation of the images into gray-scale and create a histogram. The second one focused on dividing the image into three RGB components palette and then proceed with thresholding. On such prepared samples was conducted edge detection by used the above-mentioned methods.
The aim of the paper is to present the neural image analysis as a method useful for identifying the position of the corpus luteum of domestic bovine on digital USG (UltraSonoGraphy) images. Corpus luteum is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The main function of the corpus luteum is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Position of the corpus luteum was considered in two locations: on the surface of the ovary and within its parenchymal. Prior to the classification, the ultrasound images have been processed using a sharpening filter - unsharp mask. To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the location of the corpus luteum. Results of this study indicate that neural image analysis may be a useful instrument for identifying the bovine corpus luteum in the context of its location on the surface or in the ovarian parenchyma. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-364- 285-1:1.
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