Identification of physical parameters of cereal grain using computer image analysis and neural models

Nowakowski, Krzysztof; Raba, Barbara; Tomczak, Robert Jacek; Boniecki, P.; Kujawa, Sebastian; Nowak, Paweł; Matz, Radosław

doi:10.1117/12.2030769

Cited by 26 publications

(6 citation statements)

References 7 publications

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“…The proposed method has also a practical dimension, particularly in the context of the quality assessment of oocytes automation, and as a tool supporting the qualitative assessment process [37]. The generated neural classifier code can be a nucleus of the IT system dedicated to support the automation process of the oocyte quality assessment.…”

Section: Discussionmentioning

confidence: 99%

Neural image analysis in the process of quality assessment: domestic pig oocytes

et al. 2014

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The questions related to quality classification of animal oocytes are explored by numerous scientific and research centres. This research is important, particularly in the context of improving the breeding value of farm animals. The methods leading to the stimulation of normal development of a larger number of fertilised animal oocytes in extracorporeal conditions are of special importance. Growing interest in the techniques of supported reproduction resulted in searching for new, increasingly effective methods for quality assessment of mammalian gametes and embryos. Progress in the production of in vitro animal embryos in fact depends on proper classification of obtained oocytes.The aim of this paper was the development of an original method for quality assessment of oocytes, performed on the basis of their graphical presentation in the form of microscopic digital images. The classification process was implemented on the basis of the information coded in the form of microphotographic pictures of the oocytes of domestic pig, using the modern methods of neural image analysis.

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Section: Discussionmentioning

confidence: 99%

Neural image analysis in the process of quality assessment: domestic pig oocytes

et al. 2014

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“…The prepared data are subjected to neural classification using for exapmple Statistica Neural Networks package. The detailed methodology for the preparation of learning sets from digital images for the purpose of artificial neural networks are described in the publications [11]- [15].…”

Section: Methodsmentioning

confidence: 99%

Development of software for airborne photos analysis

et al. 2014

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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 [1]. 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 [2]. 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.

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“…In recent years, neural network models have played a special role because they are able to solve a range of issues, including scientific problems defined as unstructured (not susceptible to algorithmising). They can also effectively solve problems for which there is insufficient scientific knowledge or a lack of representative empirical data [4][5][6]. Neural network models are the general name for mathematical formulas and their software (or hardware) structures that implement signal processing through a network of interconnected elements (neurons) performing elementary mathematical operations.…”

Section: Introductionmentioning

confidence: 99%

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

et al. 2023

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Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.

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Identification of physical parameters of cereal grain using computer image analysis and neural models

Cited by 26 publications

References 7 publications

Neural image analysis in the process of quality assessment: domestic pig oocytes

Neural image analysis in the process of quality assessment: domestic pig oocytes

Development of software for airborne photos analysis

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

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