Neural networks type MLP in the process of identification chosen varieties of maize

Boniecki, P.; Nowakowski, Krzysztof; Tomczak, Robert Jacek

doi:10.1117/12.896184

Cited by 25 publications

(17 citation statements)

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“…To identify the quality of oocytes, neural classification of information coded in a graphic form was used [21][22][23][24]. Artificial Neural Networks (ANN) are acknowledged data classifiers, also graphical data in the form of images [25][26][27].…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

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

et al. 2014

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“…Hundreds of simulations were made with the ANN simulator in Statistica, as a result of which three adequate neural networks were chosen (Table 2). Networks characterized by the greatest ability for classification were defined by the Multi-Layer Perceptron (MLP) model [41][42][43]. This type of network turned out to be appropriate for the batch of five fruits, with the sound of fruit falling into the water and the sound of the crushed strawberry.…”

Section: Classificationmentioning

confidence: 99%

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

Przybył

Duda

Koszela

et al. 2020

Sensors

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In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

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“…A lot of commercial and free software is also available [9]. Success has been achieved in many research areas such as software design and integration, modeling identification [2,7,8], control techniques implementation [14], aerial image processing [3,4,6] and many others. UAVs used in the study was self-built from specialized parts available to the RC (Remote Controlled) models.…”

Section: Introductionmentioning

confidence: 99%

Autonomous flying system for grasslands and fields monitoring

et al. 2012

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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 obtained automatically.

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Neural networks type MLP in the process of identification chosen varieties of maize

Cited by 25 publications

References 0 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

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

Autonomous flying system for grasslands and fields monitoring

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