Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling

Pilarska, Agnieszka A.; Boniecki, P.; Idzior-Haufa, Małgorzata; Zaborowicz, Maciej; Pilarski, Krzysztof; Przybylak, Andrzej; Piekarska-Boniecka, H.

doi:10.3390/agriculture11080732

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…Multilayer perceptron type ANNs have been used as well to enhance the spatial resolution in detector modules constituting the PET (positron emission tomography) scanner [47]. Artificial neural networks are also used in agriculture, agrophysics or agricultural engineering [16,40,41,[43][44][45][48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

et al. 2022

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The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the modulus of elasticity bending and maximum stress in bending as the dependent variables. As independent variables (inputs of the neural network) we assume water content, internode number, maximum bending force value and dimensions characterizing the cross-section of miscanthus stem: maximum and minimum stem diameter and stem wall thickness. The four developed neural models, enabling the determination of the value of the modulus of elasticity in bending and the maximum stress in bending, demonstrate sufficient and even very high accuracy. The neural networks have an average relative error of 2.18%, 2.21%, 3.24% and 0.18% for all data subsets, respectively. The results of the sensitivity analysis confirmed that all input variables are important for the accuracy of the developed neural models—correct semantic models.

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Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

et al. 2022

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“…The work focuses on the determination of malting barley grain (Latin: Hordeum vulgare) impurities. This choice was dictated by unsatisfactory reports concerning the state of the art of classification methods currently used in the malting industry [7,8]. The qualitative evaluation of kernels is conducted by certified malt workers acting as experts.…”

Section: Introductionmentioning

confidence: 99%

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

Boniecki

Raba

Pilarska

et al. 2021

Sensors

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Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.

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Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique

Asefa

Tsige²,

Mehdi³

et al. 2023

Smart Agricultural Technology

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Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling

Cited by 7 publications

References 40 publications

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley

Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique

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