Neural classification of the selected family of butterflies

Zaborowicz, Maciej; Boniecki, P.; Piekarska-Boniecka, H.; Koszela, Krzysztof; Mueller, W.; Górna, Karolina; Okoń, P.

doi:10.1117/12.2281705

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A linear model is represented by an artificial neural network with no hidden layers which means that it is one-layer structure (except for the special case of three-layer autoassociative networks implementing dimension reduction of the data vector by means of a linear principal components analysis (PCA) transformation). The neurons in the output layer are fully linear [21,22], i.e., they are neurons in which the total excitation is determined as a linear combination of the input values and which have a linear activation function. Of course, only the second layer processes the information, while the role of the first layer is to introduce the information (signal) into the network.…”

Section: Linear Neural Network Versus Linear Regression Modelsmentioning

confidence: 99%

“…The statistical equivalents for standard linear neural network models are logistic regression for classification issues and linear (least squares) regression for regression analysis methods [5,22].…”

Section: Linear Neural Network Versus Linear Regression Modelsmentioning

confidence: 99%

“…In [21], the authors proposed a methodology for creating the learning sets necessary to generate artificial neural networks (ANN) for analysing graphically encoded information on the quality of pork half carcasses. Publication [22] deals with the determination of a selected family of butterflies presented in digital images by means of a generated ANN classifier, and an additional study [24] describes the application of neural network image analysis methods in quality assessment of greenhouse tomatoes using linear ANNs.…”

Section: Linear Neural Network Versus Linear Regression Modelsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Linear Neural Network Versus Linear Regression Modelsmentioning

confidence: 99%

Section: Linear Neural Network Versus Linear Regression Modelsmentioning

confidence: 99%

Section: Linear Neural Network Versus Linear Regression Modelsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

View full text Add to dashboard Cite

show abstract

Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN

et al. 2020

View full text Add to dashboard Cite

In this paper, the classification capabilities of perceptron and radial neural networks are compared using the identification of selected pests feeding in apple tree orchards in Poland as an example. The goal of the study was the neural separation of five selected apple tree orchard pests. The classification was based on graphical information coded as selected characteristic features of the pests, presented in digital images. In the paper, MLP (MultiLayer Perceptrons), RBF (Radial Basis Function) and DNN (Deep Neural Networks) neural classification models are compared, generated using learning files acquired on the basis of information contained in digital photographs of five selected pests. In order to classify the pests, neural modeling methods were used, including digital image analysis techniques. The qualitative analysis of the neural models enabled the selection of optimal neuron topology that was characterized by the highest classification capability. As representative graphic features were selected five selected coefficients of shape and two defined graphical features of the classified objects. The created neuron model is dedicated as a core for computer systems supporting the decision processes occurring during apple production, particularly in the context of apple tree orchard pest protection automation.

show abstract

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

et al. 2021

View full text Add to dashboard Cite

Quality evaluation of products is a critical stage in the process of production. It also applies to the production of beer and its main ingredients, i.e., hops, yeast, malting barley and other components. The research described in this paper deals with the multifaceted quality evaluation of malting barley needed for the production of malt. The project aims to elaborate on the original methodology used for identifying grain varieties, grain contamination degree and other visual characteristics of malting barley employing new computer technologies, including artificial intelligence (AI) and neural image analysis. The neural modelling and digital image analysis assist in identifying the quality of barley varieties. According to the study, information concerning the colour of barley varieties presented in digital images is sufficient for this purpose. The multi-layer perceptron (MLP)-type neural network generated using a data set describing the colour of kernels presented in digital images was the best model for recognising the analysed malting barley varieties. The proposed procedure may bring specific benefits to malthouses, influencing the beer production quality in the future.

show abstract

Neural classification of the selected family of butterflies

Cited by 3 publications

References 10 publications

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

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

Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN

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

Contact Info

Product

Resources

About