Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN
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 class...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/10/6/218 |
| _version_ | 1797565679322267648 |
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| author | Piotr Boniecki Maciej Zaborowicz Agnieszka Pilarska Hanna Piekarska-Boniecka |
| author_facet | Piotr Boniecki Maciej Zaborowicz Agnieszka Pilarska Hanna Piekarska-Boniecka |
| author_sort | Piotr Boniecki |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:16:26Z |
| format | Article |
| id | doaj.art-dfb0027270084b679ca6f72c26e65c75 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T19:16:26Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-dfb0027270084b679ca6f72c26e65c752023-11-20T03:24:26ZengMDPI AGAgriculture2077-04722020-06-0110621810.3390/agriculture10060218Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNNPiotr Boniecki0Maciej Zaborowicz1Agnieszka Pilarska2Hanna Piekarska-Boniecka3Institute of Biosystems Engineering, Poznan University of Life Sciences, 60-637 Poznan, PolandInstitute of Biosystems Engineering, Poznan University of Life Sciences, 60-637 Poznan, PolandFood Engineering Group, Institute of Plant Origin Food Technology, Poznan University of Life Sciences, 60-637 Poznan, PolandFaculty of Horticulture and Landscape Architecture, Poznan University of Life Sciences, 60-637 Poznan, PolandIn 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.https://www.mdpi.com/2077-0472/10/6/218artificial neural networksidentification of apple pestsdeep learning |
| spellingShingle | Piotr Boniecki Maciej Zaborowicz Agnieszka Pilarska Hanna Piekarska-Boniecka Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN Agriculture artificial neural networks identification of apple pests deep learning |
| title | Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN |
| title_full | Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN |
| title_fullStr | Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN |
| title_full_unstemmed | Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN |
| title_short | Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN |
| title_sort | identification process of selected graphic features apple tree pests by neural models type mlp rbf and dnn |
| topic | artificial neural networks identification of apple pests deep learning |
| url | https://www.mdpi.com/2077-0472/10/6/218 |
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