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Open AccessArticle

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

1
Institute of Biosystems Engineering, Poznan University of Life Sciences, 60-637 Poznan, Poland
2
Food Engineering Group, Institute of Plant Origin Food Technology, Poznan University of Life Sciences, 60-637 Poznan, Poland
3
Faculty of Horticulture and Landscape Architecture, Poznan University of Life Sciences, 60-637 Poznan, Poland
*
Author to whom correspondence should be addressed.
Agriculture 2020, 10(6), 218; https://doi.org/10.3390/agriculture10060218
Received: 28 May 2020 / Accepted: 9 June 2020 / Published: 10 June 2020
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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. View Full-Text
Keywords: artificial neural networks; identification of apple pests; deep learning artificial neural networks; identification of apple pests; deep learning
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Boniecki, P.; Zaborowicz, M.; Pilarska, A.; Piekarska-Boniecka, H. Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN. Agriculture 2020, 10, 218.

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