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

Piotr Boniecki, Maciej Zaborowicz, Agnieszka Pilarska and Hanna Piekarska-Boniecka
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Piotr Boniecki: Institute of Biosystems Engineering, Poznan University of Life Sciences, 60-637 Poznan, Poland
Maciej Zaborowicz: Institute of Biosystems Engineering, Poznan University of Life Sciences, 60-637 Poznan, Poland
Agnieszka Pilarska: Food Engineering Group, Institute of Plant Origin Food Technology, Poznan University of Life Sciences, 60-637 Poznan, Poland
Hanna Piekarska-Boniecka: Faculty of Horticulture and Landscape Architecture, Poznan University of Life Sciences, 60-637 Poznan, Poland

Agriculture, 2020, vol. 10, issue 6, 1-9

Abstract: 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.

Keywords: artificial neural networks; identification of apple pests; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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