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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Piotr Boniecki & Maciej Zaborowicz & Agnieszka Pilarska & Hanna Piekarska-Boniecka, 2020. "Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN," Agriculture, MDPI, vol. 10(6), pages 1-9, June.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:6:p:218-:d:369566
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    References listed on IDEAS

    as
    1. Jun Sun & Xiaofei He & Xiao Ge & Xiaohong Wu & Jifeng Shen & Yingying Song, 2018. "Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background," Agriculture, MDPI, vol. 8(12), pages 1-15, December.
    2. Piotr Boniecki & Krzysztof Koszela & Krzysztof Świerczyński & Jacek Skwarcz & Maciej Zaborowicz & Jacek Przybył, 2020. "Neural Visual Detection of Grain Weevil ( Sitophilus granarius L.)," Agriculture, MDPI, vol. 10(1), pages 1-9, January.
    3. Dean C. J. Rice & Rupp Carriveau & David S. -K. Ting & Mo’tamad H. Bata, 2017. "Evaluation of Crop to Crop Water Demand Forecasting: Tomatoes and Bell Peppers Grown in a Commercial Greenhouse," Agriculture, MDPI, vol. 7(12), pages 1-14, December.
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    Cited by:

    1. Dana Čirjak & Ivan Aleksi & Ivana Miklečić & Ana Marija Antolković & Rea Vrtodušić & Antonio Viduka & Darija Lemic & Tomislav Kos & Ivana Pajač Živković, 2022. "Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    2. Agnieszka A. Pilarska & Piotr Boniecki & Małgorzata Idzior-Haufa & Maciej Zaborowicz & Krzysztof Pilarski & Andrzej Przybylak & Hanna Piekarska-Boniecka, 2021. "Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling," Agriculture, MDPI, vol. 11(8), pages 1-11, August.
    3. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    4. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.

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