IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i6p218-d369566.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/6/218/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/6/218/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng Wang & Jiang Liu & Lijia Xu & Peng Huang & Xiong Luo & Yan Hu & Zhiliang Kang, 2021. "Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    2. Penglong Wang & Yao Wei & Fanglei Zhong & Xiaoyu Song & Bao Wang & Qinhua Wang, 2022. "Evaluation of Agricultural Water Resources Carrying Capacity and Its Influencing Factors: A Case Study of Townships in the Arid Region of Northwest China," Agriculture, MDPI, vol. 12(5), pages 1-24, May.
    3. Weidong Zhu & Jun Sun & Simin Wang & Jifeng Shen & Kaifeng Yang & Xin Zhou, 2022. "Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
    4. Bożena Kordan & Mariusz Nietupski & Emilia Ludwiczak & Beata Gabryś & Robert Cabaj, 2023. "Selected Cultivar-Specific Parameters of Wheat Grain as Factors Influencing Intensity of Development of Grain Weevil Sitophilus granarius (L.)," Agriculture, MDPI, vol. 13(8), pages 1-13, July.
    5. Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
    6. Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.
    7. Andrzej Przybylak & Radosław Kozłowski & Ewa Osuch & Andrzej Osuch & Piotr Rybacki & Przemysław Przygodziński, 2020. "Quality Evaluation of Potato Tubers Using Neural Image Analysis Method," Agriculture, MDPI, vol. 10(4), pages 1-11, April.
    8. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    9. 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.
    10. Peng Wang & Tong Niu & Dongjian He, 2021. "Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism," Agriculture, MDPI, vol. 11(11), pages 1-13, October.
    11. Lan, Hai & Zheng, Puyang & Li, Zheng, 2021. "Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    12. Chung-Liang Chang & Bo-Xuan Xie & Sheng-Cheng Chung, 2021. "Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm," Agriculture, MDPI, vol. 11(11), pages 1-21, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:10:y:2020:i:6:p:218-:d:369566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.