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Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)

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  • Bipul Neupane
  • Teerayut Horanont
  • Nguyen Duy Hung

Abstract

The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.

Suggested Citation

  • Bipul Neupane & Teerayut Horanont & Nguyen Duy Hung, 2019. "Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
  • Handle: RePEc:plo:pone00:0223906
    DOI: 10.1371/journal.pone.0223906
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    References listed on IDEAS

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    1. Bastiaanssen, Wim G. M. & Molden, David J. & Makin, Ian W., 2000. "Remote sensing for irrigated agriculture: examples from research and possible applications," Agricultural Water Management, Elsevier, vol. 46(2), pages 137-155, December.
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    1. Kilwenge, Regina & Adewopo, Julius & Sun, Zhanli & Schut, Marc, 2021. "UAV-based mapping of banana land area for village-level decision-support in Rwanda," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 13(24).
    2. Xinni Liu & Kamarul H. Ghazali & Akeel A. Shah, 2022. "Sustainable Oil Palm Resource Assessment Based on an Enhanced Deep Learning Method," Energies, MDPI, vol. 15(12), pages 1-14, June.
    3. Eggimann, Sven, 2022. "Expanding urban green space with superblocks," Land Use Policy, Elsevier, vol. 117(C).
    4. Angelica Christina Melo Nunes Astolfi & Gilberto Astolfi & Maria Gabriela Alves Ferreira & Thaynara D’avalo Centurião & Leyzinara Zenteno Clemente & Bruno Leonardo Marques Castro de Oliveira & João Vi, 2021. "Recognizing and counting Dendrocephalus brasiliensis (Crustacea: Anostraca) cysts using deep learning," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-15, March.

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