IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0223906.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223906
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0223906&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0223906?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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.

    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. Anna‐Katharina Hornidge & Lisa Oberkircher & Bernhard Tischbein & Gunther Schorcht & Anik Bhaduri & Ahmad M. Manschadi, 2011. "Reconceptualizing water management in Khorezm, Uzbekistan," Natural Resources Forum, Blackwell Publishing, vol. 35(4), pages 251-268, November.
    2. Corbari, Chiara & Paciolla, Nicola & Rossi, Greta & Mancini, Marco, 2023. "A double two-sources energy-water balance model for improving evapotranspiration estimates and irrigation management in fruit trees fields," Agricultural Water Management, Elsevier, vol. 289(C).
    3. Martin de Santa Olalla, F. & Calera, A. & Dominguez, A., 2003. "Monitoring irrigation water use by combining Irrigation Advisory Service, and remotely sensed data with a geographic information system," Agricultural Water Management, Elsevier, vol. 61(2), pages 111-124, June.
    4. Bastiaanssen, W. G. M. & Chandrapala, L., 2003. "Water balance variability across Sri Lanka for assessing agricultural and environmental water use," Agricultural Water Management, Elsevier, vol. 58(2), pages 171-192, February.
    5. Muhammad Usman & Talha Mahmood & Christopher Conrad & Habib Ullah Bodla, 2020. "Remote Sensing and Modelling Based Framework for Valuing Irrigation System Efficiency and Steering Indicators of Consumptive Water Use in an Irrigated Region," Sustainability, MDPI, vol. 12(22), pages 1-33, November.
    6. Yongqing Zhao & Rendong Li & Juan Qiu & Xiangdong Sun & Lu Gao & Mingquan Wu, 2019. "Prediction of Human Brucellosis in China Based on Temperature and NDVI," IJERPH, MDPI, vol. 16(21), pages 1-15, November.
    7. Xiaoxiao Li & Man Yu & Jing Ma & Zhanbin Luo & Fu Chen & Yongjun Yang, 2018. "Identifying the Relationship between Soil Properties and Rice Growth for Improving Consolidated Land in the Yangtze River Delta, China," Sustainability, MDPI, vol. 10(9), pages 1-14, August.
    8. Yotsaphat Kittichotsatsawat & Varattaya Jangkrajarng & Korrakot Yaibuathet Tippayawong, 2021. "Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies," Sustainability, MDPI, vol. 13(8), pages 1-20, April.
    9. Yonela Mndela & Naledzani Ndou & Adolph Nyamugama, 2023. "Irrigation Scheduling for Small-Scale Crops Based on Crop Water Content Patterns Derived from UAV Multispectral Imagery," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    10. Santiago Castaño & David Sanz & Juan Gómez-Alday, 2010. "Methodology for Quantifying Groundwater Abstractions for Agriculture via Remote Sensing and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(4), pages 795-814, March.
    11. Er-Raki, S. & Chehbouni, A. & Guemouria, N. & Duchemin, B. & Ezzahar, J. & Hadria, R., 2007. "Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region," Agricultural Water Management, Elsevier, vol. 87(1), pages 41-54, January.
    12. Jongschaap, Raymond E.E., 2007. "Sensitivity of a crop growth simulation model to variation in LAI and canopy nitrogen used for run-time calibration," Ecological Modelling, Elsevier, vol. 200(1), pages 89-98.
    13. Hemakumara, H. M. & Chandrapala, Lalith & Moene, Arnold F., 2003. "Evapotranspiration fluxes over mixed vegetation areas measured from large aperture scintillometer," Agricultural Water Management, Elsevier, vol. 58(2), pages 109-122, February.
    14. Hamze, Mohamad & Cheviron, Bruno & Baghdadi, Nicolas & Lo, Madiop & Courault, Dominique & Zribi, Mehrez, 2023. "Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model," Agricultural Water Management, Elsevier, vol. 283(C).
    15. Nanda, Arunav & Das, Narendra & Singh, Gurjeet & Bindlish, Rajat & Andreadis, Konstantinos M. & Jayasinghe, Susantha, 2024. "Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture," Agricultural Water Management, Elsevier, vol. 300(C).
    16. Elshaikh, Ahmed E. & Jiao, Xiyun & Yang, Shi-hong, 2018. "Performance evaluation of irrigation projects: Theories, methods, and techniques," Agricultural Water Management, Elsevier, vol. 203(C), pages 87-96.
    17. Folhes, M.T. & Rennó, C.D. & Soares, J.V., 2009. "Remote sensing for irrigation water management in the semi-arid Northeast of Brazil," Agricultural Water Management, Elsevier, vol. 96(10), pages 1398-1408, October.
    18. Abdul-Wadood Moomen & Lily Lisa Yevugah & Louvis Boakye & Jeff Dacosta Osei & Francis Muthoni, 2024. "Review of Applications of Remote Sensing towards Sustainable Agriculture in the Northern Savannah Regions of Ghana," Agriculture, MDPI, vol. 14(4), pages 1-22, March.
    19. Alessandro Scandiffio, 2021. "Parametric Definition of Slow Tourism Itineraries for Experiencing Seasonal Landscapes. Application of Sentinel-2 Imagery to the Rural Paddy-Rice Landscape in Northern Italy," Sustainability, MDPI, vol. 13(23), pages 1-15, November.
    20. Christos Zoumides & Adriana Bruggeman & Theodoros Zachariadis & Stelios Pashiardis, 2013. "Quantifying the Poorly Known Role of Groundwater in Agriculture: the Case of Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2501-2514, May.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0223906. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.