IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v287y2023ics0378377423003207.html
   My bibliography  Save this article

Subsurface drainage pipe detection using an ensemble learning approach and aerial images

Author

Listed:
  • Woo, Dong Kook
  • Ji, Junghu
  • Song, Homin

Abstract

Subsurface drainage pipes are commonly used in the Midwestern United States to reduce excess soil moisture and improve crop yields. However, they are the considerable source of nonpoint pollution due to nutrient losses. Detecting the locations of drainage pipes is crucial for water quality management, but information about drainage pipe maps is often privately owned and unavailable. In this study, we propose an ensemble learning approach that uses eight fully convolutional networks (FCNs), including well-known architectures such as Unet, DenseNet, and Wnet, to detect subsurface drainage pipe locations from aerial images. Each FCN model is trained and validated using an aerial image dataset, taking a 256 × 256 × 3 pixel aerial image patch as input and outputting a pixel-wise drainage pipe detection map. Weighted averaging is then applied to the individual FCN outputs to create a unified drain pipe detection map. The performance of the proposed approach is evaluated using large-scale aerial image data that has not been used during the training and validation phases. The results demonstrate that the proposed approach provides accurate and more robust drain pipe detection over the case of using an individual FCN model. We further explore the effects of image resolution for the effective use of the proposed drain pipe detection approach.

Suggested Citation

  • Woo, Dong Kook & Ji, Junghu & Song, Homin, 2023. "Subsurface drainage pipe detection using an ensemble learning approach and aerial images," Agricultural Water Management, Elsevier, vol. 287(C).
  • Handle: RePEc:eee:agiwat:v:287:y:2023:i:c:s0378377423003207
    DOI: 10.1016/j.agwat.2023.108455
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423003207
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2023.108455?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Allred, Barry & Martinez, Luis & Fessehazion, Melake K. & Rouse, Greg & Koganti, Triven & Freeland, Robert & Eash, Neal & Wishart, DeBonne & Featheringill, Robert, 2021. "Time of day impact on mapping agricultural subsurface drainage systems with UAV thermal infrared imagery," Agricultural Water Management, Elsevier, vol. 256(C).
    2. Naz, B.S. & Ale, S. & Bowling, L.C., 2009. "Detecting subsurface drainage systems and estimating drain spacing in intensively managed agricultural landscapes," Agricultural Water Management, Elsevier, vol. 96(4), pages 627-637, April.
    3. Song, Homin & Woo, Dong Kook & Yan, Qina, 2021. "Detecting subsurface drainage pipes using a fully convolutional network with optical images," Agricultural Water Management, Elsevier, vol. 249(C).
    4. Takahiro Oga & Ryosuke Harakawa & Sayaka Minewaki & Yo Umeki & Yoko Matsuda & Masahiro Iwahashi, 2020. "River state classification combining patch-based processing and CNN," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-14, December.
    5. Allred, Barry & Martinez, Luis & Fessehazion, Melake K. & Rouse, Greg & Williamson, Tanja N. & Wishart, DeBonne & Koganti, Triven & Freeland, Robert & Eash, Neal & Batschelet, Adam & Featheringill, Ro, 2020. "Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes," Agricultural Water Management, Elsevier, vol. 232(C).
    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. Carlsen, Ask Holm & Fensholt, Rasmus & Looms, Majken Caroline & Gominski, Dimitri & Stisen, Simon & Jepsen, Martin Rudbeck, 2024. "Systematic review of the detection of subsurface drainage systems in agricultural fields using remote sensing systems," Agricultural Water Management, Elsevier, vol. 299(C).

    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. Carlsen, Ask Holm & Fensholt, Rasmus & Looms, Majken Caroline & Gominski, Dimitri & Stisen, Simon & Jepsen, Martin Rudbeck, 2024. "Systematic review of the detection of subsurface drainage systems in agricultural fields using remote sensing systems," Agricultural Water Management, Elsevier, vol. 299(C).
    2. Allred, Barry & Martinez, Luis & Khanal, Sami & Sawyer, Audrey H. & Rouse, Greg, 2022. "Subsurface drainage outlet detection in ditches and streams with UAV thermal infrared imagery: Preliminary research," Agricultural Water Management, Elsevier, vol. 271(C).
    3. Allred, Barry & Martinez, Luis & Fessehazion, Melake K. & Rouse, Greg & Koganti, Triven & Freeland, Robert & Eash, Neal & Wishart, DeBonne & Featheringill, Robert, 2021. "Time of day impact on mapping agricultural subsurface drainage systems with UAV thermal infrared imagery," Agricultural Water Management, Elsevier, vol. 256(C).
    4. Song, Homin & Woo, Dong Kook & Yan, Qina, 2021. "Detecting subsurface drainage pipes using a fully convolutional network with optical images," Agricultural Water Management, Elsevier, vol. 249(C).
    5. Allred, Barry & Eash, Neal & Freeland, Robert & Martinez, Luis & Wishart, DeBonne, 2018. "Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study," Agricultural Water Management, Elsevier, vol. 197(C), pages 132-137.
    6. Emaad Ansari & Mohammad Nishat Akhtar & Mohamad Nazir Abdullah & Wan Amir Fuad Wajdi Othman & Elmi Abu Bakar & Ahmad Faizul Hawary & Syed Sahal Nazli Alhady, 2021. "Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    7. Woo, Dong Kook & Song, Homin & Kumar, Praveen, 2019. "Mapping subsurface tile drainage systems with thermal images," Agricultural Water Management, Elsevier, vol. 218(C), pages 94-101.
    8. Deuss, Kirstin Ella & Almond, Peter C. & Carrick, Sam & Kees, Lawrence John, 2023. "Identification, mapping, and characterisation of a mature artificial mole channel network using ground-penetrating radar," Agricultural Water Management, Elsevier, vol. 288(C).
    9. Ale, S. & Bowling, L.C. & Owens, P.R. & Brouder, S.M. & Frankenberger, J.R., 2012. "Development and application of a distributed modeling approach to assess the watershed-scale impact of drainage water management," Agricultural Water Management, Elsevier, vol. 107(C), pages 23-33.
    10. Allred, Barry & Martinez, Luis & Fessehazion, Melake K. & Rouse, Greg & Williamson, Tanja N. & Wishart, DeBonne & Koganti, Triven & Freeland, Robert & Eash, Neal & Batschelet, Adam & Featheringill, Ro, 2020. "Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes," Agricultural Water Management, Elsevier, vol. 232(C).
    11. Tlapáková Lenka, 2017. "Development of drainage system in the Czech landscape – identification and functionality assessment by means of remote sensing," European Countryside, Sciendo, vol. 9(1), pages 77-98, March.
    12. Kratt, C.B. & Woo, D.K. & Johnson, K.N. & Haagsma, M. & Kumar, P. & Selker, J. & Tyler, S., 2020. "Field trials to detect drainage pipe networks using thermal and RGB data from unmanned aircraft," Agricultural Water Management, Elsevier, vol. 229(C).

    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:eee:agiwat:v:287:y:2023:i:c:s0378377423003207. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.