IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p8151-d855398.html
   My bibliography  Save this article

Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China

Author

Listed:
  • Yu Wang

    (Karst Research Institute, Guizhou Normal University, Guiyang 550001, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
    National Engineering Technology Research Center for Karst Rocky Desertification Control, Guiyang 550001, China)

  • Zhongfa Zhou

    (Karst Research Institute, Guizhou Normal University, Guiyang 550001, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
    National Engineering Technology Research Center for Karst Rocky Desertification Control, Guiyang 550001, China)

  • Denghong Huang

    (Karst Research Institute, Guizhou Normal University, Guiyang 550001, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
    National Engineering Technology Research Center for Karst Rocky Desertification Control, Guiyang 550001, China)

  • Tian Zhang

    (Karst Research Institute, Guizhou Normal University, Guiyang 550001, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
    National Engineering Technology Research Center for Karst Rocky Desertification Control, Guiyang 550001, China)

  • Wenhui Zhang

    (Karst Research Institute, Guizhou Normal University, Guiyang 550001, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
    National Engineering Technology Research Center for Karst Rocky Desertification Control, Guiyang 550001, China)

Abstract

Refined tobacco plant information extraction is the basis of efficient yield estimation. Tobacco planting in mountainous plateau areas in China is characterized by scattered distribution, uneven growth, and mixed/intercropping crops. Thus, it is difficult to accurately extract information on the tobacco plants. The study area is Beipanjiang topographic fracture area in China, using the smart phantom 4 Pro v2.0 quadrotor unmanned aerial vehicle to collect the images of tobacco planting area in the study area. By screening the visible light band, Excess Green Index, Normalized Green Red Difference Vegetation Index, and Excess Green Minus Excess Red Index were used to obtain the best color index calculation method for tobacco plants. Low-pass filtering was used to enhance tobacco plant information and suppress noise from weeds, corn plants, and rocks. Combined with field measurements of tobacco plant data, the computer interactive interpretation method performed gray-level segmentation on the enhanced image and extracted tobacco plant information. This method is suitable for identifying tobacco plants in mountainous plateau areas. The detection rates of the test and verification areas were 96.61% and 97.69%, and the completeness was 95.66% and 96.53%, respectively. This study can provide fine data support for refined tobacco plantation management in the terrain broken area with large exposed rock area and irregular planting land.

Suggested Citation

  • Yu Wang & Zhongfa Zhou & Denghong Huang & Tian Zhang & Wenhui Zhang, 2022. "Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8151-:d:855398
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/8151/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/13/8151/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Wang & Xue Gao & Yukun Cheng & Yi Ren & Zhihui Zhang & Rui Wang & Junmei Cao & Hongwei Geng, 2022. "QTL Mapping of Leaf Area Index and Chlorophyll Content Based on UAV Remote Sensing in Wheat," Agriculture, MDPI, vol. 12(5), pages 1-19, April.
    2. Héctor García-Martínez & Héctor Flores-Magdaleno & Roberto Ascencio-Hernández & Abdul Khalil-Gardezi & Leonardo Tijerina-Chávez & Oscar R. Mancilla-Villa & Mario A. Vázquez-Peña, 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-24, July.
    Full references (including those not matched with items on IDEAS)

    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. Dejan Ranković & Goran Todorović & Marijenka Tabaković & Slaven Prodanović & Jan Boćanski & Nenad Delić, 2021. "Direct and Joint Effects of Genotype, Defoliation and Crop Density on the Yield of Three Inbred Maize Lines," Agriculture, MDPI, vol. 11(6), pages 1-14, May.
    2. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    3. Pompilica Iagăru & Pompiliu Pavel & Romulus Iagăru & Anca Șipoș, 2022. "Aerial Monitorization—A Vector for Ensuring the Agroecosystems Sustainability," Sustainability, MDPI, vol. 14(10), pages 1-12, May.
    4. Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    5. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    6. Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    7. Flavio Borfecchia & Paola Crinò & Angelo Correnti & Anna Farneti & Luigi De Cecco & Domenica Masci & Luciano Blasi & Domenico Iantosca & Vito Pignatelli & Carla Micheli, 2020. "Assessing the Impact of Water Salinization Stress on Biomass Yield of Cardoon Bio-Energetic Crops through Remote Sensing Techniques," Resources, MDPI, vol. 9(10), pages 1-27, October.
    8. Romeu Gerardo & Isabel P. de Lima, 2023. "Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal," Agriculture, MDPI, vol. 13(10), pages 1-18, September.

    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:jsusta:v:14:y:2022:i:13:p:8151-:d:855398. 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.