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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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