IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1668-d1223912.html
   My bibliography  Save this article

Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology

Author

Listed:
  • Sellaperumal Pazhanivelan

    (Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641003, India)

  • Ramalingam Kumaraperumal

    (Department of RS and GIS, Tamil Nadu Agricultural University, Coimbatore 641003, India)

  • P. Shanmugapriya

    (Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641003, India)

  • N. S. Sudarmanian

    (Krishi Vigyan Kendra, Aruppukottai 626107, India)

  • A. P. Sivamurugan

    (Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641003, India)

  • S. Satheesh

    (Department of RS and GIS, Tamil Nadu Agricultural University, Coimbatore 641003, India)

Abstract

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were used to assess crop phenology, condition, and stress by profiling spectral vegetation indices in crop fields. In this study, vegetation indices, viz ., Atmospherically Resistant Vegetation Index (ARVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Wide Dynamic Range Vegetation Index (WDRVI), Normalized Red–Green Difference Index (NGRDI), Excess Green Index (ExG), Red–Green Blue Vegetation Index (RGBVI), and Visible Atmospherically Resistant Index (VARI) were generated. Furthermore, Pearson correlation analysis showed a better correlation between WDRVI and VARI with LAI (R = 0.955 and R = 0.982) ground truth data. In contrast, a strong correlation (R = 0.931 and R = 0.844) was recorded with MCARI and NGRDI with SPAD chlorophyll ground truth data. Then, the best-performing indices, WDRVI and MCARI in cotton, and VARI and NGRDI in rice, were further used to generate the yield model. This study for determining LAI and chlorophyll shows that high spatial resolution drone imageries are accurate and fast. As a result, finding out the LAI and chlorophyll and how they affect crop yield at a regional scale is helpful. The widespread use of unmanned aerial vehicles (UAV) and yield prediction were technical components of large-scale precision agriculture.

Suggested Citation

  • Sellaperumal Pazhanivelan & Ramalingam Kumaraperumal & P. Shanmugapriya & N. S. Sudarmanian & A. P. Sivamurugan & S. Satheesh, 2023. "Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology," Agriculture, MDPI, vol. 13(9), pages 1-16, August.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1668-:d:1223912
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1668/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/13/9/1668/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pradosh Kumar Parida & Eagan Somasundaram & Ramanujam Krishnan & Sengodan Radhamani & Uthandi Sivakumar & Ettiyagounder Parameswari & Rajagounder Raja & Silambiah Ramasamy Shri Rangasami & Sundapalaya, 2024. "Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize," Agriculture, MDPI, vol. 14(7), pages 1-21, July.

    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:jagris:v:13:y:2023:i:9:p:1668-:d:1223912. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.