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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
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    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 1-20, July.

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