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Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Author

Listed:
  • Sanaz Shafian
  • Nithya Rajan
  • Ronnie Schnell
  • Muthukumar Bagavathiannan
  • John Valasek
  • Yeyin Shi
  • Jeff Olsenholler

Abstract

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April–October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for within-season data collection of agricultural crops such as sorghum.

Suggested Citation

  • Sanaz Shafian & Nithya Rajan & Ronnie Schnell & Muthukumar Bagavathiannan & John Valasek & Yeyin Shi & Jeff Olsenholler, 2018. "Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
    • Handle: RePEc:plo:pone00:0196605
    DOI: 10.1371/journal.pone.0196605
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    Cited by:

    1. Tailin Li & Massimiliano Schiavo & David Zumr, . "Seasonal variations of vegetative indices and their correlation with evapotranspiration and soil water storage in a small agricultural catchment," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 0.
    2. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    3. Pappalardo, S. & Consoli, S. & Longo-Minnolo, G. & Vanella, D. & Longo, D. & Guarrera, S. & D’Emilio, A. & Ramírez-Cuesta, J.M., 2023. "Performance evaluation of a low-cost thermal camera for citrus water status estimation," Agricultural Water Management, Elsevier, vol. 288(C).
    4. Antonis V. Papadopoulos & Dionissios P. Kalivas, 2021. "Assessing Soil and Crop Characteristics at Sub-Field Level Using Unmanned Aerial System and Geospatial Analysis," Sustainability, MDPI, vol. 13(5), pages 1-24, March.
    5. Tailin Li & Massimiliano Schiavo & David Zumr, 2023. "Seasonal variations of vegetative indices and their correlation with evapotranspiration and soil water storage in a small agricultural catchment," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(4), pages 246-268.
    6. Luis Vargas Tamayo & Christopher Thron & Jean Louis Kedieng Ebongue Fendji & Shauna-Kay Thomas & Anna Förster, 2020. "Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range," Sustainability, MDPI, vol. 12(21), pages 1-25, October.
    7. Peroni Venancio, Luan & Chartuni Mantovani, Everardo & do Amaral, Cibele Hummel & Usher Neale, Christopher Michael & Zution Gonçalves, Ivo & Filgueiras, Roberto & Coelho Eugenio, Fernando, 2020. "Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction," Agricultural Water Management, Elsevier, vol. 236(C).

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