Acta Scientific Agriculture (ASAG)(ISSN: 2581-365X)

Short Communication Volume 6 Issue 4

Evaluation of Multispectral Sensors for Assessing Pigment Index in Soybean

Harman Singh Sangha1*, Ajay Sharda1, William Schapaugh2 and Dylan Walta2

1Carl and Melinda Helwig Department of Biological and Agricultural Engineering, Kansas State University, USA
2Department of Agronomy, Kansas State University, USA

*Corresponding Author: Harman Singh Sangha, Carl and Melinda Helwig Department of Biological and Agricultural Engineering, Kansas State University, USA.

Received: February 01, 2022; Published: March 11, 2022

Abstract

The collection of image-based data for vegetative crops is advancing with the introduction of vigorous and lightweight camera sensors. Among these camera sensors, multispectral sensors are prevalent with researchers due to the wide range of functions that can be performed. Multispectral sensors are carefully chosen according to their capacity to recognize specific wavelengths, and specifications such as focal length, sensor size, and radiometric resolution is often overlooked. Therefore, a study was performed to evaluate two forms of multispectral sensors for assessing pigment index by examining correlation between sensor output and ground parameters. A narrowband and broadband sensor was used to collect spectral data on a soybean field using a quadcopter. Spectral data was evaluated based on ground resolution, orthomosaic quality, along with statistical comparison with agronomical data (wilting scores and maturity). The broadband sensor had a better ability to capture comprehensive spatial data than the narrowband sensor. The broadband sensor was highly correlated with soybean maturity (r = 0.83, p ≤ 0.001). Wilting scores collected were of wide resolution as compared to spectral data. Narrow resolution ground data can verify that pigment index can be used as crop parameters. The Narrowband sensor was limited in estimating pigment index due to smaller sensor size and restricted spectral bands.

Keywords: Multispectral Sensors; Orthomosaic; Pigment-Index; Precision Agriculture

References

  1. MC Hunter., et al. “Agriculture in 2050: Recalibrating Targets for Sustainable Intensification”. Bioscience4 (2017).
  2. R Duesterhaus. “The SWCS view: Sustainability’s promise”. Journal of Soil and Water Conservation 1 (1990): 4.
  3. R Gebbers and VI Adamchuk. “Precision Agriculture and Food Security”. Science 5967 (2010): 828-831.
  4. S Khanal., et al. “An overview of current and potential applications of thermal remote sensing in precision agriculture”. Computers and Electronics in Agriculture Elsevier B.V (2017): 22-32.
  5. A Mac Arthur and I Robinson. “A critique of field spectroscopy and the challenges and opportunities it presents for remote sensing for agriculture, ecosystems, and hydrology”. in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII 9637 (2015): 963705.
  6. JD Rudd., et al. “Application of satellite, unmanned aircraft system, and ground-based sensor data for precision agriculture: A review” (2017).
  7. C Zhang., et al. “The application of small unmanned aerial systems for precision agriculture: a review”. Agric 13 (2012): 693-712.
  8. JAJ Berni., et al. “Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle”. IEEE Transactions on Geoscience and Remote Sensing 3 (2009): 722-738.
  9. J Baluja., et al. “Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)”. Irrigation Science 6 (2012): 511-522.
  10. S Candiago., et al. “Evaluating multispectral images and vegetation indices for precision farming applications from UAV images”. Remote Sensing 4 (2015): 4026-4047.
  11. M Zaman-Allah., et al. “Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize”. Plant Methods1 (2015): 1-10.
  12. J Senthilnath., et al. “Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods”. Computers and Electronics in Agriculture 140 (2017): 8-24.
  13. J Bendig., et al. “Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley”. The International Journal of Applied Earth Observation and Geoinformation 39 (2015): 79-87.
  14. S Park., et al. “Estimation of crop water stress in a nectarine orchard using high-resolution imagery from unmanned aerial vehicle (UAV)”. in Proceedings of the 21st International Congress on Modelling and Simulation, Gold Coast, Australia (2015): 29.
  15. S Park., et al. “Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard”. Remote Sensing 14 (2021): 2775.
  16. M Awais., et al. “UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review”. International Journal of Environmental Science and Technology 2021 (2022): 1-18.
  17. D Kingston and R Beard. “Real-time attitude and position estimation for small UAVs using low-cost sensors”. in AIAA 3rd" Unmanned Unlimited" Technical Conference, Workshop and Exhibit (2004): 6488.
  18. B Majidi and A Bab-Hadiashar. “Real time aerial natural image interpretation for autonomous ranger drone navigation”. in Digital Image Computing: Techniques and Applications (DICTA’05) (2005): 65.
  19. PJ Zarco-Tejada., et al. “Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy”. Remote Sensing of Environment3 (2005): 271-287.
  20. L Deng., et al. “UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras”. ISPRS Journal of Photogrammetry and Remote Sensing 146 (2018): 124-136.
  21. D Zhao., et al. “A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy”. ISPRS Journal of Photogrammetry and Remote Sensing 1 (2007): 25-33.
  22. HS Sangha., et al. “Impact of camera focal length and sUAS flying altitude on spatial crop canopy temperature evaluation”. Computers and Electronics in Agriculture 172 (2020): 105344.
  23. “2018 Kansas Performance Tests with soybean varieties”. Report of Progress 1146. Manhattan (2018).
  24. “2019 Kansas Performance Tests with soybean varieties”. Report of Progress 1153. Manhattan (2019).
  25. CA King., et al. “Differential wilting among soybean genotypes in response to water deficit”. Crop Science 1 (2009): 290-298.
  26. WR Fehr., et al. “Stage of development descriptions for soybeans, Glycine Max (L.) Merrill 1”. Crop Science 6 (1971): 929-931.
  27. T Hengl. “Finding the right pixel size”. Computational Geosciences 9 (2006): 1283-1298.
  28. BER Hodecker., et al. “Water availability preceding long-term drought defines the tolerance of Eucalyptus to water restriction”. New For2 (2018): 173-195.
  29. SH Kim., et al. “Down-regulation of β-carotene hydroxylase increases β-carotene and total carotenoids enhancing salt stress tolerance in transgenic cultured cells of sweetpotato”. Phytochemistry 74 (2012): 69-78.
  30. EW Chappelle., et al. “Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves”. Remote Sensing of Environment 3 (1992): 239-247.
  31. “LED light spectrum 101: Absorption spectra” (2014).
  32. AA Gitelson., et al. “Use of a green channel in remote sensing of global vegetation from EOS-MODIS”. Remote Sensing of Environment 3 (1996): 289-298.
  33. NM Hatton. “Use of small unmanned aerial system for validation of sudden death syndrome in soybean through multispectral and thermal remote sensing”. Environmental Science (2018).
  34. C Lu and J Zhang. “Changes in photosystem II function during senescence of wheat leaves”. Physiologia Plantarum 2 (1998): 239-247.

Citation

Citation: Harman Singh Sangha., et al. “Evaluation of Multispectral Sensors for Assessing Pigment Index in Soybean”. Acta Scientific Agriculture 6.4 (2022): 13-23.

Copyright

Copyright: © 2022 Harman Singh Sangha., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Acceptance rate32%
Acceptance to publication20-30 days
Impact Factor1.014

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