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Plant Science Today (PST)

Research Articles

Vol. 11 No. 3 (2024)

Rice yield prediction through drone-derived vegetation indices: A case study in Tamil Nadu, India

DOI
https://doi.org/10.14719/pst.4521
Submitted
1 August 2024
Published
16-09-2024 — Updated on 20-09-2024
Versions

Abstract

Precision farming has been revolutionized by advancements in drone technology and remote sensing, enabling high accuracy in real-time crop monitoring and yield prediction. To explore the potential of drone-based remote sensing for predicting the rice yield by the assessment of vegetation indices were generated and analyzed to identify the most sensitive indices for predicting Laef Area Index (LAI), chlorophyll content, and biomass. The experiment was conducted in two seasons, Kuruvai (July - November 2023) and Navarai (December 2023 - March 2024). In Kuruvai 2023, a positive correlation was observed between vegetation indices, Wide Dynamic Range Vegetation Index (WDRVI), Modified Chlorophyll and Reflectance Index (MCARI) and Modified Soil Adjusted Vegetation Index (MSAVI) with ground truth biophysical parameters, while Navarai season Perpendicular Vegetation Index (PVI), Modified Chlorophyll and Reflectance Index (MCARI) and Green Normalized Difference Vegetation Index (GNDVI) exhibited the highest positive correlation. The multiple linear regression analysis revealed that a combined model incorporating LAI, SPAD chlorophyll, and biomass registered the highest R2 values of 0.792 and 0.800 for the Kuruvai and Navarai seasons. The predicted yield was positively correlated with the real-time yield with R2 values of 0.819 and 0.803 for both seasons. This study underscores the potential of drone-based VIs for precise yield prediction, offering a scalable and non-destructive method to enhance agricultural productivity and support decision-making in precision farming. Future research should focus on refining these models for broader applications across crops and agro-climatic conditions.

References

  1. Pazhanivelan S, Geethalakshmi V, Tamilmounika R, Sudarmanian NS, et al. Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite-derived products with DSSAT crop simulation model. Agronomy. 2022;12(9):2008. https://doi.org/10.3390/agronomy12092008
  2. Tamilmounika R, Pazhanivelan S, Ragunath KP, Sivamurugan AP, et al. Paddy area estimation in Cauvery Delta Region Using Synthetic Aperture Radar. International Journal of Environment, Ecology and Conservation. 2022;S517-22. http://doi.org/10.53550/EEC.2022.v28i01s.069
  3. De Castro AI, Shi Y, Maja JM, Peña JM. UAVs for vegetation monitoring: Overview and recent scientific contributions. Remote Sensing. 2021;13(11):2139. https://doi.org/10.3390/rs13112139
  4. Kazemi F, Ghanbari Parmehr E. Evaluation of RGB vegetation indices derived from UAV images for rice crop growth monitoring. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023;10:385-90. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-385-2023
  5. Marang IJ, Filippi P, Weaver TB, Evans BJ, Whelan BM, et al. Machine learning optimized hyperspectral remote sensing retrieves cotton nitrogen status. Remote Sensing. 2021;13(8):1428. https://doi.org/10.3390/rs13081428
  6. Boiarskii B, Hasegawa H. Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. Journal of mechanics of continua and mathematical sciences. 2019;4:20-9. https://doi.org/10.26782/jmcms.spl.4/2019.11.00003
  7. Shanmugapriya P, Latha KR, Pazhanivelan S, Kumaraperumal R, et al. Cotton yield prediction using drone derived LAI and chlorophyll content. Journal of Agrometeorology. 2022;24(4):348-52. https://doi.org/10.54386/jam.v24i4.1770
  8. Yue J, Feng H, Yang G, Li Z. A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sensing. 2018;10(1):66. https://doi.org/10.3390/rs10010066
  9. Delegido J, Verrelst J, Rivera JP, Ruiz-Verdú A, Moreno J. Brown and green LAI mapping through spectral indices. International Journal of Applied Earth Observation and Geoinformation. 2015;35:350-8. https://doi.org/10.1016/j.jag.2014.10.001
  10. Rouse Jr JW, Haas RH, Deering DW, Schell JA, Harlan JC. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Earth Resources And Remote Sensing 1974.
  11. Gitelson AA, Kaufman YJ, Merzlyak MN. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment. 1996;58(3):289-98. https://doi.org/10.1016/S0034-4257(96)00072-7
  12. Huete AR. A soil-adjusted vegetation index (SAVI). Remote sensing of Environment. 1988;25(3):295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  13. Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote sensing of Environment. 1991;35(2-3):161-73. https://doi.org/10.1016/0034-4257(91)90009-U
  14. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. A modified soil adjusted vegetation index. Remote sensing of Environment. 1994;48(2):119-26. https://doi.org/10.1016/0034-4257(94)90134-1
  15. Din M, Zheng W, Rashid M, Wang S, Shi Z. Evaluating hyperspectral vegetation indices for leaf area index estimation of Oryza sativa L. at diverse phenological stages. Frontiers in plant science. 2017;8:237162. https://doi.org/10.3389/fpls.2017.00820
  16. Wang W, Sun N, Bai B, Wu H, et al. Prediction of wheat SPAD using integrated multispectral and support vector machines. Frontiers in Plant Science. 2024;15:1405068. https://doi.org/10.3389/fpls.2024.1405068
  17. Vidican R, M?lina? A, Ranta O, Moldovan C, Marian O, et al. Using remote sensing vegetation indices for the discrimination and monitoring of agricultural crops: a critical review. Agronomy. 2023;13(12):3040. https://doi.org/10.3390/agronomy13123040
  18. Shanmugapriya P, Latha KR, Pazhanivelan S, Kumaraperumal R, et al. Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices. Current Science. 2022:1473-80. https://doi.org/10.18520/cs/v123/i12/1473-1480
  19. Palaniswamy KM, Gomez KA. Length-width method for estimating leaf area of rice 1. Agronomy Journal. 1974;66(3):430-3.
  20. Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P. Tree height quantification using very high-resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy. 2014;55:89-99. http://dx.doi.org/10.1016/j.eja.2014.01.004
  21. Cao J, Gu Z, Xu J, Duan Y, et al. Sensitivity analysis for leaf area index (LAI) estimation from CHRIS/PROBA data. Frontiers of Earth Science. 2014;8:405-13. http://dx.doi.org/10.1007/s11707-014-0432-0
  22. Mudereri BT, Dube T, Adel-Rahman EM, Niassy S, et al. A comparative analysis of planetscope and sentinel sentinel-2 space-borne sensors in mapping striga weed using guided regularised random forest classification ensemble. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019;42:701-8. http://dx.doi.org/10.5194/isprs-archives-XLII-2-W13-701-2019
  23. Gitelson AA. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology. 2004;161(2):165-73. https://doi.org/10.1078/0176-1617-01176
  24. Towers PC, Strever A, Poblete-Echeverría C. Comparison of vegetation indices for leaf area index estimation in vertical shoot positioned vine canopies with and without grenbiule hail-protection netting. Remote Sensing. 2019;11(9):1073. https://doi.org/10.3390/rs11091073
  25. Hunt Jr ER, Doraiswamy PC, McMurtrey JE, Daughtry CS, et al. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation. 2013;21:103-12.
  26. Friedman JM, Hunt Jr ER, Mutters RG. Assessment of leaf color chart observations for estimating maize chlorophyll content by analysis of digital photographs. Agronomy Journal. 2016;108(2):822-9. https://doi.org/10.2134/agronj2015.0258
  27. Taskos DG, Koundouras S, Stamatiadis S, Zioziou E, et al. Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity. Precision Agriculture. 2015;16:77-98. https://doi.org/10.1007/s11119-014-9363-8
  28. Shang J, Liu J, Ma B, Zhao T, Jiao X, et al. Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada. Remote Sensing of Environment. 2015;168:113-25. https://doi.org/10.1016/j.rse.2015.06.024
  29. Kanke Y, Tubana B, Dalen M, Harrell D. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision agriculture. 2016;17:507-30. https://doi.org/10.1007/s11119-016-9433-1
  30. Khan MS, Semwal M, Sharma A, Verma RK. An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI. Precision Agriculture. 2020;21:18-33. https://doi.org/10.1007/s11119-019-09655-9
  31. Ranjan R, Chandel AK, Khot LR, Bahlol HY, et al. Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture. 2019;6(4):502-14. https://doi.org/10.1016/j.inpa.2019.01.005
  32. Zhou L, Chen N, Chen Z, Xing C. ROSCC: an efficient remote sensing observation-sharing method based on cloud computing for soil moisture mapping in precision agriculture. IEEE Journal of selected topics in applied earth observations and remote sensing. 2016;9(12):5588-98. https://doi.org/10.1109/JSTARS.2016.2574810
  33. Zhang PP, Zhou XX, Wang ZX, Mao W, et al. Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat. Scientific Reports. 2020;10(1):5173. https://doi.org/10.1038/s41598-020-62125-5
  34. Lee DH, Shin HS, Park JH. Developing a p-NDVI map for highland kimchi cabbage using spectral information from UAVs and a field spectral radiometer. Agronomy. 2020;10(11):1798. https://doi.org/10.3390/agronomy10111798
  35. Ashapure A, Jung J, Chang A, Oh S, et al. A comparative study of RGB and multispectral sensor-based cotton canopy cover modelling using multi-temporal UAS data. Remote Sensing. 2019;11(23):2757. https://doi.org/10.3390/rs11232757
  36. Henebry GM, Viña A, Gitelson AA. The wide dynamic range vegetation index and its potential utility for gap analysis. 2004, 12, 50-56.
  37. Li M, Wu J, Song C, He Y, Niu B,et al. Temporal variability of precipitation and biomass of alpine grasslands on the northern Tibetan plateau. Remote Sensing. 2019;11(3):360. https://doi.org/10.3390/rs11030360
  38. Vélez S, Martínez-Peña R, Castrillo D. Beyond vegetation: A review unveiling additional insights into agriculture and forestry through the application of vegetation indices. J — Multidisciplinary Scientific Journal. 2023;6(3):421-36. https://doi.org/10.3390/j6030028
  39. Kaufman YJ, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing. 1992;30(2):261-70. https://doi.org/10.1109/36.134076
  40. Alves KS, Guimarães M, Ascari JP, Queiroz MF, Alfenas RF, et al. RGB-based phenotyping of foliar disease severity under controlled conditions. Tropical Plant Pathology. 1:1-3.
  41. Pazhanivelan S, Kumaraperumal R, Shanmugapriya P, Sudarmanian NS, et al. Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology. Agriculture. 2023;13(9):1668. https://doi.org/10.3390/agriculture13091668
  42. Utari D, Kamal M, Sidik F. Above-ground biomass estimation of mangrove forest using WorldView-2 imagery in Perancak Estuary, Bali. InIOP Conference Series: Earth and Environmental Science 2020;500(1):012011. https://doi.org/10.1088/1755-1315/500/1/012011
  43. Adak A, Murray SC, Božinovi? S, Lindsey R, Nakasagga S, et al. Temporal vegetation indices and plant height from remotely sensed imagery can predict grain yield and flowering time breeding value in maize via machine learning regression. Remote Sensing. 2021;13(11):2141. https://doi.org/10.3390/rs13112141
  44. Shamshiri RR, Mahadi MR, Ahmad D, Bejo SK, Aziz SA, et al. Controller design for an osprey drone to support precision agriculture research in oil palm plantations. In2017 ASABE Annual International Meeting 2017 (2-13).
  45. Gitelson AA, Viña A, Verma SB, Rundquist DC, Arkebauer TJ, et al. Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity. Journal of Geophysical Research: Atmospheres. 2006;111(D8). https://doi.org/10.1029/2005JD006017
  46. Baloloy AB, Blanco AC, Candido CG, Argamosa RJ, et al. Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: Rapideye, planetscope and sentinel-2. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences. 2018;4:29-36. https://doi.org/10.5194/isprs-annals-IV-3-29-2018
  47. Théau J, Gavelle E, Ménard P. Crop scouting using UAV imagery: a case study for potatoes. Journal of Unmanned Vehicle Systems. 2020;8(2):99-118. https://doi.org/10.1139/juvs-2019-0009
  48. Pagola M, Ortiz R, Irigoyen I, Bustince H, Barrenechea E, et al. New method to assess barley nitrogen nutrition status based on image colour analysis: Comparison with SPAD-502. Computers and Electronics in Agriculture. 2009;65(2):213-8. https://doi.org/10.1016/j.compag.2008.10.003

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