High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery


This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops.

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  1. 1.

    Arroyo, J.A., Gomez-Castaneda, C., Ruiz, E., Munoz de Cote, E., Gavi, F., Sucar, L.E.: UAV technology and machine learning techniques applied to the yield improvement in precision agriculture. In: 2017 IEEE Mexican Humanitarian Technology Conference (MHTC), pp. 137–143 (2017). https://doi.org/10.1109/MHTC.2017.8006410

  2. 2.

    Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., Bareth, G.: Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens. 6(11), 10395–10412 (2014). https://doi.org/10.3390/rs61110395

    Article  Google Scholar 

  3. 3.

    Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., Gattelli, M.: Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7(4), 4026–4047 (2015). https://doi.org/10.3390/rs70404026

    Article  Google Scholar 

  4. 4.

    Carrijo, G.L.A., Oliveira, D.E., de Assis, G.A., Carneiro, M.G., Guizilini, V.C., Souza, J.R.: Automatic detection of fruits in coffee crops from aerial images. In: 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), pp. 1–6 (2017)

  5. 5.

    Gevaert, C.M., Suomalainen, J., Tang, J., Kooistra, L.: Generation of spectral-temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 3140–3146 (2015). https://doi.org/10.1109/JSTARS.2015.2406339

    Article  Google Scholar 

  6. 6.

    Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D.: Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80(1), 76–87 (2002). https://doi.org/10.1016/S0034-4257(01)00289-9

    Article  Google Scholar 

  7. 7.

    Gnyp, M.L., Miao, Y., Yuan, F., Ustin, S.L., Yu, K., Yao, Y., Huang, S., Bareth, G.: Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Res. 155, 42–55 (2014). https://doi.org/10.1016/j.fcr.2013.09.023

    Article  Google Scholar 

  8. 8.

    Guo, T., Kujirai, T., Watanabe, T.: Mapping crop status from an unmanned aerial vehicle for precision agriculture applications. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XXXIX-B1, 485–490 (2012)

    Article  Google Scholar 

  9. 9.

    Harrell, D.L., Tubana, B.S., Walker, T.W., Phillips, S.B.: Estimating rice grain yield potential using normalized difference vegetation index. Agron. J. 103(6), 1717–1723 (2011)

    Article  Google Scholar 

  10. 10.

    Hongli, L., Zhoumiqi, Y., Jinshui, Z., Shuai, G.: Highly efficient paddy classification using UAV-based orthorectified image. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3230–3233 (2017), https://doi.org/10.1109/IGARSS.2017.8127685

  11. 11.

    Honrado, J.L.E., Solpico, D.B., Favila, C.M., Tongson, E., Tangonan, G.L., Libatique, N.J.C.: UAV imaging with low-cost multispectral imaging system for precision agriculture applications. In: 2017 IEEE Global Humanitarian Technology Conference (GHTC), pp. 1–7 (2017)

  12. 12.

    Kanke, Y., Tubaña, 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. Precis. Agri. 17(5), 507–530 (2016). https://doi.org/10.1007/s11119-016-9433-1

    Article  Google Scholar 

  13. 13.

    Khanna, R., Möller, M, Pfeifer, J., Liebisch, F., Walter, A., Siegwart, R.: Beyond point clouds - 3D mapping and field parameter measurements using UAVs. In: IEEE 20th Conference on Emerging Technologies Factory Automation (ETFA), pp. 1–4 (2015)

  14. 14.

    Liu, Y., Cheng, T., Zhu, Y., Tian, Y., Cao, W., Yao, X., Wang, N.: Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 7362–7365 (2016). https://doi.org/10.1109/IGARSS.2016.7730920

  15. 15.

    Lu, J., Miao, Y., Huang, Y., Shi, W., Hu, X., Wang, X., Wan, J.: Evaluating an unmanned aerial vehicle-based remote sensing system for estimation of rice nitrogen status. In: 2015 4th International Conference on Agro-Geoinformatics (Agro-geoinformatics), pp. 198–203 (2015)

  16. 16.

    Naito, H., Ogawa, S., Valencia, M., Mohri, H., Urano, Y., Hosoi, F., Shimizu, Y., Chavez, A., Ishitani, M., Selvaraj, M., Omasa, K.: Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras. J. Photogramm. Remote Sens. 125, 50–62 (2017). https://doi.org/10.1016/j.isprsjprs.2017.01.010

    Article  Google Scholar 

  17. 17.

    Ndikumana, E., Minh, D., Thu, D., Baghdadi, N., Courault, D., Hossard, L., Moussawi, I.: Rice height and biomass estimations using multitemporal sar sentinel-1: Camargue case study, vol. 10783, p. 10783 (2018)

  18. 18.

    Prabhakara, K., Dean Hively, W., McCarty, G.W.: Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. Int. J. Appl. Earth Obs. Geoinf. 39, 88–102 (2015). https://doi.org/10.1016/j.jag.2015.03.002

    Article  Google Scholar 

  19. 19.

    Stroppiana, D., Migliazzi, M., Chiarabini, V., Crema, A., Musanti, M., Franchino, C., Villa, P.: Rice yield estimation using multispectral data from UAV: a preliminary experiment in northern Italy. In: 2015 IEEE International on Geoscience and Remote Sensing Symposium (IGARSS), pp. 4664–4667. IEEE (2015)

  20. 20.

    Tadasi, C., Kiyoshi, M., Shigeto, T., Kengo, Y., Shinichi, I., Masami, F.: Monitoring rice growth over a production region using an unmanned aerial vehicle: preliminary trial for establishing a regional rice strain. In: 3rd IFAC Conference in Modelling and Control in Agriculture, Horticulture and Post-Harvest Processing - Agricontrol, vol. 43, pp. 178–183 (2010)

  21. 21.

    Tanger, P., Klassenn, S., Mojica, J., Lovell, J., Moyers, B., Baraoidan, M., Elizabeth, M., Kenneth, B., McNally, L., Poland, J., Bush, D., Leung, H., Leach, J., McKay, J.: Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Sci. Rep. 7, 42839 (2017)

    Article  Google Scholar 

  22. 22.

    Viljanen, N., Honkavaara, E., Näsi R, Hakala, T., Niemeläinen, O, Kaivosoja, J.: A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone. Agriculture 8(5) (2018)

    Article  Google Scholar 

  23. 23.

    Xue, J., Su, B.: Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. Volume, 1353691, 1–17 (2017)

    Article  Google Scholar 

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The authors would like to thank all participating technicians and staff members from the CIAT station located in Santa Rosa -Meta, for supporting the trials carried out over the upland crops. Furthermore, to CIAT staff that supported the experiments over the lowland crops located at CIAT headquarters in Palmira, Valle del Cauca, Colombia; in particular to Yolima Ospina and Cecile Grenier for their support in upland and lowland trials.

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Correspondence to J. Colorado.

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This work was funded in part by the research project entitled Desarrollo de una herramienta para la agricultura de precision en los cultivos de arroz: sensado del estado de crecimiento y de nutricion de las plantas usando un drone autonomo, under the COLCIENCIAS - GRANT ID 120371551916, CT167-2016 (FONDO NACIONAL DE FINANCIAMIENTO PARA LA CIENCIA, LA TECNOLOGIA Y LA INNOVACION - FRANCISCO JOSE DE CALDAS) and the OMICAS program: Optimizacióń n Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y validación en Arroz y Caña de Azúcar), sponsored within the Colombian Scientific Ecosystem by The WORLD BANK, COLCIENCIAS, ICE- TEX, the Colombian Ministry of Education and the Colombian Ministry of Industry and Turism under GRANT ID: FP44842-217-2018.

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Devia, C.A., Rojas, J.P., Petro, E. et al. High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery. J Intell Robot Syst 96, 573–589 (2019). https://doi.org/10.1007/s10846-019-01001-5

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  • UAV-based precision agriculture
  • Multispectral imagery
  • Biomass estimation
  • Vegetation indices