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Journal of Intelligent & Robotic Systems

, Volume 96, Issue 3–4, pp 573–589 | Cite as

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

  • Carlos A. Devia
  • Juan P. Rojas
  • E. Petro
  • Carol Martinez
  • Ivan F. Mondragon
  • D. Patino
  • M. C. Rebolledo
  • J. ColoradoEmail author
Article

Abstract

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.

Keywords

UAV-based precision agriculture Multispectral imagery Biomass estimation Vegetation indices 

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Notes

Acknowledgements

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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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of EngineeringPontificia Universidad JaverianaBogotaColombia
  2. 2.CIRAD, AGAP-PamMontpellierFrance
  3. 3.The International Center for Tropical Agriculture -CIAT, AgrodiversityPalmiraColombia

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