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 approac...

Full description

Saved in:
Bibliographic Details
Published inJournal of intelligent & robotic systems Vol. 96; no. 3-4; pp. 573 - 589
Main Authors Devia, Carlos A., Rojas, Juan P., Petro, E., Martinez, Carol, Mondragon, Ivan F., Patino, D., Rebolledo, M. C., Colorado, J.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.12.2019
Springer
Springer Nature B.V
Subjects
Agricultural industry
Analysis
Artificial Intelligence
Biomass
Control
Crops
Drone aircraft
Electrical Engineering
Engineering
Equipment and supplies
Image processing
Infrared imagery
Mechanical Engineering
Mechatronics
Rice
Ripening
Robotics
Sampling
Unmanned aerial vehicles
Vegetation
Multispectral imagery
UAV-based precision agriculture
Vegetation indices
Biomass estimation
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-019-01001-5