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Remote Sensing
Volume 13
Issue 7
10.3390/rs13071272
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Article

Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner

by
Tyler Adams
1,*,
Richard Bruton
2,
Henry Ruiz
1,
Ilse Barrios-Perez
1,
Michael G. Selvaraj
3 and
Dirk B. Hays
2
1
Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX 77843, USA
2
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
3
International Center for Tropical Agriculture, Santiago de Cali 6713, Colombia
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(7), 1272; https://doi.org/10.3390/rs13071272
Submission received: 25 February 2021 / Revised: 18 March 2021 / Accepted: 23 March 2021 / Published: 26 March 2021
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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Abstract

Challenges in rapid prototyping are a major bottleneck for plant breeders trying to develop the needed cultivars to feed a growing world population. Remote sensing techniques, particularly LiDAR, have proven useful in the quick phenotyping of many characteristics across a number of popular crops. However, these techniques have not been demonstrated with cassava, a crop of global importance as both a source of starch as well as animal fodder. In this study, we demonstrate the applicability of using terrestrial LiDAR for the determination of cassava biomass through binned height estimations, total aboveground biomass and total leaf biomass. We also tested using single LiDAR scans versus multiple registered scans for estimation, all within a field setting. Our results show that while the binned height does not appear to be an effective method of aboveground phenotyping, terrestrial laser scanners can be a reliable tool in acquiring surface biomass data in cassava. Additionally, we found that using single scans versus multiple scans provides similarly accurate correlations in most cases, which will allow for the 3D phenotyping method to be conducted even more rapidly than expected.
Keywords: remote sensing; high-throughput phenotyping; terrestrial laser scanner; cassava; point cloud; binned height; biomass remote sensing; high-throughput phenotyping; terrestrial laser scanner; cassava; point cloud; binned height; biomass
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MDPI and ACS Style

Adams, T.; Bruton, R.; Ruiz, H.; Barrios-Perez, I.; Selvaraj, M.G.; Hays, D.B. Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner. Remote Sens. 2021, 13, 1272. https://doi.org/10.3390/rs13071272

AMA Style

Adams T, Bruton R, Ruiz H, Barrios-Perez I, Selvaraj MG, Hays DB. Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner. Remote Sensing. 2021; 13(7):1272. https://doi.org/10.3390/rs13071272

Chicago/Turabian Style

Adams, Tyler, Richard Bruton, Henry Ruiz, Ilse Barrios-Perez, Michael G. Selvaraj, and Dirk B. Hays. 2021. "Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner" Remote Sensing 13, no. 7: 1272. https://doi.org/10.3390/rs13071272

APA Style

Adams, T., Bruton, R., Ruiz, H., Barrios-Perez, I., Selvaraj, M. G., & Hays, D. B. (2021). Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner. Remote Sensing, 13(7), 1272. https://doi.org/10.3390/rs13071272

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