Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Li, Jiating; Schachtman, Daniel P.; Creech, Cody F.; Wang, Lin; Ge, Yufeng; Shi, Yeyin

doi:10.1016/j.cj.2022.04.005

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Journal Article · Mon May 16 00:00:00 EDT 2022 · The Crop Journal (Online)

DOI:https://doi.org/10.1016/j.cj.2022.04.005· OSTI ID:2420355

Li, Jiating ^[1]; Schachtman, Daniel P. ^[2]; Creech, Cody F. ^[3]; Wang, Lin ^[2]; Ge, Yufeng ^[2]; Shi, Yeyin ^[2]

University of Nebraska, Lincoln, NE (United States); OSTI
University of Nebraska, Lincoln, NE (United States)
University of Nebraska, Scottsbluff, NE (United States)

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R²) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

View Accepted Manuscript (DOE)

Research Organization:: University of Nebraska, Lincoln, NE (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDA

Grant/Contract Number:: SC0014395

OSTI ID:: 2420355

Journal Information:: The Crop Journal (Online), Journal Name: The Crop Journal (Online) Journal Issue: 5 Vol. 10; ISSN 2214-5141

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (68)

High biomass yield energy sorghum: developing a genetic model for C4 grass bioenergy crops: Modeling and Analysis: Developing a genetic model for C4 grass bioenergy crops Olson, Sara N.; Ritter, Kimberley; Rooney, William Biofuels, Bioproducts and Biorefining, Vol. 6, Issue 6 https://doi.org/10.1002/bbb.1357	journal	September 2012
Designing sorghum as a dedicated bioenergy feedstock Rooney, William L.; Blumenthal, Jürg; Bean, Brent Biofuels, Bioproducts and Biorefining, Vol. 1, Issue 2 https://doi.org/10.1002/bbb.15	journal	October 2007
Comparison of yield based drought tolerance indices in improved varieties, genetic stocks and landraces of potato (Solanum tuberosum L.) Cabello, Rolando; Monneveux, Philippe; De Mendiburu, Felipe Euphytica, Vol. 193, Issue 2 https://doi.org/10.1007/s10681-013-0887-1	journal	February 2013
Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration Gómez-Candón, David; Virlet, Nicolas; Labbé, Sylvain Precision Agriculture, Vol. 17, Issue 6 https://doi.org/10.1007/s11119-016-9449-6	journal	April 2016
Response of upland cotton (G.hirsutum L.) genotypes to drought stress using drought tolerance indices Singh, Chandrakant; Kumar, Vijay; Prasad, Indivar Journal of Crop Science and Biotechnology, Vol. 19, Issue 1 https://doi.org/10.1007/s12892-015-0073-1	journal	March 2016
High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard Santesteban, L. G.; Di Gennaro, S. F.; Herrero-Langreo, A. Agricultural Water Management, Vol. 183 https://doi.org/10.1016/j.agwat.2016.08.026	journal	March 2017
Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation El-Hendawy, Salah E.; Hassan, Wael M.; Al-Suhaibani, Nasser A. Agricultural Water Management, Vol. 182 https://doi.org/10.1016/j.agwat.2016.12.003	journal	March 2017
Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review Katsoulas, Nikolaos; Elvanidi, Angeliki; Ferentinos, Konstantinos P. Biosystems Engineering, Vol. 151 https://doi.org/10.1016/j.biosystemseng.2016.10.003	journal	November 2016
Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet Quebrajo, L.; Perez-Ruiz, M.; Pérez-Urrestarazu, L. Biosystems Engineering, Vol. 165 https://doi.org/10.1016/j.biosystemseng.2017.08.013	journal	January 2018
Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat Moghimi, Ali; Yang, Ce; Anderson, James A. Computers and Electronics in Agriculture, Vol. 172 https://doi.org/10.1016/j.compag.2020.105299	journal	May 2020
Classification of soybean leaf wilting due to drought stress using UAV-based imagery Zhou, Jing; Zhou, Jianfeng; Ye, Heng Computers and Electronics in Agriculture, Vol. 175 https://doi.org/10.1016/j.compag.2020.105576	journal	August 2020
Evaluation of drought resistance indices under various environmental conditions Sio-Se Mardeh, A.; Ahmadi, A.; Poustini, K. Field Crops Research, Vol. 98, Issue 2-3 https://doi.org/10.1016/j.fcr.2006.02.001	journal	August 2006
Characterization of sorghum genotypes for traits related to drought tolerance Mutava, R. N.; Prasad, P. V. V.; Tuinstra, M. R. Field Crops Research, Vol. 123, Issue 1 https://doi.org/10.1016/j.fcr.2011.04.006	journal	July 2011
Integrated selection criteria for drought tolerance in wheat (Triticum aestivum L.) breeding programs using discriminant analysis Abdolshahi, R.; Nazari, M.; Safarian, A. Field Crops Research, Vol. 174 https://doi.org/10.1016/j.fcr.2015.01.009	journal	March 2015
Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars Elsayed, Salah; Rischbeck, Pablo; Schmidhalter, Urs Field Crops Research, Vol. 177 https://doi.org/10.1016/j.fcr.2015.03.010	journal	June 2015
Multivariate approach to identify and characterize bread wheat (Triticum aestivum) germplasm for waterlogging tolerance in India Singh, Gyanendra; Kumar, Pradeep; Gupta, Vikas Field Crops Research, Vol. 221 https://doi.org/10.1016/j.fcr.2018.02.019	journal	May 2018
Estimating early season growth and biomass of field pea for selection of divergent ideotypes using proximal sensing Tefera, Abeya Temesgen; Banerjee, Bikram Pratap; Pandey, Babu Ram Field Crops Research, Vol. 277 https://doi.org/10.1016/j.fcr.2021.108407	journal	March 2022
Performances of biomass sorghum [Sorghum bicolor (L.) Moench] under different water regimes in Mediterranean region Habyarimana, E.; Laureti, D.; Ninno, M. De Industrial Crops and Products, Vol. 20, Issue 1 https://doi.org/10.1016/j.indcrop.2003.12.019	journal	July 2004
Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging Li, Bo; Xu, Xiangming; Zhang, Li ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 162 https://doi.org/10.1016/j.isprsjprs.2020.02.013	journal	April 2020
The use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes Biju, Sajitha; Fuentes, Sigfredo; Gupta, Dorin Plant Physiology and Biochemistry, Vol. 127 https://doi.org/10.1016/j.plaphy.2018.03.005	journal	June 2018
Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery Berni, J. A. J.; Zarco-Tejada, P. J.; Sepulcre-Cantó, G. Remote Sensing of Environment, Vol. 113, Issue 11 https://doi.org/10.1016/j.rse.2009.06.018	journal	November 2009
Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera Zarco-Tejada, P. J.; González-Dugo, V.; Berni, J. A. J. Remote Sensing of Environment, Vol. 117 https://doi.org/10.1016/j.rse.2011.10.007	journal	February 2012
Soybean yield prediction from UAV using multimodal data fusion and deep learning Maimaitijiang, Maitiniyazi; Sagan, Vasit; Sidike, Paheding Remote Sensing of Environment, Vol. 237 https://doi.org/10.1016/j.rse.2019.111599	journal	February 2020
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration Xu, W.; Rosenow, D. T.; Nguyen, H. T. Plant Breeding, Vol. 119, Issue 4 https://doi.org/10.1046/j.1439-0523.2000.00506.x	journal	August 2000
Agronomic and physiological assessments of genotypic variation for drought resistance in sorghum Blum, A.; Mayer, J.; Golan, G. Australian Journal of Agricultural Research, Vol. 40, Issue 1 https://doi.org/10.1071/AR9890049	journal	January 1989
Secondary traits explaining sorghum genotype by environment interactions for grain yield Carcedo, Ana J. P.; Pardo, Pedro A.; Gambin, Brenda L. Crop and Pasture Science, Vol. 68, Issue 7 https://doi.org/10.1071/CP17015	journal	January 2017
Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate Sinclair, Thomas R.; Hammer, Graeme L.; van Oosterom, Erik J. Functional Plant Biology, Vol. 32, Issue 10 https://doi.org/10.1071/FP05047	journal	January 2005
Genomic regions for canopy temperature and their genetic association with stomatal conductance and grain yield in wheat Rebetzke, Greg J.; Rattey, Allan R.; Farquhar, Graham D. Functional Plant Biology, Vol. 40, Issue 1 https://doi.org/10.1071/FP12184	journal	January 2013
Phenotyping novel stay-green traits to capture genetic variation in senescence dynamics Christopher, John T.; Veyradier, Mathieu; Borrell, Andrew K. Functional Plant Biology, Vol. 41, Issue 11 https://doi.org/10.1071/FP14052	journal	January 2014
Narrow band vegetation indices overcome the saturation problem in biomass estimation Mutanga, O.; Skidmore, A. K. International Journal of Remote Sensing, Vol. 25, Issue 19 https://doi.org/10.1080/01431160310001654923	journal	October 2004
UAV-based thermal imaging in the assessment of water status of soybean plants Crusiol, Luís Guilherme Teixeira; Nanni, Marcos Rafael; Furlanetto, Renato Herrig International Journal of Remote Sensing, Vol. 41, Issue 9 https://doi.org/10.1080/01431161.2019.1673914	journal	October 2019
Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress Hein, Nathan T.; Ciampitti, Ignacio A.; Jagadish, S. V. Krishna Journal of Experimental Botany, Vol. 72, Issue 14 https://doi.org/10.1093/jxb/erab021	journal	January 2021
Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria Prakash, Parthiban Thathapalli; Banan, Darshi; Paul, Rachel E. Journal of Experimental Botany, Vol. 72, Issue 13 https://doi.org/10.1093/jxb/erab166	journal	April 2021
Enhancing drought tolerance in C4 crops Lopes, M. S.; Araus, J. L.; van Heerden, P. D. R. Journal of Experimental Botany, Vol. 62, Issue 9 https://doi.org/10.1093/jxb/err105	journal	April 2011
Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology Lopes, Marta S.; Reynolds, Matthew P. Journal of Experimental Botany, Vol. 63, Issue 10 https://doi.org/10.1093/jxb/ers071	journal	March 2012
Aerial canopy temperature differences between fast‐ and slow‐wilting soya bean genotypes Bai, H.; Purcell, L. C. Journal of Agronomy and Crop Science, Vol. 204, Issue 3 https://doi.org/10.1111/jac.12259	journal	January 2018
The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization McCormick, Ryan F.; Truong, Sandra K.; Sreedasyam, Avinash The Plant Journal, Vol. 93, Issue 2 https://doi.org/10.1111/tpj.13781	journal	December 2017
Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery Li, Jiating; Veeranampalayam-Sivakumar, Arun-Narenthiran; Bhatta, Madhav Plant Methods, Vol. 15, Issue 1 https://doi.org/10.1186/s13007-019-0508-7	journal	November 2019
Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research Shi, Yeyin; Thomasson, J. Alex; Murray, Seth C. PLOS ONE, Vol. 11, Issue 7 https://doi.org/10.1371/journal.pone.0159781	journal	July 2016
A Genomic Resource for the Development, Improvement, and Exploitation of Sorghum for Bioenergy Brenton, Zachary W.; Cooper, Elizabeth A.; Myers, Mathew T. Genetics, Vol. 204, Issue 1 https://doi.org/10.1534/genetics.115.183947	journal	June 2016
Use of physiological parameters as fast tools to screen for drought tolerance in sugarcane Silva, Marcelo de A.; Jifon, John L.; Silva, Jorge A. G. da Brazilian Journal of Plant Physiology, Vol. 19, Issue 3 https://doi.org/10.1590/S1677-04202007000300003	journal	September 2007
Using Canopy Temperature Measurements to Evaluate Drought Tolerance of Potato Genotypes Stark, Jeffrey C.; Pavek, Joseph J.; McCann, Ian R. Journal of the American Society for Horticultural Science, Vol. 116, Issue 3 https://doi.org/10.21273/JASHS.116.3.412	journal	May 1991
An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study Alzaben, Heba; Fraser, Roydon; Swanton, Clarence Agronomy Journal, Vol. 111, Issue 6 https://doi.org/10.2134/agronj2019.04.0309	journal	November 2019
Stress Tolerance in Soybeans. I. Evaluation of Three Screening Techniques for Heat and Drought Tolerance¹ Bouslama, M.; Schapaugh, W. T. Crop Science, Vol. 24, Issue 5 https://doi.org/10.2135/cropsci1984.0011183X002400050026x	journal	September 1984
Cooler Canopy Contributes to Higher Yield and Drought Tolerance in New Wheat Cultivars Pradhan, Gautam P.; Xue, Qingwu; Jessup, Kirk E. Crop Science, Vol. 54, Issue 5 https://doi.org/10.2135/cropsci2013.11.0788	journal	September 2014
Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines Potgieter, Andries B.; George-Jaeggli, Barbara; Chapman, Scott C. Frontiers in Plant Science, Vol. 8 https://doi.org/10.3389/fpls.2017.01532	journal	September 2017
UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought Ludovisi, Riccardo; Tauro, Flavia; Salvati, Riccardo Frontiers in Plant Science, Vol. 8 https://doi.org/10.3389/fpls.2017.01681	journal	September 2017
Elucidating Sorghum Biomass, Nitrogen and Chlorophyll Contents With Spectral and Morphological Traits Derived From Unmanned Aircraft System Li, Jiating; Shi, Yeyin; Veeranampalayam-Sivakumar, Arun-Narenthiran Frontiers in Plant Science, Vol. 9 https://doi.org/10.3389/fpls.2018.01406	journal	October 2018
High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery Ostos-Garrido, Francisco J.; de Castro, Ana I.; Torres-Sánchez, Jorge Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.00948	journal	July 2019
Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring Zhang, Liyuan; Niu, Yaxiao; Zhang, Huihui Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.01270	journal	October 2019
Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions Gano, Boubacar; Dembele, Joseph Sékou B.; Ndour, Adama Agronomy, Vol. 11, Issue 5 https://doi.org/10.3390/agronomy11050850	journal	April 2021
Scaling of Thermal Images at Different Spatial Resolution: The Mixed Pixel Problem Jones, Hamlyn; Sirault, Xavier Agronomy, Vol. 4, Issue 3 https://doi.org/10.3390/agronomy4030380	journal	July 2014
Using Thermography to Confirm Genotypic Variation for Drought Response in Maize Casari, Raphael; Paiva, Dayane; Silva, Vivianny International Journal of Molecular Sciences, Vol. 20, Issue 9 https://doi.org/10.3390/ijms20092273	journal	May 2019
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows Aasen, Helge; Honkavaara, Eija; Lucieer, Arko Remote Sensing, Vol. 10, Issue 7 https://doi.org/10.3390/rs10071091	journal	July 2018
Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery Bian, Jiang; Zhang, Zhitao; Chen, Junying Remote Sensing, Vol. 11, Issue 3 https://doi.org/10.3390/rs11030267	journal	January 2019
UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras Sagan, Vasit; Maimaitijiang, Maitiniyazi; Sidike, Paheding Remote Sensing, Vol. 11, Issue 3 https://doi.org/10.3390/rs11030330	journal	February 2019
Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera Kelly, Julia; Kljun, Natascha; Olsson, Per-Ola Remote Sensing, Vol. 11, Issue 5 https://doi.org/10.3390/rs11050567	journal	March 2019
A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images Zhang, Xin; Han, Liangxiu; Dong, Yingying Remote Sensing, Vol. 11, Issue 13 https://doi.org/10.3390/rs11131554	journal	June 2019
Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data Masjedi, Ali; Crawford, Melba M.; Carpenter, Neal R. Remote Sensing, Vol. 12, Issue 21 https://doi.org/10.3390/rs12213587	journal	November 2020
Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses De Swaef, Tom; Maes, Wouter H.; Aper, Jonas Remote Sensing, Vol. 13, Issue 1 https://doi.org/10.3390/rs13010147	journal	January 2021
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection Danilevicz, Monica F.; Bayer, Philipp E.; Boussaid, Farid Remote Sensing, Vol. 13, Issue 19 https://doi.org/10.3390/rs13193976	journal	October 2021
Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture Ribeiro-Gomes, Krishna; Hernández-López, David; Ortega, José Sensors, Vol. 17, Issue 10 https://doi.org/10.3390/s17102173	journal	September 2017
A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments Aragon, Bruno; Johansen, Kasper; Parkes, Stephen Sensors, Vol. 20, Issue 11 https://doi.org/10.3390/s20113316	journal	June 2020
The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines Shu, Meiyan; Shen, Mengyuan; Zuo, Jinyu Plant Phenomics, Vol. 2021 https://doi.org/10.34133/2021/9890745	journal	January 2021
Sorghum as a versatile feedstock for bioenergy production Xin, Zhanguo; Wang, Ming Li Biofuels, Vol. 2, Issue 5 https://doi.org/10.4155/bfs.11.125	journal	September 2011
Selection indices to identify drought-tolerant grain sorghum cultivars Menezes, C. B.; Ticona-Benavente, C. A.; Tardin, F. D. Genetics and Molecular Research, Vol. 13, Issue 4 https://doi.org/10.4238/2014.November.27.9	journal	January 2014
Physiological mechanisms of drought tolerance in sorghum, genetic basis and breeding methods: A review Beyene, Amelework; Hussien, Shimelis; Pangirayi, Tongoona African Journal of Agricultural Research, Vol. 10, Issue 31 https://doi.org/10.5897/AJAR2015.9595	journal	July 2015

Similar Records

The Consortium for Advanced Sorghum Phenomics (CASP). Final report

Technical Report · Wed Sep 02 00:00:00 EDT 2020 · OSTI ID:1657354

Related Subjects

09 BIOMASS FUELS
Agriculture
Crop phenotyping
Drought stress
Machine learning
Plant Sciences
Thermal
Unmanned Aerial System (UAS)

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Citation Formats

References (68)

Similar Records

Related Subjects