Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs Geoinf. 2015;39:79–87.

Li W, Niu Z, Huang N, Wang C, Gao S, Wu CY. Airborne LiDAR technique for estimating biomass components of maize: a case study in Zhangye City, Northwest China. Ecol Indic. 2015;57:486–96.

Yang S, Feng Q, Liang T, Liu B, Zhang W, Xie H. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sens Environ. 2018;204:448–55.

Yang X, Xu B, Yunxiang J, Jinya L, Zhu X. On grass yield remote sensing estimation models of China’s northern farming-pastoral ecotone. In: Lee G, editor. Advances in computational environment science. Berlin: Springer; 2012. p. 281–91.

Zheng G, Chen JM, Tian QJ, Ju WM, Xia XQ. Combining remote sensing imagery and forest age inventory for biomass mapping. J Environ Manag. 2007;85:616–23.

Zheng D, Rademacher J, Chen J, Crow T, Bresee M, Le Moine J, Ryu S-R. Estimating aboveground biomass using Landsat 7 ETM + data across a managed landscape in northern Wisconsin, USA. Remote Sens Environ. 2004;93:402–11.

Güneralp İ, Filippi AM, Randall J. Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling. Int J Appl Earth Obs Geoinf. 2014;33:119–26.

Anaya JA, Chuvieco E, Palacios-Orueta A. Aboveground biomass assessment in Colombia: a remote sensing approach. For Ecol Manag. 2009;257:1237–46.

Moriondo M, Maselli F, Bindi M. A simple model of regional wheat yield based on NDVI data. Eur J Agron. 2007;26:266–74.

J-h Bai, S-k Li, K-r Wang, Sui X-y, Chen B, Wang F-y. Estimating aboveground fresh biomass of different cotton canopy types with homogeneity models based on hyper spectrum parameters. Agric Sci China. 2007;6:437–45.

Yan N, Wu B. Integrated spatial–temporal analysis of crop water productivity of winter wheat in Hai Basin. Agric Water Manag. 2014;133:24–33.

Mutanga O, Adam E, Cho MA. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int J Appl Earth Obs Geoinf. 2012;18:399–406.

Matese A, Toscano P, Di Gennaro S, Genesio L, Vaccari F, Primicerio J, Belli C, Zaldei A, Bianconi R, Gioli B. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 2015;7:2971.

Aasen H, Burkart A, Bolten A, Bareth G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. Isprs J Photogramm Remote Sens. 2015;108:245–59.

Geipel J, Link J, Claupein W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sens. 2014;6:10335–55.

Pugh NA, Horne DW, Murray SC, Carvalho G, Malambo L, Jung J, Chang A, Maeda M, Popescu S, Chu T, Starek MJ, Brewer MJ, Richardson G, Rooney WL. Temporal estimates of crop growth in sorghum and maize breeding enabled by unmanned aerial systems. Plant Phenome J. 2018;1:170006.

Varela S, Assefa Y, Prasad PVV, Peralta NR, Griffin TW, Sharda A, Ferguson A, Ciampitti IA. Spatio-temporal evaluation of plant height in corn via unmanned aerial systems. J Appl Remote Sens. 2017;11:12.

Bendig J, Willkomm M, Tilly N, Gnyp M, Bennertz S, Qiang C, Miao Y, Lenz-Wiedemann V, Bareth G. Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring in Northeast China. Int Arch Photogramm Remote Sens Spat Inf Sci. 2013;40:45–50.

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. 2014;6:10395–412.

Brocks S, Bareth G. Estimating barley biomass with crop surface models from oblique RGB imagery. Remote Sens. 2018;10:268.

Chu TX, Chen RZ, Landivar JA, Maeda MM, Yang CH, Starek MJ. Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery. J Appl Remote Sens. 2016;10:17.

Muharam FM, Bronson KF, Maas SJ, Ritchie GL. Inter-relationships of cotton plant height, canopy width, ground cover and plant nitrogen status indicators. Field Crop Res. 2014;169:58–69.

Souza CHWD, Lamparelli RAC, Rocha JV. Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds. Int J Remote Sens. 2017;38:2218–30.

Holman FH, Riche AB, Michalski A, Castle M, Wooster MJ, Hawkesford MJ. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. 2016;8:1031.

Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N, Iwata H. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci. 2017;8:11.

Tilly N, Aasen H, Bareth G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens. 2015;7:11449.

Jing R, Gong ZN, Zhao WJ, Pu RL, Deng L. Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform—a case study in Wild Duck Lake Wetland, Beijing, China. Isprs J Photogramm Remote Sens. 2017;134:122–34.

Li W, Niu Z, Chen HY, Li D, Wu MQ, Zhao W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol Ind. 2016;67:637–48.

Yue JB, Yang GJ, Li CC, Li ZH, Wang YJ, Feng HK, Xu B. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens. 2017;9:19.

Ali I, Greifeneder F, Stamenkovic J, Neumann M, Notarnicola C. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sens. 2015;7:15841.

Zhang C, Denka S, Cooper H, Mishra DR. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sens Environ. 2018;204:366–79.

Hassan M, Yang M, Rasheed A, Jin X, Xia X, Xiao Y, He Z. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread Wheat. Remote Sens. 2018;10:809.

Gitelson AA, Gritz † Y, Merzlyak MN. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol. 2003;160:271–82.

Vincini M, Frazzi E, D’Alessio P. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis Agric. 2008;9:303–19.

Gitelson A, Merzlyak MN. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B Biol. 1994;22:247–52.

Pearson RL, Miller LD. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In: Remote sensing of environment, VIII. 1972. p. 7–12.

Serrano L, Filella I, Peñuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci. 2000;40:723–31.

Jordan CF. Derivation of leaf-area index from quality of light on the forest floor. Ecology. 1969;50:663–6.

Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. 1979;8:127–50.

Rouse JW Jr, Haas RH, Schell JA, Deering DW. Monitoring vegetation systems in the great plains with ERTS. In: Freden SC, Mercanti EP, Becker MA, editors. Third earth resources technology satellite-1 symposium, vol. 1. Washington: NASA; 1974. p. 309–17.

Gitelson AA. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol. 2004;161:165–73.

Louhaichi M, Borman M, Johnson D. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int. 2001;16:65–70.

Eraymondjr H, Cst D, Januh E, Long D. Remote sensing leaf chlorophyll content using a visible band index. Agron J. 2011;103:1090.

Gitelson AA, Viña A, Arkebauer JT, Rundquist DC, Galina K, Bryan L. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett. 2003;30:1248.

Gitelson AA, Kaufman YJ, Stark R, Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ. 2002;80:76–87.

Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA. Color indices for weed identification under various soil, residue, and lighting conditions. In: American society of agricultural engineers meeting. 1994.

Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9:62–6.

Lancashire PD, Bleiholder H, Den Boom TV, Langeluddeke P, Stauss R, Weber E, Witzenberger A. A uniform decimal code for growth stages of crops and weeds. Ann Appl Biol. 1991;119:561–601.

Pike RJ, Wilson SE. Elevation-relief ratio, hypsometric integral, and geomorphic area-altitude analysis. Geol Soc Am Bull. 1971;82(4):1079–84.

Parker GG, Harmon ME, Lefsky MA, Chen JQ, Van Pelt R, Weis SB, Thomas SC, Winner WE, Shaw DC, Frankling JF. Three-dimensional structure of an old-growth Pseudotsuga-tsuga canopy and its implications for radiation balance, microclimate, and gas exchange. Ecosystems. 2004;7:440–53.

Yue JB, Feng HK, Yang GJ, Li ZH. A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens. 2018;10:23.

Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng. 2014;40:16–28.

Guyon Isabelle, Elisseeff Andr. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82.

Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.

Lebedev AV, Westman E, Van Westen GJP, Kramberger MG, Lundervold A, Aarsland D, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, et al. Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage Clin. 2014;6:115–25.

Kuhn M. Variable selection using the caret package. Int Rev Electr Eng. 2010;1:44–9.

Kuhn M, Johnson K. Applied predictive modeling. New York: Springer; 2013.

Nasrabadi NM. Pattern recognition and machine learning. J Electron Imaging. 2007;16:049901.

Bergstra J, Bardenet R, Bengio Y, Kégl B. Algorithms for hyper-parameter optimization. In: 25th annual conference on neural information processing systems (NIPS 2011); 2011-12-12; Granada, Spain. Neural Information Processing Systems Foundation; 2011.

Efron B, Tibshirani R. Improvements on cross-validation: the 632 + bootstrap method. J Am Stat Assoc. 1997;92:548–60.

Chang W. R graphics cookbook: practical recipes for visualizing data. San Francisco: O’Reilly Media Inc.; 2012.

Breiman L. Random forests. Mach Learn. 2001;45:5–32.

Han L, Yang G, Feng H, Zhou C, Yang H, Xu B, Li Z, Yang X. Quantitative identification of maize lodging-causing feature factors using unmanned aerial vehicle images and a nomogram computation. Remote Sens. 2018;10:1528.

Salas Fernandez MG, Becraft PW, Yin Y, Lübberstedt T. From dwarves to giants? Plant height manipulation for biomass yield. Trends Plant Sci. 2009;14:454–61.

Alheit KV, Busemeyer L, Liu W, Maurer HP, Gowda M, Hahn V, Weissmann S, Ruckelshausen A, Reif JC, Würschum T. Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack). Theor Appl Genet. 2014;127:251–60.

Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 2011;121:268–73.

Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods. 2015;11:19.

Matese A, Di Gennaro SF, Berton A. Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging. Int J Remote Sens. 2017;38:2150–60.

Walter A, Liebisch F, Hund A. Plant phenotyping: from bean weighing to image analysis. Plant Methods. 2015;11:14.

Jimenezberni JA, Deery DM, Rozaslarraondo P, Condon AG, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XRR. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci. 2018;9:237.

Yue J, Feng H, Jin X, Yuan H, Li Z, Zhou C, Yang G, Tian Q. A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sens. 2018;10:1138.

Graham MH. Confronting multicollinearity in ecological multiple regression. Ecology. 2003;84:2809–15.

Lin X, Sun L, Li Y, Guo Z, Li Y, Zhong K, Wang Q, Lu X, Yang Y, Xu G. A random forest of combined features in the classification of cut tobacco based on gas chromatography fingerprinting. Talanta. 2010;82:1571–5.