Sensors for measuring plant phenotyping: A review

Ruicheng Qiu, Shuang Wei, Man Zhang, Han Li, Hong Sun, Gang Liu, Minzan Li

Abstract


Food crisis is a matter of prime importance because it becomes more severe as the global population grows. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, which hinders the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plant are particularly interested to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years. Some parameters that have been measured in phenotyping are introduced and discussed, including plant height, leaf parameters, in-plant space, chlorophyll, water stress, and biomass. And the measurement methods of each parameter with different sensors were classified and compared. Some comprehensive measurement platforms were also summarized, which are able to measure several parameters simultaneously. Besides, some deficiencies of phenotyping should be addressed, and novel methods should be proposed to reduce cost, improve efficiency, and promote phenotyping in the future.

Keywords: plant phenotype, high-throughput phenotyping, sensor, morphometric parameters, physiological parameters

DOI: 10.25165/j.ijabe.20181102.2696

Citation: Qiu R C, Wei S, Zhang M, Sun H, Li H, Liu G, et al. Sensors for measuring plant phenotyping: A review. Int J Agric & Biol Eng, 2018; 11(2): 1–17.

Keywords


plant phenotype, high-throughput phenotyping, sensor, morphometric parameters, physiological parameters

Full Text:

PDF

References


Großkinsky D K, Svensgaard J, Christensen S, Roitsch T. Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J Exp Bot, 2015; 66(18): 5429–5440.

Yang W, Duan L, Chen G, Xiong L, Liu Q. Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr Opin Plant Biol, 2013; 16(2): 180–187.

Cobb J N, DeClerck G, Greenberg A, Clark R, McCouch S. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor Appl Genet, 2013; 126(4): 867–887.

Sankaran S, Khot L R, Espinoza C Z, Jarolmasjed S, Sathuvalli V R, Vandemark G J, et al. Low-altitude; high-resolution aerial imaging systems for row and field crop phenotyping: A review. Eur J Agron, 2015; 70: 112–123.

Lee W S, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C. Sensing technologies for precision specialty crop production. Comput Electron Agric, 2010; 74: 2–33.

Dworak V, Selbeck J, Ehlert D. Ranging sensors for vehicle-based measurement of crop stand and orchard parameters: A review. Trans ASABE, 2011; 54(4): 1497–1510.

Gai J, Tang L, Steward B. Plant recognition through the fusion of 2D and 3D images for robotic weeding. In Proceedings of the 2015 ASABE Annual International Meeting, New Orleans, Louisiana, USA, 26-29 July 2015; Paper No. 152181371.

Lin Y. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? Comput Electron Agric, 2015; 119: 61–73.

Kazmi W, Foix S, Alenyà G, Andersen H J R. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison. ISPRS J Photogramm Remote Sen, 2014; 88: 128–146.

Xia C, Wang L, Chung B, Lee J. In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation. Sensors, 2015; 15(8): 20463–20479.

Shakoor N, Lee S, Mockler T C. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr Opin Plant Biol, 2017; 38: 184–192.

Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, et al. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci, 2017; 8: 1111.

Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors, 2014; 14(11): 20078–20111.

Kwon T, Kim K, Yoon H, Lee S, Kim B, Siddiqu Z S. Phenotyping of plants for drought and salt tolerance using infra-red thermography. Plant Breeding and Biotechnology, 2015; 3(4): 299–307.

Grenzdörffer G J. Crop height determination with UAS point clouds. Int. Arch. Photogramm. Remote Sens. Spat Inf Sci, 2014; XL-1: 135–140.

Gao S, Niu Z, Sun G, Zhao D, Jia K, Qin Y. Height extraction of maize using airborne full-waveform LIDAR data and a deconvolution algorithm. IEEE Geosci Remote Sens Lett, 2015; 12(9): 1–5.

Jiang Y, Li C, Paterson A H. High throughput phenotyping of cotton plant height using depth images under field conditions. Comput Electron Agric, 2016; 130: 57–68.

Sharma L K, Bu H, Franzen D W, Denton A. Use of corn height

measured with an acoustic sensor improves yield estimation with ground based active optical sensors. Comput Electron Agric, 2016; 124: 254–262.

Shi Y, Thomasson J A, Murray S C, Pugh N A, Rooney W L, Shafian S, et al. Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLOS ONE, 2016; 11(7): e0159781.

Nguyen T T, Slaughter D C, Max N, Maloof J N, Sinha N. Structured light-based 3d reconstruction system for plants. Sensors, 2015; 15(8): 18587–18612.

Jay S, Rabatel G, Hadoux X, Moura D, Gorretta N. In-field crop row phenotyping from 3D modeling performed using structure from motion. Comput Electron Agric, 2015; 110: 70–77.

Chen B, He C, Ma Y, Bai Y. 3D image monitoring and modeling for corn plants growth in field condition. Trans of the CSAE, 2011; 27(S1): 366–372. (in Chinese)

Santos T T, Rodrigues G C. Flexible three-dimensional modeling of plants using low- resolution cameras and visual odometry. Mach Vis Appl, 2016; 27(5): 695–707.

Chatzinikos A, Gemtos T, Fountas S. The use of a laser scanner for measuring crop properties in three different crops in Central Greece. Precision Agriculture’13: Proceedings of the 9th European Conference on Precision Agriculture, Lleida, Catalonia, Spain, Wageningen Academic Publishers; Netherlands, 7-11 July 2013; pp.129–136.

Saeys W, Lenaerts B, Craessaerts G, Baerdemaeker J D. Estimation of the crop density of small grains using LiDAR sensors. Biosyst Eng, 2009; 102: 22–30.

Zhang L, Grift T E. A LIDAR-based crop height measurement system for Miscanthus giganteus. Comput Electron Agric, 2012; 85: 70–76.

Hoffmeister D, Waldhoff G, Korres W, Curdt C, Bareth G. Crop height variability detection in a single field by multi-temporal terrestrial laser scanning. Precis Agric, 2015; 17(3): 296–312.

Weiss U, Biber P. Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robot Auton Syst, 2011; 59(5): 265–273.

Ehlert D, Heisig M. Sources of angle-dependent errors in terrestrial laser scanner-based crop stand measurement. Comput Electron Agric, 2013; 93: 10–16.

Selbeck J, Dworak V, Ehlert D. Testing a vehicle-based scanning lidar sensor for crop detection. Can J Remote Sens, 2010; 36(1): 24–35.

Ehlert D, Heisig M, Adamek R. Suitability of a laser rangefinder to characterize winter wheat. Precis Agric, 2010; 11(6): 650–663.

Bendig J, Bolten A, Bareth G. UAV-based imaging for multi-temporal; very high resolution crop surface models to monitor crop growth variability. Photogramm Fernerkund Geoinf, 2013; 6: 551–562.

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(11): 10395–10412.

Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, et al. 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 Geoinform, 2015; 39: 79–87.

Sui R, Fisher D K, Reddy K N. Cotton yield assessment using plant height mapping system. J Agric Sci, 2013; 5(1): 23–31.

Sui R, Thomasson J, Ge Y. Development of sensor systems for precision agriculture in cotton. Int J Agric Biol Eng, 2012; 4(5): 1–14.

Finkelshtain R, Bechar A, Yovel Y, Kósa G. Investigation and analysis of an ultrasonic sensor for specific yield assessment and greenhouse features identification. Precis Agric, 2016; 17: 1–16.

Pittman J, Arnall D, Interrante S, Moffet C, Butler T. Estimation of biomass and canopy height in bermudagrass; alfalfa; and wheat using ultrasonic; laser; and spectral sensors. Sensors, 2015; 15(2): 2920–2943.

Klose R, Scholz C, Ruckelshausen A. 3D Time-of-Flight camera-based sensor system for automatic crop height monitoring for plant phenotyping. In Proceedings of the CIGR-AgEng 2012 International Conference of Agricultural Engineering, Valencia, Spain, 8-12 July 2012; pp. 55–60.

Azzari G, Goulden M, Rusu R. Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors, 2013; 13(2): 2384–2398.

Andújar D, Dorado J, Fernández-Quintanilla C, Ribeiro A. An approach to the use of depth cameras for weed volume estimation. Sensors, 2016; 16(7): 972–982.

Shi Yeyin. Performance evaluation of off-shelf range sensors for in-field crop height measurement. Master's dissertation. Stillwater: Oklahoma State University, 2009.

Zou X, Ttus M M, Tammeorg P, Torres C L, Takala T, Pisek J, et al. Photographic measurement of leaf angles in field crops. Agric For Meteorol, 2014; 184(2): 137–146.

Deng L, Yu R, Ma W. Measurement method of maize leaf posture based on image processing. J Henan Agric Sci, 2014; 43(9): 168–172. (in Chines)

An N, Palmer C M, Baker R L, Markelz R J C, Ta J, Covington M F, et al. Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area. Comput Electron Agric, 2016; 127: 376–394.

Ribera J, He F, Chen Y, Habib A F, Delp E J. Estimating phenotypic traits from UAV based RGB imagery. In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, 13-17 August 2016. doi: 10.1145/1235.

Scharr H, Minervini M, French A P, Klukas C, Kramer D M, Liu X, et al. Leaf segmentation in plant phenotyping: a collation study. Mach Vis Appl, 2016; 27(4): 585–606.

Pape J M, Klukas C. 3-D histogram-based segmentation and leaf detection for rosette plants. European Conference on Computer Vision Workshops. Springer International Publishers; Switzerland, 2015; pp: 61–74.

Liu J, Pattey E. Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agric For Meteorol, 2010; 150(11): 1485–1490.

Yeh Y F, Lai T, Liu T, Liu C, Chung W, Lin T. An automated growth measurement system for leafy vegetables. Biosyst Eng, 2014; 117: 43–50.

Leemans V, Dumont B, Destain M, Vancutsem F, Bodson B. A method for plant leaf area measurement by using stereo vision. In Proceedings of the CIGR-AgEng 2012 International Conference of Agricultural Engineering, Valencia, Spain, 8-12 July 2012.

Zhang Y, Teng P, Shimizu Y, Hosoi F, Omasa K. Estimating 3D leaf and stem shape of nursery paprika plants by a novel multi-camera photography system. Sensors, 2016; 16(6): 874–891.

Bazaza A, Farimana Z, Bannayanb M. Modeling individual leaf area of basil (Ocimum basilicum) using different methods. Int J Plant Prod, 2011; 5(4): 439–447.

Oner F, Odabas M S, Sezer I, Odabas F. Leaf area prediction for corn (Zea mays L.) cultivars with multiregression analysis. Photosynthetica, 2011; 49(4): 637–640.

Song Y, Glasbey C A, Polder G, Van Der Heijden G W A M. Non-destructive automatic leaf area measurements by combining stereo and time-of-flight images. IET Comput Vis, 2014; 8(5): 391–403.

Chéné Y, Rousseau D, Lucidarme P, Bertheloot J, Caffier V, Morel P, et al. On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric, 2012; 82: 122–127.

Paulus S, Behmann J, Mahlein A, Plümer L, Kuhlmann H. Low-cost 3D systems: suitable tools for plant phenotyping. Sensors, 2014; 14(2): 3001–3018.

Huete A, Jackson R. Soil and atmosphere influences on the spectra of partial canopies. Remote Sens Environ, 1988; 25(1): 89–105.

Hasegawa K, Matsuyama H, Tsuzuki H, Sweda T. Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures. Remote Sens Environ, 2010; 114(3): 514–519.

Neinavaz E, Darvishzadeh R, Skidmore A K, Groen T A. Measuring the response of canopy emissivity spectra to leaf area index variation using thermal hyperspectral data. Int J Appl Earth Obs Geoinform, 2016; 53: 40–47.

Neinavaz E, Skidmore A K, Darvishzadeh R, Groen T A. Retrieval of leaf area index in different plant species using thermal hyperspectral data. ISPRS J Photogramm Remote Sen, 2016; 119: 390–401.

Dammer K, Dworak V, Selbeck J. On-the-go phenotyping in field potatoes using camera vision. Potato Res, 2016; 59(2): 113–127.

Schirrmann M, Hamdorf A, Garz A, Ustyuzhanin A, Dammer K. Estimating wheat biomass by combining image clustering with crop height. Comput Electron Agric, 2016; 121: 374–384.

Garrido M, Paraforos D S, Reiser D, Arellano M V, Griepentrog H W, Valero C. 3D maize plant reconstruction based on georeferenced overlapping LiDAR point clouds. Remote Sens, 2015; 7(12): 17077–17096.

Kempthorne D M, Turner I W, Belward J A, Mccue S W, Barry M, Young J, et al. Surface reconstruction of wheat leaf morphology from three-dimensional scanned data. Funct Plant Biol, 2014; 42(5): 444.

Paulus S, Dupuis J, Mahlein A K, Kuhlmann H. Surface feature based classification plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform, 2013; 14: 238.

Paulus S, Schumann H, Kuhlmann H, Léon J. High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst Eng, 2014; 121: 1–11.

Hosoi F, Nakabayashi K, Omasa K. 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors, 2011; 11(12): 2166–2174.

Sirault X R R S, Fripp J, Paproki A, Kuffner P, Nguyen C, Li R X, et al. PlantScan: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselkä, Finland, 9-14 June 2013; pp.45–48.

Gebbers R, Ehlert D, Adamek R. Rapid mapping of the leaf area index in agricultural crops. Agron J, 2011; 103(5): 1532.

Nakarmi A D, Tang T. Automatic inter-plant spacing sensing at early growth stages using a 3D vision sensor. Comput Electron Agric, 2012; 82: 23–31.

Shi Y, Wang N, Taylor R K, Raun W R. Improvement of a ground-LiDAR-based corn plant population and spacing measurement system. Comput Electron Agric, 2015; 112: 92–101.

Shi Y, Wang N, Taylor R K, Raun W R, Hardin J A. Automatic corn plant location and spacing measurement using laser line-scan technique. Precis Agric, 2013; 14(5): 478–494.

Jin J, Tang L. Corn plant sensing using real-time stereo vision. J Field Robot, 2009; 26(6-7): 591–608.

Nakarmi A D, Tang T. Within-row spacing sensing of maize plants using 3D computer vision. Biosyst Eng, 2014; 125: 54–64.

Ulissi V, Antonucci F, Benincasa P, Farneselli M, Tosti G, Guiducci M, et al. Nitrogen concentration estimation in tomato leaves by VIS-NIR non-destructive spectroscopy. Sensors, 2011; 11(12): 6411–6424.

Lamb D W, Steyn-Ross M, Schaare P, Hanna M M, Silvester W, Steyn-Ross A. Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modelling and experimental observations. Int. J. Remote Sens, 2002; 23(18): 3619–3648.

Bai G, Ge Y, Hussain W, Baenziger P S, Graef G. A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Comput Electron Agric, 2016; 128: 181–192.

Raper T B, Varco J J. Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precis Agric, 2015; 16(1): 62–76.

He L, Zhang H Y, Zhang Y S, Song X, Feng W, Kang G Z, Wang C Y, Guo T C. Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. Eur J Agron, 2016; 73: 170–185.

Thorp K R, Gore M A, Andrade-Sanchez P, Carmo-Silva A E, Welch S M, White J W, French A N. Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Comput Electron Agric, 2015; 118: 225–236.

Inoue Y, Guérif M, Baret F, Skidmore A, Gitelson A, Schlerf M, et al. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. Plant Cell Environ, 2016; 39(12): 2609–2623.

Samborski S M, Gozdowski D, Walsh O S, Lamb D W, Stępień M, Gacek E S, et al. Winter wheat genotype effect on canopy reflectance: implications for using NDVI for in-season nitrogen topdressing recommendations. Agron J, 2015; 107(6): 2097.

Barker J, Zhang N, Sharon J, Steeves R, Wang X, Wei Y, et al. Development of a field-based high-throughput mobile phenotyping platform. Comput Electron Agric, 2016; 122: 74–85.

Kipp S, Mistele B, Baresel P, Schmidhalter U. High-throughput phenotyping early plant vigour of winter wheat. Eur J Agron, 2014; 52: 271.

Taskos D G, Koundouras S, Stamatiadis S, Zioziou E, Nikolaou N, Karakioulakis K, et al. Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity. Precis Agric, 2015; 16(1): 77–98.

Padilla F M, Teresa Peña-Fleitas M, Gallardo M, Thompson R B. Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon. Eur J Agron, 2014; 58: 39–52.

Li F, Miao Y, Feng G, Yuan F, Yue S, Gao X, et al. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crop Res, 2014; 157: 111–123.

Kipp S, Mistele B, Schmidhalter U. The performance of active spectral reflectance sensors as influenced by measuring distance; device temperature and light intensity. Comput Electron Agric, 2014; 100: 24–33.

Raper T B, Varco J J, Hubbard K J. Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agron J, 2013; 105(5): 1345.

Stamatiadis S, Taskos D, Tsadila E, Christofides C, Tsadilas C, Schepers J S. Comparison of passive and active canopy sensors for the estimation of vine biomass production. Precis Agric, 2010; 11(3): 306–315.

Mahlein A K, Oerke E C, Steiner U, Dehne H W. Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Path, 2012; 133(1): 197–209.

Bourgeon M A, Paoli J N, Jones G, Villette S, Gée C. Field radiometric calibration of a multispectral on-the-go sensor dedicated to the characterization of vineyard foliage. Comput Electron Agric, 2016; 123: 184–194.

Leblanc G, Kalacska M, Soffer R. Detection of single graves by airborne hyperspectral imaging. Forensic Sci Int, 2014; 245: 17–23.

Elarab M, Ticlavilca A M, Torres-Rua A F, Maslova I, McKee M. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. Int J Appl Earth Obs Geoinform, 2015; 43: 32–42.

Zaman-Allah M, Vergara O, Araus J L, Tarekegne A, Magorokosho C, Zarco-Tejada P J, et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 2015; 11: 35.

Kalacska M, Lalonde M, Moore T R. Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: scaling from leaf to image. Remote Sens Environ, 2015; 169(4): 270–279.

Houborg R, McCabe M F, Angel Y, Middleton E M. Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery. In Proceedings of SPIE Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 999812, 25 October 2016.

Cendrero-Mateo M P, Moran M S, Papuga S A, Thorp K R, Alonso L, Moreno J, et al. Plant chlorophyll fluorescence: active and passive measurements at canopy and leaf scales with different nitrogen treatments. J Exp Bot, 2015; 67(1): 275–286.

Yang J, Shi S, Gong W, Du L, Ma Y Y, Zhu B, et al. Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content. Plant Soil Environ, 2015; 61(4): 182–188.

Yang J, Gong W, Shi S, Du L, Sun J, Song S, et al. Analyzing the performance of fluorescence parameters in the monitoring of leaf nitrogen content of paddy rice. Sci Rep, 2016; 6: 28787.

Agati G, Foschi L, Grossi N, Volterrani M. In field non-invasive sensing of the nitrogen status in hybrid bermudagrass (Cynodon dactylon × C. transvaalensis Burtt Davy) by a fluorescence-based method. Eur J Agron, 2015; 63: 89–96.

Agati G, Foschi L, Grossi N, Guglielminetti L, Cerovic Z G, Volterrani M. Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses. Eur J Agron, 2013; 45: 39–51.

Longchamps L, Khosla R. Early detection of nitrogen variability in maize using fluorescence. Agron J, 2014; 106(2): 511.

Thoren D, Thoren P, Schmidhalter U. Influence of ambient light and temperature on laser-induced chlorophyll fluorescence measurements. Eur J Agron, 2010; 32(2): 169–176.

Daughtry C S T, Walthall C L, Kim M S, Colstoun E B D, McMurtrey J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ, 2000; 74(2): 229–239.

Eitel J U H, Vierling L A, Long D S. Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner. Remote Sens Environ, 2010; 114(10): 2229–2237.

Eitel J U H, Vierling L A, Long D S, Hunt E R. Early season remote sensing of wheat nitrogen status using a green scanning laser. Agric For Meteorol, 2011; 151(10): 1338–1345.

Eitel J U H, Magney T S, Vierling L A, Dittmar G. Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology. ISPRS J Photogramm Remote Sen, 2014; 97: 229–240.

Eitel J U H, Magney T S, Vierling L A, Brown T T, Huggins D R. LiDAR based biomass and crop nitrogen estimates for rapid; non-destructive assessment of wheat nitrogen status. Field Crop Res, 2014; 159: 21–32.

Behmann J, Mahlein A K, Paulus S, Dupuis J, Kuhlmann H, Oerke E C, et al. Generation and application of hyperspectral 3d plant models: methods and challenges. Mach Vis Appl, 2016; 27(5): 611–624.

Sun J, Shi S, Gong W, Yang J, Du L, Song S, et al. Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer. Sci Rep, 2017; 7: 40362.

Nevalainen O, Hakala T, Suomalainen J, Mäkipää R, Peltoniemi M, Krooks A, et al. Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR. Agric For Meteorol, 2014; 198-199: 250–258.

Du L, Gong W, Shi S, Yang J, Sun J, Zhu B, et al. Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR. Int J Appl Earth Obs Geoinform, 2016; 44: 136–143.

Du L, Shi S, Yang J, Sun J, Gong W. Using different regression methods to estimate leaf nitrogen content in rice by fusing hyperspectral LiDAR data and laser-induced chlorophyll fluorescence data. Remote Sens, 2016; 8(6): 526–539.

Ounis A, Bach J, Mahjoub A, Daumard F, Moya L, Goulas Y. Combined use of LIDAR and hyperspectral measurements for remote sensing of fluorescence and vertical profile of canopies. Spanish Association of Remote Sensing, 2016; 45: 87–94.

Prashar A, Jones H G. Assessing drought responses using thermal infrared imaging. Methods Mol Biol, 2016; 1398: 209–219.

Bellvert J, Marsal J, Girona J, Zarco-Tejada P J. Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery. Irrigation Sci, 2015; 33(2): 81–93.

Rischbeck P, Elsayed S, Mistele B, Barmeier G, Heil K, Schmidhalter U. Data fusion of spectral; thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. Eur J Agron, 2016; 78: 44–59.

Ni Z, Liu Z, Huo H, Li Z L, Nerry F, Wang Q, et al. Early water stress detection using leaf-level measurements of chlorophyll fluorescence and temperature data. Remote Sens, 2015; 7(3): 3232–3249.

Kim M, Kim S, Kim Y, Choi Y, Seo M. Infrared estimation of canopy temperature as crop water stress indicator. Korean Journal of Soil Science and Fertilizer, 2015; 48(5): 499–504.

Bellvert J, Zarco-Tejada P J, Girona J, Fereres E. Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precis Agric, 2014; 15(4): 361–376.

Cohen Y, Alchanatis V, Sela E, Saranga Y, Cohen S, Meron M, et al. Crop water status estimation using thermography: multi-year model development using ground-based thermal images. Precis Agric, 2015; 16(3): 311–329.

Zia S, Romano G, Spreer W, Sanchez C, Cairns J, Araus J L, et al. Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. J Agron Crop Sci, 2013; 199(2): 75–84.

Grant O M, Ochagavía H, Baluja J, Diago M P, Tardáguila J. Thermal imaging to detect spatial and temporal variation in the water status of grapevine (Vitis vinifera L.). J Hortic Sci Biotech, 2016; 91(1): 43–54.

Gago J, Douthe C, Coopman R E, Gallego P P, Ribas-Carbo M, Flexas J, et al. UAVs challenge to assess water stress for sustainable agriculture. Agric Water Manag, 2015; 153: 9–19.

Mangus D L, Sharda A, Zhang N. Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Comput Electron Agric, 2016; 121: 149–159.

Buitrago M F, Groen T A, Hecker C A, Skidmore A K. Changes in thermal infrared spectra of plants caused by temperature and water stress. ISPRS J Photogramm Remote Sens, 2016; 111: 22–31.

Elsayed S, Rischbeck P, Schmidhalter U. Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars. Field Crop Res, 2015; 177: 148–160.

Bandyopadhyay K K, Pradhan S, Sahoo R N, Singh R, Gupta V K, Joshi D

K, et al. Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices. Agric Water Manag, 2014; 146: 115–123.

Winterhalter L, Mistele B, Jampatong S, Schmidhalter U. High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur J Agron, 2011; 35(1): 22–32.

Moshou D, Pantazi X, Kateris D, Gravalos I. Water stress detection based on optical multisensor fusion with a least squares support vector machine classifier. Biosyst Eng, 2014; 117: 15–22.

Rossini M, Fava F, Cogliati S, Meroni M, Marchesi A, Panigada C, et al. Assessing canopy PRI from airborne imagery to map water stress in maize. ISPRS J Photogramm. Remote Sens, 2013; 86(3): 168–177.

Prashar A, Jones H G. Infra-red thermography as a high-throughput tool for field phenotyping. Agronomy, 2014; 4(3): 397–417.

Zarco-Tejada P J, González-Dugo V, Berni J A J. Fluorescence; temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ, 2012; 117(1): 322–337.

Tattaris M, Reynolds M P, Chapman S C. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci, 2016; 7: 1131.

Cerovic Z G, Goulas Y, Gorbunov M, Briantais J M, Camenen L, Moya I. Fluorosensing of water stress in plants: diurnal changes of the mean lifetime and yield of chlorophyll fluorescence, measured simultaneously and at distance with a τ-LIDAR and a modified PAM-fluorimeter, in maize, sugar beet, and kalanchoë. Remote Sens Environ, 1996; 58(3): 311–321.

Tucker C. A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass. Grass Forage Sci, 1980, 35(3): 177–182.

Silva A G P, Görgens E B, Campoe O C, Alvares C A, Stape J L, Rodriguez L C E. Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations. Sci Agr, 2015; 72(6): 504–512.

Andújar D, Fernández-Quintanilla C, Dorado J. Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry. Sensors, 2015; 15(6): 12999–13011.

Ehlert D, Adamek R, Horn H J. Laser rangefinder-based measuring of crop biomass under field conditions. Precis Agric, 2009; 10(5): 395-408.

Ehlert D, Horn H J, Adamek R. Measuring crop biomass density by laser triangulation. Comput Electron Agric, 2008; 61: 117–125.

Marshall M, Thenkabail P. Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing. Remote Sens, 2015; 7(1): 808–835.

Tilly N, Hoffmeister D, Cao Q, Lenz-Wiedemann V, Miao Y, Bareth G. Transferability of models for estimating paddy rice biomass from spatial plant height data. Agriculture, 2015; 5(3): 538–560.

Fricke T, Richter F, Wachendorf M. Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor. Comput Electron Agric, 2011; 79: 142–152.

Li W, Niu Z, Huang N, Wang C, Gao S, Wu C. Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City; Northwest China. Ecol Indic, 2015; 57(2): 486–496.

Li W, Niu Z, Wang C, Huang W, Chen H, Gao S, et al. Combined use of airborne LiDAR and satellite GF-1 data to estimate leaf area index; height; and aboveground biomass of maize during peak growing season. IEEE J Sel Top Appl Earth Observ Remote Sems, 2015; 8(9): 4489–4501.

Serrano L, Filella I, Penuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci, 2000; 40: 723–731.

Fricke T, Wachendorf M. Combining ultrasonic sward height and spectral signatures to assess the biomass of legume-grass swards. Comput Electron Agric, 2013; 99: 236–247.

Gao S, Niu Z, Huang N, Hou X. Estimating the leaf area index; height and biomass of maize using HJ-1 and RADARSAT-2. Int J Appl Earth Obs Geoinform, 2013; 24: 1–8.

Winterhalter L, Mistele B, Schmidhalter U. Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies. Field Crop Res, 2012; 129: 14–20.

Gnyp M L, Bareth G, Li F, Lenz-Wiedemann V I S, Koppe W, Miao Y, et al. Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Int J Appl Earth Obs Geoinform, 2014; 33(12): 232–242.

Gnyp M L, Miao Y, Yuan F, Ustin S L, Yu K, Yao Y, et al. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crop Res, 2014; 155: 42–55.

Mistele B, Schmidhalter U. Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crop Res, 2008; 106(1): 94–103.

Mistele B, Schmidhalter U. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agron J, 2010; 102(2): 499.

Erdle K, Mistele B, Schmidhalter U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crop Res, 2011; 124(1): 74–84.

Montes J M, Technow F, Dhillon B S, Mauch F, Melchinger A E. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crop Res, 2011; 121: 268–273.

Freeman K W, Girma K, Arnall D B, Mullen R W, Martin K L, Teal R K, et al. By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron J, 2007; 99(2): 530–536.

Tilly N, Aasen H, Bareth G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens, 2015; 7(9): 11449–11480.

Andrade-Sanchez P, Gore M A, Heun J T, Thorp K R, Carmo-Silva A E, French A N, et al. Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol, 2014; 41(1): 68.

Jared B. Development of a field-based high-throughput mobile phenotyping platform. Master's dissertation. Manhattan: Kansas State University, 2014.

Redden L, Colgan M. 2015. Method for automatic phenotype measurement and selection. U.S. Patent 14/329,161. Data issued: 11 July.

Busemeyer L, Mentrup D, Moller K, Wunder E, Alheit K, Hahn V, et al. BreedVision--a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors, 2013; 13(3): 2830–2847.

Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy, 2014; 4(3): 349–379.

Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford M J. Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol, 2017; 44(1): 143.

Shafiekhani A, Kadam S, Fritschi F B, DeSouza G N. Vinobot and Vinoculer: two robotic platforms for high-throughput field phenotyping. Sensors, 2017; 17(1): 214–237.

Ruckelshausen A, Biber P, Dorna M, Gremmes H, Klose R, Linz A, et al. BoniRob: an autonomous field robot platform for individual plant phenotyping. Precis Agric, 2009; 9: 841–847.

Strothmann W, Ruckelshausen A, Hertzberg J, Scholz C, Langsenkamp F. Plant classification with In-field-labeling for crop/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system. Comput Electron Agric, 2017; 134: 79–93.

Mueller S T, Jenkins M, Abel J, Kantor G. The Robotanist: a ground-based agricultural robot for high-throughput crop phenotyping. https://static1.squarespace.com/static/5879c6b59f745611b9f086da/t/587d229103596e8775c61645/1484595865270/ICRA_2017_Robotanist.pdf. Accessed on [2017-08-01].

Chapman S C, Merz T, Chan A, Jackway P, Hrabar S, Dreccer M F, et al. Pheno-Copter: a low-altitude; autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy, 2014; 4(2): 279–301.

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: 9.

He X, Jane B, Andreas H, Jan L. Recent development of unmanned aerial vehicle for plant protection in East Asia. Int J Agric Biol Eng, 2017; 10(3): 18–30.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office