Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements

Plant Methods

Volumn 12, Issue 1, 2016, Pages

  Hämmerle, Martin ^a   Höfle, Bernhard ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

3D geodata; Continuous raster crop height model; Individual plant height; Low cost time of flight camera; Precision agriculture; Site specific crop management

Indexed keywords

EID: 84999274565     PISSN: None     EISSN: 17464811     Source Type: Journal    
DOI: 10.1186/s13007-016-0150-6     Document Type: Article

References (44)

1. Rosell-Polo JR, Auat Cheein F, Gregorio E, Andújar D, Puigdomènech L, Masip J, Escolà A. Advances in structured light sensors applications in precision agriculture and livestock farming. In: Sparks DL, editor. Advances in agronomy, vol. 133. Cambridge: Elsevier Academic Press; 2015. p. 71-112.
2. Auernhammer H. Precision farming-the environmental challenge. Comput Electron Agric. 2001;30:31-43.
3. Anwar MR, Liu DL, Macadam I, Kelly G. Adapting agriculture to climate change: a review. Theor Appl Climatol. 2013;113:225-45.
4. Zhang N, Wang M, Wang N. Precision agriculture-a worldwide overview. Comput Electron Agric. 2002;36:113-32.
5. Schellberg J, Hill MJ, Gerhards R, Rothmund M, Braun M. Precision agriculture on grassland: applications, perspectives and constraints. Eur J Agron. 2008;29:59-71.
6. Eitel JUH, Magney TS, Vierling LA, Brown TT, Huggins DR. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crop Res. 2014;159:21-32.
7. 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.
8. Li W, Niu Z, Chen H, Li D, Wu M, 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 Indic. 2016;67:637-48.
9. 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. 2016;17(3):296-312.
10. Freeman K, Girma K, Arnall D, Mullen R, Martin K, Teal R, Raun W. By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron J. 2007;99:530-6.
11. Sharma LK, Bu H, Franzen DW, Denton A. Use of corn height measured with an acoustic sensor improves yield estimation with ground based active optical sensors. Comput Electron Agric. 2014;124:254-62.
12. Yin X, Hayes RM, McClure MA, Savoy HJ. Assessment of plant biomass and nitrogen nutrition with plant height in early-to mid-season corn. J Sci Food Agric. 2012;92:2611-7.
13. 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.
14. McCarthy CL, Hancock NH, Raine SR. Applied machine vision of plants: a review with implications for field deployment in automated farming operations. Intell Serv Robot. 2010;3:209-17.
15. 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;57:486-96.
16. Friedli M, Kirchgessner N, Grieder C, Liebisch F, Mannale M, Walter A. Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods. 2016;12(1):1-15.
17. Crommelinck S, Höfle B. Simulating an autonomously operating low-cost static terrestrial LiDAR for multitemporal maize crop height measurements. Remote Sens. 2016;8(3):205.
18. Bareth G, Bendig J, Tilly N, Hoffmeister D, Aasen H, Bolten A. A comparison of UAV- and TLS-derived plant height for crop monitoring: using polygon grids for the analysis of crop surface models (CSMs). Photogramm Fernerkund Geoinf. 2016;2:85-94.
19. Marx S, Hämmerle M, Klonner C, Höfle B. 3D participatory sensing with low-cost mobile devices for crop height assessment-a comparison with terrestrial laser scanning data. PLoS ONE. 2016;11(4):1-22.
20. Ehlert D, Adamek R, Horn HJ. Vehicle based laser range finding in crops. Sensors. 2009;9:3679-94.
21. Ehlert D, Heisig M, Adamek R. Suitability of a laser rangefinder to characterize winter wheat. Precis Agric. 2010;11:650-63.
22. Grenzdörffer GJ. Crop height determination with UAS point clouds. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2014;XL-1:135-40.
23. Luo S, Chen JM, Wang C, Xi X, Zeng H, Peng D, Li D. Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters. Opt Express. 2016;24:11578-93.
24. Marinello F, Pezzuolo A, Gasparini F, Arvidsson J, Sartori L. Application of the Kinect sensor for dynamic soil surface characterization. Prec Agricult. 2015;16(6):601-12.
25. Gonzalez-Jorge H, Rodríguez-Gonzálvez P, Martínez-Sánchez J, González-Aguilera D, Arias P, Gesto M, Díaz-Vilariño L. Metrological comparison between Kinect I and Kinect II sensors. Measurement. 2015;70:21-6.
26. Sarbolandi H, Lefloch D, Kolb A. Kinect range sensing: structured-light versus time-of-flight Kinect. Comput Vis Image Underst. 2015;139:1-20.
27. Microsoft. Kinect for Xbox One hardware specifications. https://dev.windows.com/en-us/kinect/hardware. Accessed 10 Oct 2016.
28. Lengeling T. Kinect for Windows v2 library for processing (provided under MIT license). https://github.com/ThomasLengeling/KinectPV2. Accessed 10 Oct 2016.
29. Besl PJ, McKay HD. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell. 1992;14(2):239-56.
30. Chen Y, Medioni G. Object modelling by registration of multiple range images. Image Vis Comput. 1992;10(3):145-55.
31. Boehm J. Accuracy investigation for structured-light based consumer 3D sensors. Photogramm Fernerkund Geoinf. 2014;2:117-27.
32. Riegl Measurement Systems GmbH. VZ-400 data sheet. 2014. http://www.riegl.com/uploads/tx_pxpriegldownloads/10_DataSheet_VZ-400_2014-09-19.pdf. Accessed 10 Oct 2016.
33. Point Cloud Library. Removing outliers using a StatisticalOutlierRemoval filter. http://pointclouds.org/documentation/tutorials/statistical_outlier.php. Accessed 10 Oct 2016.
34. Pfeifer N, Mandlburger G, Otepka J, Karel W. OPALS-a framework for airborne laser scanning data analysis. Comput Environ Urban Syst. 2014;45:125-36.
35. Höhle J, Höhle M. Accuracy assessment of digital elevation models by means of robust statistical methods. ISPRS J Photogramm Remote Sens. 2009;64(4):398-406.
36. Höfle B. Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection. IEEE Geosci Remote Sens Lett. 2014;11(1):94-8.
37. Lachat E, Macher H, Mittet MA, Landes T, Grussenmeyer P. First experiences with Kinect v2 sensor for close range 3D modelling. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2015;XL-5/W4:93-100.
38. Ball D, Ross P, English A, Patten T, Upcroft B, Fitch R, Sukkarieh S, Wyeth G, Corke P. Robotics for sustainable broad-acre agriculture. In: Proceedings of 9th international conference on field and service robotics. 2013. p. 439-453.
39. 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;5:538-60.
40. Tilly N, Aasen H, Bareth G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens. 2015;7:11449-80.
41. Avian M, Wujanz D. Movement of Hinteres Langtalkar rock glacier 2009-2013 by using the ICProx-algorithm at very high resolution point clouds from terrestrial laserscanning. In: EGU General Assembly conference abstracts, vol 16. 2014. p 12152.
42. Nüchter A, Borrmann D, Koch P, Kühn M, May S. A man-portable, IMU-free mobile mapping system. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. 2015;II-3/W5:17-23.
43. Höfle B, Canli E, Schmitz E, Crommelinck S, Hoffmeister D, Glade T. 4D near real-time environmental monitoring using highly temporal LiDAR. In: Geophysical research abstracts, vol 18(EGU2016-11295-2). 2016. p 1.
44. Maize plant drawing (provided under creative commons license CC0 1.0). http://all-free-download.com/free-vector/download/maize_plant_143968_download.html. Accessed 10 Oct 2016.