Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery

International Journal of Applied Earth Observation and Geoinformation

Volumn 64, Issue , 2018, Pages 31-42

  Malambo, L ^a   Popescu, S C ^a   Murray, S C ^a   Putman, E ^a   Pugh, N A ^a   Horne, D W ^a   Richardson, G ^a   Sheridan, R ^a   Rooney, W L ^a   Avant, R ^b   Vidrine, M ^b   McCutchen, B ^b   Baltensperger, D ^a   Bishop, M ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

3D point cloud; Above ground level; Field based; High throughput phenotyping; Maize; Multitemporal; Pix4D; Plant height; Sorghum; Structure from motion; Terrestrial laser scanning; Unmanned aerial systems

Indexed keywords

AERIAL SURVEY; ANGIOSPERM; HEIGHT DETERMINATION; IMAGE RESOLUTION; MAIZE; PHENOTYPE; TERRESTRIAL ENVIRONMENT; THREE-DIMENSIONAL MODELING; UNMANNED VEHICLE;

SORGHUM BICOLOR; ZEA MAYS;

EID: 85032189665     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2017.08.014     Document Type: Article

References (23)

1. Araus, J.L., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19 (2014), 52–61.
2. Arend, D., Lange, M., Pape, J.-M., Weigelt-Fischer, K., Arana-Ceballos, F., Mücke, I., Klukas, C., Altmann, T., Scholz, U., Junker, A., Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping. Sci. Data, 3, 2016.
3. 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.-Basel 6 (2014), 10395–10412.
4. Castillo, J.Z.M., Diaz, R.L., Guzmán, C.L.R., Development of an aerial counting system in oil palm plantations. IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2016 p. 012007.
5. Chen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., Klukas, C., Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26 (2014), 4636–4655.
6. Farfan, I.D.B., Murray, S.C., Labar, S., Pietsch, D., A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize. Field Crops Res. 149 (2013), 167–176.
7. 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, 12, 2016, 9.
8. Ghanem, M.E., Marrou, H., Sinclair, T.R., Physiological phenotyping of plants for crop improvement. Trends Plant Sci. 20 (2015), 139–144.
9. Grenzdörffer, G., Crop height determination with UAS point clouds. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 40, 2014, 135.
10. Holman, F.H., Riche, A.B., Michalski, A., Castle, M., Wooster, M.J., Hawkesford, M.J., High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens.-Basel, 8, 2016, 1031.
11. Kraus, K., Pfeifer, N., Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 53 (1998), 193–203.
12. Leberl, F., Irschara, A., Pock, T., Meixner, P., Gruber, M., Scholz, S., Wiechert, A., Point clouds: Lidar versus 3D vision. Photogramm. Eng. Remote Sens. 76 (2010), 1123–1134.
13. Lowe, D.G., Object recognition from local scale-invariant features. Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, IEEE, 1999, 1150–1157.
14. Malambo, L., Heatwole, C.D., A multitemporal profile-based interpolation method for gap filling nonstationary data. IEEE Trans. Geosci. Remote Sens., 2015, 1–10.
15. McGaughey, R.J., FUSION/LDV: Software for LIDAR Data Analysis and Visualization. 2009, US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, 123.
16. Peña, J.M., Torres-Sánchez, J., de Castro, A.I., Kelly, M., López-Granados, F., Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One, 8, 2013, e77151.
17. Shi, Y., Thomasson, J.A., Murray, S.C., Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS One, 11, 2016, e0159781.
18. Steel, R., A rank sum test for comparing all pairs of treatments. Technometrics 2 (1960), 197–207.
19. Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., Peña, J.M., High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS One, 10, 2015, e0130479.
20. Walter, A., Liebisch, F., Hund, A., Plant phenotyping: from bean weighing to image analysis. Plant Methods, 11, 2015, 14.
21. Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M., 'structure-from-Motion’ photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179 (2012), 300–314.
22. Yu, N., Li, L., Schmitz, N., Tian, L.F., Greenberg, J.A., Diers, B.W., Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sens. Environ. 187 (2016), 91–101.
23. Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13 (2012), 693–712.