Assessing lodging severity over an experimental maize (Zea mays L.) field using UAS images

Remote Sensing

Volumn 9, Issue 9, 2017, Pages

  Chu, Tianxing ^a   Starek, Michael J ^a   Brewer, Michael J ^b   Murray, Seth C ^c   Pruter, Luke S ^b  

^a TEXAS A AND M UNIVERSITY CORPUS CHRISTI   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Crop height modelling; Lodging rate; Maize lodging; Multivariate regression; Structure from motion photogrammetry; Unmanned aircraft systems

Indexed keywords

AIRCRAFT DETECTION; CROPS; GRAIN (AGRICULTURAL PRODUCT); IMAGE RECONSTRUCTION; INFRARED DEVICES; PHOTOGRAMMETRY; REGRESSION ANALYSIS;

CROP HEIGHT; MAIZE LODGING; MULTIVARIATE REGRESSION; STRUCTURE FROM MOTION; UNMANNED AIRCRAFT SYSTEM;

UNMANNED AERIAL VEHICLES (UAV);

EID: 85029355195     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9090923     Document Type: Article

References (43)

1. Nielsen, R.L.; Colville, D. Stalk Lodging in Corn: Guidelines for Preventive Management; Cooperative Extension Service, Purdue University: West Lafayette, IN, USA, 1988.
2. Wu, W.; Ma, B.-L. A new method for assessing plant lodging and the impact of management options on lodging in canola crop production. Sci. Rep. 2016, 6, 31890.
3. Robertson, D.J.; Julias, M.; Lee, S.Y.; Cook, D.D. Maize stalk lodging: Morphological determinants of stalk strength. Crop Sci. 2017, 57, 926-934.
4. Grant, B.L. Types of Plant Lodging: Treating Plants Affected by Lodging. Available online: https://www.gardeningknowhow.com/edible/vegetables/vgen/plants-affected-by-lodging.htm/?print=1&loc=top (accessed on 13 March 2017).
5. Elmore, R. Mid- to Late-Season Lodging. Iowa State University Extension and Outreach. Available online: http://crops.extension.iastate.edu/corn/production/management/mid/silking.html (accessed on 3 April 2017).
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 Crop. Res. 2013, 149, 167-176.
7. Ogden, R.T.; Miller, C.E.; Takezawa, K.; Ninomiya, S. Functional regression in crop lodging assessment with digital images. J. Agric. Biol. Environ. Stat. 2002, 7, 389-402.
8. Zhang, J.; Gu, X.;Wang, J.; Huang, W.; Dong, Y.; Luo, J.; Yuan, L.; Li, Y. Evaluating maize grain quality by continuous wavelet analysis under normal and lodging circumstances. Sens. Lett. 2012, 10, 1-6.
9. Yang, H.; Chen, E.; Li, Z.; Zhao, C.; Yang, G.; Pignatti, S.; Casa, R.; Zhao, L. Wheat lodging monitoring using polarimetric index from RADARSAT-2 data. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 157-166.
10. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97.
11. Gómez-Candón, D.; De Castro, A.I.; López-Granados, F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis. Agric. 2014, 15, 44-56.
12. Zhang, C.; Kovacs, J.M. The applications of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712.
13. Chen, R.; Chu, T.; Landivar, J.A.; Yang, C.; Maeda, M.M. Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images. Precis. Agric. 2017.
14. Chu, T.; Chen, R.; Landivar, J.A.; Maeda, M.M.; Yang, C.; Starek, M.J. Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery. J. Appl. Remote Sens. 2016, 10, 036018.
15. Wallace, L.; Lucieer, A.;Watson, C.; Turner, D. Development of a UAV??LiDAR system with application to forest inventory. Remote Sens. 2012, 4, 1519-1543.
16. 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, 322-337.
17. Murakami, T.; Yui, M.; Amaha, K. Canopy height measurement by photogrammetric analysis of aerial images: Application to buckwheat (Fagopyrum esculentumMoench) lodging evaluation. Comput. Electron. Agric. 2012, 89, 70-75.
18. Yang, M.-D.; Huang, K.-S.; Kuo, Y.-H.; Tsai, H.P.; Lin, L.-M. Spatial and spectral hybrid image classification for rice lodging assessment through UAV imagery. Remote Sens. 2017, 9, 583.
19. 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, 9.
20. Khanna, R.; Möller, M.; Pfeifer, J.; Liebisch, F.; Walter, A.; Siegwart, R. Beyond point clouds-3D mapping and field parameter measurements using UAVs. In Proceedings of the 20th IEEE Conference on Emerging Technologies&Factory Automation (ETFA), Luxembourg, 8-11 September 2015.
21. Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 2014, 55, 89-99.
22. Anthony, D.; Elbaum, S.; Lorenz, A.; Detweiler, C. On crop height estimation with UAVs. In Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, 14-18 September 2014.
23. De Souza, C.H.W.; Lamparelli, R.A.C.; Rocha, J.V.; Magalhães, P.S.G. Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds. Int. J. Remote Sens. 2017, 38, 2218-2230.
24. Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; 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.
25. 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, 10395-10412.
26. Stanton, C.; Starek, M.J.; Elliott, N.; Brewer, M.; Maeda, M.M.; Chu, T. Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment. J. Appl. Remote Sens. 2017, 11, 026035.
27. Chu, T.; Starek, M.J.; Brewer, M.J.; Masiane, T.; Murray, S.C. UAS imaging for automated crop lodging detection: A case study over an experimental maize field. In Proceedings of the SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, Anaheim, CA, USA, 10-11 April 2017.
28. 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. 2016, 8, 1031.
29. Willkomm, M.; Bolten, A.; Bareth, G. Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: Case study of field-grown rice in north Rhine-Westphalia, Germany. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 1071-1077.
30. 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, 245-259.
31. Calculating Harvest Yields. Purdue University Research Repository. Available online: https://purr.purdue.edu/publications/1600/serve/1/3332?el=3&download=1 (accessed on 25 May 2017).
32. Starek, M.J.; Davis, T.; Prouty, D.; Berryhill, J. Small-scale UAS for geoinformatics applications on an island campus. In Proceedings of the 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Corpus Christi, TX, USA, 20-21 November 2014.
33. Li, W.; Niu, Z.; Gao, S.; Huang, N.; Chen, H. Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest. Forests 2014, 5, 1910-1930.
34. Næsset, E.; Bollandsås, O.M.; Gobakken, T.; Gregoire, T.G.; Ståhl, G. Model-assisted estimation of change in forest biomass over an 11 year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide "activity data". Remote Sens. Environ. 2013, 128, 299-314.
35. Pike, R.J.;Wilson, S.E. Elevation-relief ratio, hypsometric integral, and geomorphic area-altitude analysis. Geol. Soc. Am. Bull. 1971, 82, 1079-1084.
36. Montealegre, A.L.; Lamelas, M.T.; Tanase, M.A.; de la Riva, J. Forest fire severity assessment using ALS data in a mediterranean environment. Remote Sens. 2014, 6, 4240-4265.
37. Parker, G.G.; Russ, M.E. The canopy surface and stand development: Assessing forest canopy structure and complexity with near-surface altimetry. For. Ecol. Manag. 2004, 189, 307-315.
38. Li, W.; Niu, Z.; Chen, H.; Li, D. Characterizing canopy structural complexity for the estimation of maize LAI based on ALS data and UAV stereo images. Int. J. Remote Sens. 2017, 38, 2106-2116.
39. Nielsen, R.L. Root Lodging Concerns in Corn. Purdue University. Available online: https://www.agry.purdue.edu/ext/corn/news/articles.02/RootLodge-0711.html (accessed on 13 March 2017).
40. Tang, L. Robotic Technologies for Automated High-Throughput Plant Phenotyping. Iowa State University. Available online: http://www.me.iastate.edu/smartplants/files/2013/12/Robotic-Technologies-for- Automated-High-Throughput-Phenotyping-Tang.pdf (accessed on 26 April 2017).
41. Gnädinger, F.; Schmidhalter, U. Digital counts of maize plants by unmanned aerial vehicles (UAVs). Remote Sens. 2017, 9, 544.
42. Benassi, F.; Dall'Asta, E.; Diotri, F.; Forlani, G.; Morra di Cella, U.; Roncella, R.; Santise, M. Testing accuracy and repeatability of UAV blocks oriented with GNSS-supported aerial triangulation. Remote Sens. 2017, 9, 172.
43. Romeo, J.; Pajares, G.; Montalvo, M.; Guerrero, J.M.; Guijarro, M.; Ribeiro, A. Crop row detection in maize fields inspired on the human visual perception. Sci. World J. 2012, 2012, 484390.