Regression kriging for improving crop height models fusing ultra-sonic sensing with UAV imagery

Remote Sensing

Volumn 9, Issue 7, 2017, Pages

  Schirrmann, Michael ^a   Hamdorf, André ^a   Giebel, Antje ^a   Gleiniger, Franziska ^a   Pflanz, Michael ^a,b   Dammer, Karl Heinz ^a  

^a LEIBNIZ INSTITUTE FOR AGRICULTURAL ENGINEERING AND BIOECONOMY ATB   (Germany)

^b INSTITUTE FOR EPIDEMIOLOGY AND PATHOGEN DIAGNOSTICS   (Germany)

Author keywords

Crop monitoring; Crop surface model; Geostatistics; Precision agriculture; Unmanned aerial vehicle

Indexed keywords

AGRICULTURE; CROPS; DATA FUSION; INTERPOLATION; REGRESSION ANALYSIS; VEHICLES;

AGRONOMIC PARAMETERS; CROP MONITORING; DECISION MAKING PROCESS; GEO-STATISTICS; GEOSTATISTICAL ANALYSIS; GROUND-BASED VEHICLES; PRECISION AGRICULTURE; SURFACE MODELING;

UNMANNED AERIAL VEHICLES (UAV);

EID: 85022329720     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9070665     Document Type: Article

References (50)

1. Heege, H.J. Precision in Crop Farming; Springer: Dordrecht, The Netherlands, 2013
2. Oerke, E.-C.; Gerhards, R.; Menz, G.; Sikora, R.A. Precision Crop Protection-The Challenge and Use of Heterogeneity; Springer: Dordrecht, The Netherlands, 2014
3. Girma, K.; Martin, K.L.; Anderson, R.H.; Arnall, D.B.; Brixey, K.D.; Casillas, M.A.; Chung, B.; Dobey, B.C.; Kamenidou, S.K.; Kariuki, S.K.; et al. Mid-season prediction of wheat-grain yield potential using plant, soil, and sensor measurements. J. Plant Nutr. 2006, 29, 873-897
4. Lati, R.N.; Filin, S.; Eizenberg, H. Estimating plant growth parameters using an energy minimization-based stereovision model. Comput. Electron. Agric. 2013, 98, 260-271
5. 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
6. Schirrmann, M.; Giebel, A.; Gleiniger, F.; Pflanz, M.; Lentschke, J.; Dammer, K.-H. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens. 2016, 8, 706
7. Portz, G.; Amaral, L.R.; Molin, J.P.; Adamchuk, V.I. Field comparison of ultrasonic and canopy reflectance sensors used to estimate biomass and N-uptake in sugarcane. In Precision Agriculture'13; Springer: Dordrecht, The Netherlands, 2013; pp. 111-117
8. Floreano, D.;Wood, R.J. Science, technology and the future of small autonomous drones. Nature 2015, 521, 460-466
9. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712
10. Dandois, J.P.; Ellis, E.C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 2013, 136, 259-276
11. Rosnell, T.; Honkavaara, E. Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera. Sensors 2012, 12, 453-480
12. Grenzdörffer, G.J. Crop height determination with UAS point clouds. ISPRS-International Archives of the Photogrammetry. Remote Sens. Spat. Inf. Sci. 2014, XL-1, 135-140
13. Bendig, J.; Bolten, A.; Bareth, G. UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability Monitoring des Pflanzenwachstums mit Hilfe multitemporaler und hoch auflösender Oberflächenmodelle von Getreidebeständen auf Basis von Bildern aus UAV-Befliegungen. Photogramm. Fernerkund. Geoinf. 2013, 2013, 551-562
14. 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, 10335-10355
15. Dammer, K.-H. Real-time variable-rate herbicide application for weed control in carrots. Weed Res. 2016, 56, 237-246
16. Thoele, H.; Ehlert, D. Biomass related nitrogen fertilization with a crop sensor. Appl. Eng. Agric. 2010, 26, 769-775
17. Schirrmann, M.; Joschko, M.; Gebbers, R.; Kramer, E.; Zörner, M.; Barkusky, D.; Timmer, J. Proximal soil sensing-A contribution for species habitat distribution modelling of earthworms in agricultural soils? PLoS ONE 2016, 11, e0158271
18. Tackenberg, M.; Volkmar, C.; Dammer, K.-H. Sensor-based variable-rate fungicide application in winter wheat: Sensor-based variable-rate fungicide application in winter wheat. Pest Manag. Sci. 2016, 72, 1888-1896
19. Shibayama, M.; Akiyama, T.; Munakata, K. A portable field ultrasonic sensor for crop canopy characterization. Remote Sens. Environ. 1985, 18, 269-279
20. Scotford, I.M.; Miller, P.C.H. Combination of spectral reflectance and ultrasonic sensing to monitor the growth of winter wheat. Biosyst. Eng. 2004, 87, 27-38
21. Andújar, D.; Weis, M.; Gerhards, R. An ultrasonic system for weed detection in cereal crops. Sensors 2012, 12, 17343-17357
22. Reusch, S. Use of ultrasonic transducer for on-line biomass estimation in winter wheat. In Precision Agriculture '09: Papers Presented at the 7th European Conference on Precision Agriculture; Wageningen Academic Publishers: Wageningen, the Netherlands, 2009; pp. 169-175
23. Maertens, K.; Reyns, P.; de Clippel, J.; de Baerdemaeker, J. First experiments on ultrasonic crop density measurement. J. Sound Vib. 2003, 266, 655-665
24. Andrade-Sanchez, P.; Gore, M.A.; Heun, J.T.; Thorp, K.R.; Carmo-Silva, A.E.; French, A.N.; Salvucci, M.E.; White, J.W. Development and evaluation of a field-based high-throughput phenotyping platform. Funct. Plant Biol. 2014, 41, 68
25. 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
26. Ehlert, D.; Adamek, R.; Horn, H.-J. Laser rangefinder-based measuring of crop biomass under field conditions. Precis. Agric. 2009, 10, 395-408
27. 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, 74-84
28. Bilewski, M.; Putynkowski, G. Application of ultrasonic distance sensors for measuring height as a tool in unmanned aerial vehicles with a stabilized position in the vertical plane. Zeszyty Naukowe Akademia Morska w Szczecinie 2016
29. 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
30. Tilly, N.; Aasen, H.; Bareth, G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens. 2015, 7, 11449-11480
31. Hengl, T.; Heuvelink, G.B.M.; Rossiter, D.G. About regression-kriging: From equations to case studies. Comput. Geosci. 2007, 33, 1301-1315
32. Hengl, T.; Heuvelink, G.B.M.; Stein, A. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 2004, 120, 75-93
33. Odeh, I.O.; McBratney, A.B.; Chittleborough, D.J. Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma 1995, 67, 215-226
34. Cao, S.; Lu, A.; Wang, J.; Huo, L. Modeling and mapping of cadmium in soils based on qualitative and quantitative auxiliary variables in a cadmium contaminated area. Sci. Total Environ. 2017, 580, 430-439
35. Ge, Y.; Thomasson, J.A.; Sui, R.;Wooten, J. Regression-kriging for characterizing soils with remotesensing data. Front. Earth Sci. 2011
36. Sun, W.; Whelan, B.M.; Minasny, B.; McBratney, A.B. Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution. J. Plant Nutr. Soil Sci. 2012, 175, 212-220
37. Moral, F.J.; Terrón, J.M.; da Silva, J.R.M. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Tillage Res. 2010, 106, 335-343
38. Zhang, C.; Pei, H. Oil spills boundary tracking using universal kriging and model predictive control by UAV. In Proceedings of the 11thWorld Congress on Intelligent Control and Automation (WCICA 2014), Shenyang, China, 29 June-4 July 2014; pp. 633-638
39. Zheng, Y.; Yu, J.; Kang, S.B.; Lin, S.; Kambhamettu, C. Single-image vignetting correction using radial gradient symmetry. In Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, AK, USA, 24-26 June 2008; pp. 1-8
40. Lelong, C.C.D.; Burger, P.; Jubelin, G.; Roux, B.; Labbé, S.; Baret, F. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 2008, 8, 3557-3585
41. Verhoeven, G. Taking computer vision aloft-Archaeological three-dimensional reconstructions from aerial photographs with photoscan. Archaeol. Prospect. 2011, 18, 67-73
42. Hamuda, E.; Glavin, M.; Jones, E. A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 2016, 125, 184-199
43. Torres-Sánchez, J.; Peña, J.M.; de Castro, A.I.; López-Granados, F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 2014, 103, 104-113
44. Webster, R.; Oliver, M.A. Geostatistics for Environmental Scientists (Statistics in Practice), 2nd ed.; Wiley: Chichester, UK, 2007
45. Matheron, G. Traité de Géostatistique Appliquée, Tome I: Mémoires du Bureau de Recherches Géologiques et Minières, No. 14; Editions Technip: Paris, France, 1962; p. 333
46. Pebesma, E.J. Multivariable geostatistics in S: The gstat package. Comput. Geosci. 2004, 30, 683-691
47. R Core Team. R: A Language and Environment for Statistical Computing; R Core Team: Vienna, Austria, 2015; Available online: http://www. R-project.org/(accessed on 6 June 2017)
48. Aziz, S.A.; Steward, B.L.; Birrell, S.J.; Shrestha, D.S.; Kaspar, T.C. Ultrasonic sensing for corn plant canopy characterization. In Proceedings of the ASAE/CSAE Annual International Meeting, Ottawa, ON, Canada, 1-4 August 2004; Volume 8
49. Grenzdörffer, G.; Zacharias, P. Bestandeshöhenermittlung landwirtschaftlicher Kulturen aus UAS-Punktwolken. DGPF Tagungsband 2014, 23, 1-8
50. Zhu, Q.; Lin, H.S. Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes. Pedosphere 2010, 20, 594-606