3D participatory sensing with low-cost mobile devices for crop height assessment -a comparison with terrestrial laser scanning data

PLoS ONE

Volumn 11, Issue 4, 2016, Pages

  Marx, Sabrina ^a   Hämmerle, Martin ^a   Klonner, Carolin ^a   Höfle, Bernhard ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL MANAGEMENT; AGRICULTURAL PROCEDURES; ARTICLE; COGNITION; CONTROLLED STUDY; GENERAL DEVICE; GERMANY; GLOBAL POSITIONING SYSTEM; INTERMETHOD COMPARISON; MACHINE LEARNING; MAIZE; PARTICIPATORY SENSING; PLANT HEIGHT; REMOTE SENSING; SMARTPHONE; TERRESTRIAL LASER SCANNING; AGRICULTURE; COMPARATIVE STUDY; CROP; GROWTH, DEVELOPMENT AND AGING; HUMAN; PROCEDURES; THEORETICAL MODEL;

AGRICULTURE; CROPS, AGRICULTURAL; HUMANS; MODELS, THEORETICAL; REMOTE SENSING TECHNOLOGY; ZEA MAYS;

EID: 84963600751     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0152839     Document Type: Article

References (81)

1. Phalkey RK, Aranda-Jan C, Marx S, Höfle B, Sauerborn R. Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proceedings of the National Academy of Sciences of the United States of America 2015; 112(33):E4522-9. doi: 10.1073/pnas.1409769112 PMID: 26216952
2. Goodchild MF. Citizens as sensors: The world of volunteered geography. GeoJournal 2007; 69(4):211-21.
3. Corburn J. Confronting the challenges in reconnecting urban planning and public health. American Journal of Public Health 2004; 94(4):541-6. PMID: 15053998
4. Confalonieri R, Bregaglio S, Rosenmund AS, Acutis M, Savin I. A model for simulating the height of rice plants. European Journal of Agronomy 2011; 34(1):20-5.
5. World Bank. ICT for data collection and monitoring & evaluation: Opportunities and guidance on mobile applications for forest and agricultural sectors 2013. Available: Http://documents.worldbank.org/curated/en/2013/12/18658539/ict-data-collection-monitoring-evaluation-opportunities-guidancemobile-applications-forest-agricultural-sectors.
6. Marx S, Phalkey R, Aranda C, Profe J, Sauerborn R, Höfle B. Geographic information analysis and web-based geoportals to explore malnutrition in Sub-Saharan Africa: A systematic review of approaches. BMC Public Health 2014; 14(1):1189.
7. FAO, WFP, IFAD. The state of food insecurity in the world 2012: Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition 2012. Available: Http://www.fao.org/docrep/016/i3027e/i3027e00.htm.
8. USAID. Crowdsourcing applications for agricultural development in Africa 2013:1-6. Available: Http://pdf.usaid.gov/pdf-docs/PA00J7P7.pdf.
9. Goma HC, Rahim K, Nangendo G, Riley J, Stein A. Participatory studies for agro-ecosystem evaluation. Agriculture, Ecosystems & Environment 2001; 87(2):179-90.
10. Aragó Galindo P, Granell C, Molin PG, Huerta Guijarro J. Participative site-specific agriculture analysis for smallholders. Precision Agriculture 2012; 13(5):594-610.
11. World Bank. Reaching the rural poor: A renewed strategy for rural development-a summary. Available: Http://documents.worldbank.org/curated/en/2003/08/5271834/reaching-rural-poor-renewedstrategy-rural-development-summary.
12. Sui D, Elwood S, Goodchild M, editors. Crowdsourcing geographic knowledge: Springer Netherlands; 2013.
13. Ferster CJ, Coops NC. A review of earth observation using mobile personal communication devices. Computers & Geosciences 2013; 51:339-49.
14. Haklay M. Citizen science and volunteered geographic information: Overview and typology of participation. In: Sui D, Elwood S, Goodchild M, editors. Crowdsourcing geographic knowledge: Springer Netherlands; 2013. p. 105-22.
15. Boulos M, Wheeler S, Tavares C, Jones R. How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX. BioMedical Engineering OnLine 2011; 10(1):24.
16. Eskenazi B, Quirós-Alcalá L, Lipsitt JM, Wu LD, Kruger P, Ntimbane T et al. mSpray: A mobile phone technology to improve malaria control efforts and monitor human exposure to malaria control pesticides in Limpopo, South Africa. Environment International 2014; 68:219-26. doi: 10.1016/j.envint.2014.03. 003 PMID: 24769412
17. Ramesh MV, Jacob A, D. AR. Participatory sensing platform to revive communication network in postdisaster scenario. Kaohsiung, Taiwan; 2012. 21st Annual Wireless and Optical Communications Conference.
18. Borges DM, Zacharias P. PartSense: A participatory sensing platform and its instantiation KA-Feedback. Braunschweig, Germany; 2012. Geoinformatik 2012.
19. Boulos M, Resch B, Crowley DN, Breslin JG, Sohn G, Burtner R et al. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: Trends OGC standards and application examples. International Journal of Health Geographics 2011; 10:67. doi: 10.1186/1476-072x-10-67 PMID: 22188675
20. Tsou M, Sun C. Mobile GIServices applied to disaster management. Dynamic and Mobile GIS 2006:1-23.
21. Craig WJ, Harris TM, Weiner D. Community participation and geographical information systems. London: Taylor & Francis; 2001.
22. Batchelor S, Scott N, Manfre C, Lopez AV, Edwards D. Is there a role for mobiles to support sustainable agriculture in Africa? Stockholm, Sweden; 2014. 2nd International Conference on ICT for Sustainability.
23. Rafoss T, Sælid K, Sletten A, Gyland LF, Engravslia L. Open geospatial technology standards and their potential in plant pest risk management-GPS-enabled mobile phones utilising open geospatial technology standards Web Feature Service Transactions support the fighting of fire blight in Norway. Computers and Electronics in Agriculture 2010; 74(2):336-40.
24. Kotovirta V, Toivanen T, Tergujeff R, Huttunen M. Participatory sensing in environmental monitoring: Experiences. Palermo, Italy; 2012. Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.
25. Kotovirta V, Toivanen T, Tergujeff R, Huttunen M. EnviObserver-Participatory sensing tool for environmental monitoring. Otaniemi, Espoo; 2011. Finnish Remote Sensing Days.
26. Aanensen DM, Huntley DM, Feil EJ, Al-Own F, Spratt BG. EpiCollect: Linking smartphones to web applications for epidemiology, ecology and community data collection. PLOS ONE 2009; 4(9):e6968. doi: 10.1371/journal.pone.0006968 PMID: 19756138
27. Hartung C, Anokwa Y, Brunette W, Lerer A, Tseng C, Borriello G. Open Data Kit: Tools to build information services for developing regions. Washington, USA; 2010. ICTD.
28. Enenkel M, See L, Karner M, Álvarez M, Rogenhofer E, Baraldès-Vallverdú C et al. Food security monitoring via mobile data collection and remote sensing: Results from the Central African Republic. PLOS ONE 2015; 10(11):e0142030. doi: 10.1371/journal.pone.0142030 PMID: 26581042
29. Delgado JA, Kowalski K, Tebbe C. The first Nitrogen Index app for mobile devices: Using portable technology for smart agricultural management. Computers and Electronics in Agriculture 2013; 91:121-3.
30. Confalonieri R, Foi M, Casa R, Aquaro S, Tona E, Peterle M et al. Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Computers and Electronics in Agriculture 2013; 96:67-74.
31. Luo J, Joshi D, Yu J, Gallagher A. Geotagging in multimedia and computer vision: A survey. Multimedia Tools and Applications 2011; 51(1):187-211.
32. West R, Malpas J, Powell C, Andersion, D., Leed, J. Calflora. Available: Http://www.calflora.org/.
33. Higuera S, De Frutos, Castro M. Using smartphones as a very low-cost tool for road inventories. Transportation Research Part C: Emerging Technologies 2014; 38:136-45.
34. Schmid F, Cai C, Frommberger L. A new micro-mapping method for rapid VGI-ing of small geographic features. Columbus, OH, USA. GIScience.
35. Frommberger L, Schmid F, Cai C. Micro-mapping with smartphones for monitoring agricultural development. Bangalore, India; 2013. Symposium on Computing for Development.
36. Heipke C. Crowdsourcing geospatial data. ISPRS Centenary Celebration Issue 2010; 65(6):550-7.
37. Klonner C, Barron C, Neis P, Höfle B. Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments. International Journal of Digital Earth 2014; 8 (2):153-71.
38. Jackson S, Mullen W, Agouris P, Crooks A, Croitoru A, Stefanidis A. Assessing completeness and spatial error of features in volunteered geographic information. IJGI 2013; 2(2):507-30.
39. Dorn H, Törnros T, Zipf A. Quality evaluation of VGI using authoritative data: A comparison with land use data in Southern Germany. IJGI 2015; 4(3):1657-71.
40. Reddy S, Parker A, Hyman J, Burke J, Estrin D, Hansen M. Image browsing, processing, and clustering for participatory sensing: Lessons from a dietsense prototype. Cork, Irland; 2007. EmNets.
41. Degrossi LC, Albuquerque JP, Fava MC, Mendiondo EM. Flood Citizen Observatory: A crowdsourcingbased approach for flood risk management in Brazil. Vancouver, Canada; 2014. 26th International Conference on Software Engineering and Knowledge Engineering.
42. Sinn P. Zur Landschaft und Geschichte von Heidelberg-Handschuhsheim: Gesammelte Aufsätze. Ubstadt-Weiher, Heidelberg, Basel: Verlag Regionalkultur; 2013.
43. Schlatter BE, Hurd AR. Geocaching: 21st-century hide-and-seek. Journal of Physical Education, Recreation & Dance 2005; 76(7):28-32.
44. Garmin Ltd. OREGON series: Owner's manual; 2009. Available: Http://static.garmincdn.com/pumac/2560-OwnersManual.pdf.
45. Zandbergen PA, Barbeau SJ. Positional accuracy of assisted GPS data from high-sensitivity GPSenabled mobile phones. J. Navigation 2011; 64(03):381-99.
46. Zandbergen PA. Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning. Transactions in GIS 2009; 13:5-25.
47. Tilly N, Hoffmeister D, Cao Q, Huang S, Lenz-Wiedemann V, Miao Y et al. Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. Journal of Applied Remote Sensing 2014; 8(1):083671.
48. Weber TC, Boss DE. Use of LiDAR and supplemental data to estimate forest maturity in Charles County, MD, USA. Forest Ecology and Management 2009; 258(9):2068-75.
49. Saeys W, Lenaerts B, Craessaerts G, De Baerdemaeker J. Estimation of the crop density of small grains using LiDAR sensors. Biosystems Engineering 2009; 102(1):22-30.
50. Ehlert D, Heisig M. Sources of angle-dependent errors in terrestrial laser scanner-based crop stand measurement. Computers and Electronics in Agriculture 2013; 93:10-6.
51. Höfle B. Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection. IEEE Geoscience and Remote Sensing Letters 2014; 11(1):94-8.
52. Hämmerle M, Höfle B. Effects of reduced terrestrial LiDAR point density on high-resolution grain crop surface models in precision agriculture. Sensors 2014; 14(12):24212-30. doi: 10.3390/s141224212 PMID: 25521383
53. 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):538-60.
54. Wehr A, Lohr U. Airborne laser scanning: An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing 1999; 54(2-3):68-82.
55. Anokwa Y, Hartung C, Brunette W, Borriello G, Lerer A. Open source data collection in the developing world. Computer 2009; 42(10):97-9.
56. Brunette W, Sundt M, Dell D, Chaudhri R, Breit N, Borriello G. Open Data Kit 2.0: Expanding and refining information services for developing regions. Jekyll Island, USA. ACM HotMobile.
57. Rajput ZA, Mbugua S, Amadi D, Chepngeno V, Saleem JJ, Anokwa Y et al. Evaluation of an Androidbased mHealth system for population surveillance in developing countries. Journal of the American Medical Informatics Association: JAMIA 2012; 19(4):655-9. doi: 10.1136/amiajnl-2011-000476 PMID: 22366295
58. ESRI. ArcGIS Help; 2013. Available: Http://resources.arcgis.com/en/help/main/10.1/.
59. Mendez D, Labrador M, Ramachandran K. Data interpolation for participatory sensing systems. Pervasive and Mobile Computing 2013; 9(1):132-48.
60. Isaaks EH, Srivastava RM. Applied Geostatistics. New York: Oxford University Press; 1989.
61. Pilz J, Spöck G. Why do we need and how should we implement Bayesian kriging methods. Stochastic Environmental Research and Risk Assessment 2008; 22(5):621-32.
62. Krivoruchko K. Empirical Bayesian Kriging; 2012. ArcUser. Available: Http://www.esri.com/news/arcuser/1012/empirical-byesian-kriging.html.
63. Sritarapipat T, Rakwatin P, Kasetkasem T. Automatic rice crop height measurement using a field server and digital image processing. Sensors 2014; 14(1):900-26. doi: 10.3390/s140100900 PMID: 24451465
64. Sommer C, Strähle C, Köthe U, Hamprecth, F. A. ilastik: Interactive learning and segmentation toolkit. Chicago, U.S.A.; 2011. Eighth IEEE International Symposium on Biomedical Imaging.
65. Benavent X, Dura E, Vegara F, Domingo J. Mathematical morphology for color images: An imagedependent approach. Mathematical Problems in Engineering 2012; 2012(3):1-18.
66. RIEGL. RiSCAN PRO; 2014. Available: Http://www.riegl.com/products/software-packages/riscan-pro/.
67. TU Wien. OPALS: Orientation and processing of airborne laser scanning data; 2014. Available: Http://geo.tuwien.ac.at/opals.
68. Riaño D. Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment 2003; 86(2):177-86.
69. Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. Hoboken, NJ, USA: John Wiley & Sons, Inc; 1987.
70. Bauer C. On the (in-)accuracy of GPS measures of smartphones: A study of running tracking applications. Vienna, Austria; 2013. Proceedings of MoMM.
71. Kim J, Lee S, Ahn H, Seo D, Park S, Choi C. Feasibility of employing a smartphone as the payload in a photogrammetric UAV system. ISPRS Journal of Photogrammetry and Remote Sensing 2013; 79:1-18.
72. Hwang S, Yu D. GPS localization improvement of smartphones using built-in sensors. International Journal of Smart Home 2012; 6(3):1-8.
73. HedgecockW, Maroti M, Sallai J, Volgyesi P, Ledeczi A. High-accuracy differential tracking of low-cost GPS receivers. New York, USA; 2013. MobiSys.
74. Lwin KK. Web-based GIS system for real-time field data collection using personal mobile phone. Journal of Geographic Information System 2011; 03(04):382-9.
75. Brovelli MA, Minghini M. and Zamboni G. Web-based Participatory GIS with data collection on the field-A prototype architecture. OSGEO Journal; 13:29-33.
76. Breiman L. Random Forests. Machine Learning 2001; 45(1):5-32.
77. Hillen F, Höfle B. Geo-reCAPTCHA: Crowdsourcing large amounts of geographic information from earth observation data. International Journal of Applied Earth Observation and Geoinformation 2015; 40:29-38.
78. Vijayanarasimhan S, Grauman K. Large-scale live active learning: Training object detectors with crawled data and crowds. International Journal of Computer Vision 2014; 108(1-2):97-114.
79. Dandois JP, Ellis EC. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment 2013; 136:259-76.
80. Zarco-Tejada PJ, 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. European Journal of Agronomy 2014; 55:89-99.
81. Micheletti N, Chandler JH, Lane SN. Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone. Earth Surface Processes and Landforms 2014: 14.