Potato Research

, 54:237 | Cite as

Analysis of Potato Canopy Coverage as Assessed Through Digital Imagery by Nonlinear Mixed Effects Models

  • Carlos Ricardo BojacáEmail author
  • Sady Javier García
  • Eddie Schrevens


The characterization of the dynamics of canopy coverage represents a relevant matter of study in the field of crop physiology. The objective of this work was to calibrate a model able to simulate potato canopy coverage as a function of thermal time, but including the error structure in such model. This was accomplished by using a mixed effects modelling approach where random effects were added to the average response model. By applying this modelling approach, the structure of the data was taken into account. Calibration data for the model were obtained from canopy coverage estimates derived from image processing analysis. Digital images were taken periodically within 11 potato fields located in the Mantaro Valley (Peru) during the 2005–2006 growing season. This model gave a better fit in comparison with the traditional fixed parameters model. An additional uncertainty analysis with the objective of estimating the confidence region for the predictions of the mixed effects model was carried out. By exploring the data structure, a more comprehensive overview of the potato canopy coverage was achieved with the mixed effects model.


Canopy cover Digital imaging Image analysis Mixed effects models 



The present study was realized thanks to funds from the Flemish Interuniversity Council (VLIR) through the own initiative project ZEIN2004PR295 titled “Optimization and implementation of green waste compost applications in sustainable agriculture in the high tropics.”


  1. Becker FA, Klein AW, Winkler R, Jung B, Bleiholder H, Schmider F (1999) The degree of ground coverage by arable crops as a help in estimating the amount of spray solution intercepted by the plants. Nachrbl Dtsch Pflanzenschutzd 51(9):237–242Google Scholar
  2. Bojacá CR (2009) Generic modeling approaches to technical sustainability assessment on field level for farmings systems in the high Andean tropics. Dissertation, Katholieke Universiteit LeuvenGoogle Scholar
  3. Boyd NS, Gordon R, Martin RC (2002) Relationships between leaf area index and ground cover in potato under different management conditions. Potato Res 45:117–129CrossRefGoogle Scholar
  4. Brabec M, Konar O, Pelikan E, Maly M (2008) A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. Int J Forecasting 24(4):659–678CrossRefGoogle Scholar
  5. Campillo C, Prieto MH, Daza C, Moñino MJ, García MI (2008) Using digital images to characterize canopy coverage and light interception in a processing tomato crop. HortScience 43:1780–1786Google Scholar
  6. Davidian M, Giltinan DM (2003) Nonlinear models for repeated measurement data: an overview and update. J Agric Biol Envir S 8(4):387–419CrossRefGoogle Scholar
  7. de la Casa A, Ovando G, Bressanini L, Rodríguez A, Martínez A (2007) Use of leaf area index and ground cover to estimate intercepted radiation in potato. Agricultura Técnica 67(1):78–85Google Scholar
  8. Demidenko E (2004) Statistical image analysis. In: Demidenko E (ed) Mixed models: theory and applications, 1st edn. Wiley, Hoboken, pp 595–639CrossRefGoogle Scholar
  9. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
  10. García SJ (2011) Evaluating the biophysical resource management strategies of the agro-ecosystem in farm communities of the Mantaro Valley, Central Andes of Peru. Dissertation, Katholieke Universiteit LeuvenGoogle Scholar
  11. Johnson RA, Wichern DW (1998) Applied multivariate statistical analysis. Prentice Hall, Upper Saddle RiverGoogle Scholar
  12. Lindstrom MJ, Bates DM (1990) Nonlinear mixed effects for repeated measures data. Biometrics 46:673–687PubMedCrossRefGoogle Scholar
  13. Maas SJ, Rajan N (2008) Estimating ground cover of field crops using medium-resolution multispectral satellite imagery. Agron J 100(2):320–327CrossRefGoogle Scholar
  14. Nasrullahzadeh S, Ghassemi-Golezani K, Javanshir A, Valizade M, Shakiba MR (2007) Effects of shade stress on ground cover and grain yield of faba bean (Vicia faba L.). J Food Agric Environ 5(1):337–340Google Scholar
  15. Oshaughnessy SA, Evett SR, Colaizzi PD, Howell TA, Gowda P (2008) Estimating crop canopy coverage of cotton plants within the FOV of an infrared thermometer using a two band photodiode sensor. ASA-CSSA-SSSA Annual Meeting Abstracts, American Society of Agronomy, Soil Science Society of America, and Crop Science Society of America, HoustonGoogle Scholar
  16. Peek MS, Russek-Cohen E, Wait DA, Forseth IN (2002) Physiological response curve analysis using nonlinear mixed models. Oecologia 132(2):175–180CrossRefGoogle Scholar
  17. Pulido S, Bojacá CR, Salazar M, Chaves B (2008) Node appearance model for Lulo (Solanum quitoense Lam.) in the high altitude tropics. Biosyst Eng 101:383–387CrossRefGoogle Scholar
  18. Purcell LC (2000) Soybean canopy coverage and light interception measurements using digital imagery. Crop Sci 40(3):834–837CrossRefGoogle Scholar
  19. Rajan N, Maas SJ (2009) Mapping ground cover using airborne multispectral digital imagery. Precis Agric 10:304–318CrossRefGoogle Scholar
  20. Salako EK, Olowokere EA, Tian G, Kirchhof G, Osiname O (2007) Ground cover by three crops cultivated on marginal lands in southwestern Nigeria and implications for soil erosion. Span J Agric Res 5(4):497–505Google Scholar
  21. Sarlangue T, Purcell LC, Karcher DE (2008) Estimating the fraction of radiation intercepted in maize by digital-image analysis. Maydica 53(1):63–68Google Scholar
  22. SAS Institute Inc. (2008) SAS/STAT user's guide. SAS Institute, Cary, NCGoogle Scholar
  23. Stewart A, Edmisten K, Wells R, Collins G (2007) Measuring canopy coverage with digital imaging. Comm Soil Sci Plant Anal 38(7):895–902CrossRefGoogle Scholar
  24. The Mathworks, Inc. (2009) Matlab. Natick, MAGoogle Scholar
  25. Trincado G, VanderSchaaf CL, Burkhart HE (2007) Regional mixed-effects height–diameter models for loblolly pine (Pinus taeda L.) plantations. Eur J Forest Res 126:253–262CrossRefGoogle Scholar
  26. Vyas SP, Steven MD, Jaggard KW, Xu H (2003) Comparison of sugar beet crop cover estimates from radar and optical data. Int J Remote Sens 24(5):1071–1082CrossRefGoogle Scholar
  27. Yang RC (2010) Towards understanding and use of mixed-model analysis of agricultural experiments. Can J Plant Sci 90:605–627CrossRefGoogle Scholar
  28. Yuan FM, Bland WL (2005) Comparison of light- and temperature-based index models for potato (Solanum tubersoum L.) growth and development. Amer J Potato Res 82:345–352CrossRefGoogle Scholar
  29. Zakaluk R, Sri Ranjan R (2007) Artificial neural network modelling of leaf water potential for potatoes using RGB digital images: a greenhouse study. Potato Res 49:255–272CrossRefGoogle Scholar

Copyright information

© EAPR 2011

Authors and Affiliations

  • Carlos Ricardo Bojacá
    • 1
    Email author
  • Sady Javier García
    • 2
  • Eddie Schrevens
    • 3
  1. 1.Centro de Bio-Sistemas, Facultad de Ciencias Naturales e IngenieríaUniversidad de Bogotá Jorge Tadeo LozanoChíaColombia
  2. 2.Facultad de AgronomíaUniversidad Nacional Agraria La MolinaLimaPeru
  3. 3.Department of Biosystems, Faculty of Bioscience EngineeringKatholieke Universiteit Leuven, Geo-InstituteHeverleeBelgium

Personalised recommendations