Precision Agriculture

, Volume 18, Issue 1, pp 95–110 | Cite as

A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet



Increasing agricultural efficiency in a sustainable manner will contribute to feed a growing population under limited land, nutrient and water resources. Water scarcity and the increasing social concern for this resource are already requiring more sophisticated irrigation and decision-support systems. To address the heterogeneity in crop water status in a commercial field, precision irrigation requires accurate information about crops (e.g., crop water status), soil (e.g., moisture content) and weather (e.g., wind speed and vapor pressure deficit). Numerous studies have shown that plant canopy temperature can be used to derive reliable plant water stress indicators, thus making it a promising tool for irrigation water management. However, efficient and cost-effective measurement techniques are still lacking. This paper assesses the potential of infrared thermometry and thermal imaging for monitoring plant water stress in a commercial sugar beet field by comparing canopy temperature data acquired from a conventional thermal camera with an inexpensive infrared sensor, both mounted on a rotary-wing unmanned aerial vehicle (UAV). Measurements were taken at various phenological stages of the sugar beet growing season. Laboratory tests were performed to determine the key features for accurate temperature measurements and flight altitude. Experiments were conducted in 2014 and 2015 in experimental and commercial sugar beet fields in Southwestern Spain to (i) develop an affordable infrared temperature system suitable for mounting on a UAV to obtain thermal information, (ii) compare sugar beet canopy temperature measurements collected with the low-cost platform with those obtained from a conventional thermal camera, both mounted on a rotary-wing UAV, (iii) identify the factors that will limit the use of the low-cost system to derive temperature-based water stress indices. To accomplish these objectives, well-watered and deficit irrigated plots were established. Results indicated that the lightweight canopy temperature system was robust and reliable, although there were some constraints related to weather conditions and delimitation of the area covered by the infrared sensor.


Infrared thermometer sensor UAV Remote sensing Prescription map Precision irrigation 



This work is part of the research project “P12-AGR-1227″, which is financially promoted by the “Andalusian Government”.


  1. Ahrens, B., Hansson, K., Solly, E. F., & Schrumpf, M. (2014). Reconcilable differences: A joint calibration of fine-root turnover times with radiocarbon and minirhizotrons. New Phytologist, 204, 932–942.CrossRefPubMedGoogle Scholar
  2. Andrade-Sanchez, P., Gore, M. A., Heun, J. T., Thorp, K. R., Carmo-Silva, A. E., French, A. N., et al. (2014). Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology, 41(1), 68–79.CrossRefGoogle Scholar
  3. Ballester, C., Jimenez-Bello, M., Castel, J., & Intrigliolo, D. (2013). Usefulness of thermography for plant water stress detecction in citrus and persimmon trees. Agricultural and Forest Meteorology, 168, 120–129.CrossRefGoogle Scholar
  4. Bellvert, J., Zarco-Tejada, P. J., Girona, J., & Fereres, E. (2014). Mapping crop water stress index in a ‘Pinot-noir’ vineyard: Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15(4), 361–376.CrossRefGoogle Scholar
  5. Berk, A., Anderson, G., Acharya, P., Chetwynd, J., Bernstein, L., Shettle, E., et al. (1999). MODTRAN4 user’s manual. Bedford: Air Force Research Labaratory Hanscom AFB.Google Scholar
  6. Berni, J., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009a). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738.CrossRefGoogle Scholar
  7. Berni, J., Zarco-Tejada, P. J., Suarez, L., González-Dugo, V., & Fereres, E. (2009b). Remote sensing of vegetation from UAV platforms using lightweight multiespectral and thermal imaging sensors. In C. Heipke, K. Jacobsen, S. Müller, & U. Sörgel (Eds.), ISPRS High resolution earth imaging for geosptatial information. Hannover: Leibniz Universität.Google Scholar
  8. Calderón, R., Navas-Cortés, J., Lucena, C., & Zarco-Tejada, P. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231–245.CrossRefGoogle Scholar
  9. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.CrossRefGoogle Scholar
  10. Crawford, K., Roach, J., Dhillon, R., Rojo, F., Upadhyaya, S. (2014). An inexpensive aerial platform for precise remote sensing of almond and walnut canopy temperature.In 12th international conference on precision agriculture ISPA: Monticello, IL, USA
  11. Ehmke, T. (2013). Unmanned aerial systems for field scouting and spraying. Crops and Soils, 46(6), 4–9.Google Scholar
  12. González-Dugo, V., Zarco-Tejada, P. J., Nicolás, E., Nortes, P. A., Alarcón, J. J., Intrigliolo, D. S., et al. (2013). Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture, 14, 660–678.CrossRefGoogle Scholar
  13. Hoffmann, C. M. (2014). Adaptive responses of Beta Vulgaris L. and Cichorium Intybus L. root and leaf forms to drought stress. Journal of Agronomy and Crop Science, 200, 108–118.CrossRefGoogle Scholar
  14. Hunt, J. E. R., Hively, W. D., Fujikawa, S., Linden, D., Daughtry, C. S., & McCarty, G. (2010). Acquisition of NIRGreen-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2(1), 290–305.CrossRefGoogle Scholar
  15. Jackson, R. D., Idso, S. B., Reginato, R. J., & Pinter, P. J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 17(4), 1133–1138.CrossRefGoogle Scholar
  16. Jenkins, D., & Vasigh, B. (2013). The economic impact of unmanned aircraft systems integration in the United States. Arlington: Association for Unmanned Vehicle Systems International.Google Scholar
  17. Jimenez-Munoz, J. C., & Sobrino, J. A. (2006). Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data. International Journal of Remote Sensing, 27(5–6), 999–1014.CrossRefGoogle Scholar
  18. Lenthe, J. H., Oerke, E. C., & Dehne, H. W. (2007). Digital infrared thermography for monitoring canopy health of wheat. Precision Agriculture, 8(1–2), 15–26.CrossRefGoogle Scholar
  19. Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014a). Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15, 162–183.CrossRefGoogle Scholar
  20. Li, L., Zhang, Q., & Huang, D. (2014b). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078–20111.CrossRefPubMedPubMedCentralGoogle Scholar
  21. López, A., Molina-Aiz, F. D., Valera, D. L., & Peña, A. (2012). Determining the emissivity of the leaves of nine horticultural crops by means of infrared thermography. Scientia Horticulturae, 137, 49–58.CrossRefGoogle Scholar
  22. Lopez-Granados, F. (2011). Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Research, 51(1), 1–11.CrossRefGoogle Scholar
  23. Maes, W. H., & Steppe, K. (2012). Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review. Journal of Experimental Botany, 63(13), 4671–4712.CrossRefPubMedGoogle Scholar
  24. Mahlein, A. K., Steiner, U., Hillnhütter, C., Dehne, H. W., & Oerke, E. C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods, 8, 3. doi: 10.1186/1746-4811-8-3.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Meier, F., Scherer, D., Richters, J., & Christen, A. (2011). Atmospheric correction of thermal-infrared imagery of the 3-D urban environment acquired in oblique viewing geometry. Atmospheric Measurement Techniques, 4, 909–922.CrossRefGoogle Scholar
  26. Melexis Data Sheet. (2009). MLX90614 family, single and dual zone infrared thermometer inTO-39. Revised. 5, March 30. Melexis NV, Ieper.Google Scholar
  27. O’Shaughnessy, S. A., Evett, S. R., Colaizzi, P. D., & Howell, T. A. (2011). Using radiation thermometry to evaluate crop water stress in soybean and cotton. Agricultural Water Management, 98, 1523–1535.CrossRefGoogle Scholar
  28. Peña, J. M., Torres-Sánchez, J., de Castro, A. I., López-Granados, F., Dorado, J. (2014). The TOAS project: UAV technology for optimizing herbicide applications in weed-crop systems. In 12th international conference on precision agriculture ISPA: Monticello, IL, USA
  29. Pidgeon, J. D., Ober, E. S., Qi, A., Clark, C. J. A., Royal, A., & Jagard, K. W. (2006). Using multi-environment sugar beet variety trials to screen for drought tolerance. Field Crop Research, 95, 268–279.CrossRefGoogle Scholar
  30. Pinter, P. J., Stanghellini, M. E., Reginato, R. J., Idso, S. B., Jenkins, A. D., & Jackson, R. D. (1979). Remote detection of biological stresses in plants with infrared thermometry. Science, 205, 585–587.CrossRefPubMedGoogle Scholar
  31. Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yields trends are insufficient to double global crop production by 2050. PLoS ONE,. doi: 10.1371/journal.pone.0066428.Google Scholar
  32. Romano, A., Sogoná, A., Lupini, A., Araniti, F., Stevanato, P., Cacco, G., et al. (2013). Morpho-physiological response of sugar beet (Beta vulgaris L.) genotypes to drought stress. Acta Physiologiae Plantarum, 35(3), 853–869.CrossRefGoogle Scholar
  33. Shaw, B., Thomas, T. H., & Cooke, D. T. (2002). Responses of sugar beet (Beta Vulgaris L.) to drought and nutrient deficiency stress. Plant Growth Regulation, 37, 77–83.CrossRefGoogle Scholar
  34. Testi, L., Goldhamer, D. A., Iniesta, F., & Salinas, M. (2008). Crop water stress index is a sensitive water stress indicator in pistachio trees. Irrigation Science, 26, 395–405.CrossRefGoogle Scholar
  35. Tilman, D., Balzer, C., Hill, J., & Beford, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, USA, 108, 20260–20264.CrossRefGoogle Scholar
  36. Tipler, P. A. (2000). Physik (3rd ed.). Heidelberg: Spektrum Akademischer Verlag.Google Scholar
  37. Tsialtas, J. T., & Maslaris, N. (2012). Leaf physiological traits and their relation with sugar beet cultivar success in two contrasting environments. International Journal of Plant Production, 6(1), 15–36.Google Scholar
  38. Walker, J. (2008). Fundamentals of physics (8th ed.). New York: Wiley. ISBN 9780471758013.Google Scholar
  39. White, J. W., & Conley, M. M. (2013). A flexible, low-cost cart for proximal sensing. Crop Science, 53, 1646–1649.CrossRefGoogle Scholar
  40. Xu, Y., Ehsani, R., Kaplan, J., Ahmed, I., Kuzma, W., Orlandi, J., et al. (2014). An octo-rotor ground network for autonomous strawberry disease detection Year 1 Status Update. In: Gonzalez-de-Santos, P., Ribeiro, A. (Eds.). The 2nd international conference on robotics and associated high-technologies and equipment for agriculture and forestry, Madrid,. pp. 457-466.Google Scholar
  41. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A Review. Precision Agriculture, 13(6), 693–712.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • J. Martínez
    • 1
  • G. Egea
    • 1
  • J. Agüera
    • 2
  • M. Pérez-Ruiz
    • 1
  1. 1.Área de Ingeniería Agroforestal, Dpto. de Ingeniería Aeroespacial y Mecánica de FluidosUniversidad de SevillaSevilleSpain
  2. 2.Dpto. de Ingeniería RuralUniversidad de CórdobaCórdobaSpain

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