Precision Agriculture

, Volume 20, Issue 4, pp 788–804 | Cite as

Spatial variability in commercial orange groves. Part 1: canopy volume and height

  • André F. ColaçoEmail author
  • José P. Molin
  • Joan R. Rosell-Polo
  • Alexandre Escolà


Characterizing crop spatial variability is crucial for estimating the opportunities for site-specific management practices. In the context of tree crops, ranging sensor technology has been developed to assess tree canopy geometry and control real-time variable rate application of plant protection products and fertilizers. The objective of this study was to characterize the variability of canopy geometry attributes in commercial orange groves in Brazil and therefore estimate the potential impact of sensor-based site-specific management. Using a mobile terrestrial laser scanner, canopy volume and canopy height were measured in 0.25 m length transversal sections along the rows across five large scale commercial orange groves in São Paulo, Brazil. The coefficient of variation of canopy volume ranged from 30 to 40%. Canopy height was less variable, but closely related to canopy volume. Histograms of canopy volume and height were usually negatively skewed indicating regions of the groves with smaller plants and punctual plant resets. In scenarios where input application rates followed canopy volume variability, input savings were around 40% compared to constant rates based on the maximum canopy volume. Maps of canopy geometry derived from mobile terrestrial laser scanning revealed significant canopy spatial variability, suggesting that the groves would benefit from strategies based on management zones and other forms of site-specific management.


Precision horticulture Mobile terrestrial laser scanner LiDAR Variable rate technology Orange groves 



We thank Citrosuco and Jacto companies for supporting this Project, the São Paulo Research Foundation (FAPESP) for providing a scholarship to the first author (Grant: 2013/18853-0) and the Coordination for the Improvement of Higher Education Personnel (CAPES), for funding the first author as an exchange visitor at the University of Lleida (Grant: bex_3751/15-5).


  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.CrossRefGoogle Scholar
  2. Byers, R. E. (1987). Tree-row-volume spraying rate calculator for apples. HortScience, 22, 506–507.Google Scholar
  3. Byers, R. E., Lyons, C. G., Yoder, K. S., Horsburgh, R. L., Barden, J. A., & Donohue, S. J. (1984). Effect of apple tree size and canopy density on spray chemical deposit. HortScience, 19, 93–94.Google Scholar
  4. CloudCompare [GPL software] v2.6.1. (2018). Retrieved June 28, 2018 from,
  5. Colaço, A. F., & Molin, J. P. (2017). Variable rate fertilization in citrus: A long term study. Precision Agriculture, 18, 169–191. Scholar
  6. Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). Application of light detection and ranging and ultrasonic sensors to high throughput phenotyping and precision horticulture: Current status and challenges. Horticulture Research, 5(1), 35–46. Scholar
  7. Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (in press). Spatial variability in commercial orange groves. Part 2: relating canopy geometry to soil attributes and historical yield. Precision Agriculture.Google Scholar
  8. Colaço, A. F., Rosa, H. J. A., & Molin, J. P. (2014). A model to analyze as-applied reports from variable rate applications. Precision Agriculture, 15, 304–320. Scholar
  9. Colaço, A. F., Trevisan, R. G., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2017). A method to obtain orange crop geometry information using a mobile terrestrial laser scanner and 3D modeling. Remote Sensing, 9, 763. Scholar
  10. Escolà, A., Martínez-Casasnovas, J. A., Rufat, J., Arnó, J., Arbonés, A., Sebé, F., et al. (2017). Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture, 18, 111–132. Scholar
  11. FAO. (2018). Food and Agriculture Organization, Faostat. Retrieved June 28, 2018, from
  12. Farias, P. R. S., Nociti, L. A. S., Barbosa, J. C., & Perecin, D. (2003). Agricultura de precisão: Mapeamento da produtividade em pomares cítricos usando geoestatística (Precision Agriculture: Mapping of yield in citrus groves using geostatistics). Revista Brasileira de Fruticultura, 25(2), 235–241.CrossRefGoogle Scholar
  13. Fisher, P. D., Abuzar, M., Rab, M. A., Best, F., & Chandra, S. (2009). Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers’ historical paddock yields and normalised difference vegetation index. Crop Pasture Science, 60, 844. Scholar
  14. Giles, D. K., Delwiche, M. J., & Dodd, R. B. (1987). Control of orchard spraying based on electronic sensing of target characteristics. Transactions of the ASAE, 30, 1624–1630. Scholar
  15. Giles, D. K., Delwiche, M. J., & Dodd, R. B. (1989). Sprayer control by sensing orchard crop characteristics: Orchard architecture and spray liquid savings. Journal of Agricultural Engineering Research, 43, 271–289. Scholar
  16. Leão, M. G. A., Marques, J., Jr., de Souza, Z. M., & Pereira, G. T. (2010). Variabilidade espacial da textura de um latossolo sob cultivo de citros (Spatial variability of texture of a Latosol under cultivation of citrus). Ciência e Agrotecnologia, 34(1), 121–131.CrossRefGoogle Scholar
  17. Mann, K. K., Schumann, A. W., & Obreza, T. A. (2011). Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data. Precision Agriculture, 12, 457–472. Scholar
  18. Méndez, V., Rosell-Polo, J. R., Pascual, M., & Escolà, A. (2016). Multi-tree woody structure reconstruction from mobile terrestrial laser scanner point clouds based on a dual neighbourhood connectivity graph algorithm. Biosystems Engineering, 148, 34–47. Scholar
  19. Minasny, B., McBratney, A. B.,Whelan, B. M. (2005). VESPER version 1.62. Australian Centre for Precision Agriculture, McMillan Building A05, the University of Sydney, NSW. Retrieved June 28, 2018 from
  20. Molin, J. P., Colaço, A. F., Carlos, E. F., & Mattos, D., Jr. (2012). Yield mapping, soil fertility and tree gaps in an orange orchard. Revista Brasileira de Fruticultura, 34, 1256–1265.CrossRefGoogle Scholar
  21. Molin, J. P., & Mascarin, L. S. (2007). Colheita de citros e obtenção de dados para mapeamento da produtividade (Characterization of harvest systems and development of yield mapping for citrus). Engenharia Agrícola, 27, 259–266.CrossRefGoogle Scholar
  22. Oliveira, P. C. G., Farias, P. R. S., Lima, H. V., Fernandes, A. R., Oliveira, F. A., & Pita, J. D. (2009). Variabilidade espacial de propriedades químicas do solo e da produtividade de citros na Amazônia Oriental (Spatial variability of soil chemical properties and yield of citrus orchards in eastern Amazonia). Engenharia Agrícola e Ambiental, 13(6), 708–715.CrossRefGoogle Scholar
  23. Pringle, M. J., McBratney, A. B., Whelan, B. M., & Taylor, J. A. (2003). A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data. Agricultural Systems, 76, 273–292. Scholar
  24. QGIS v2.10—QGIS Development Team. (2018). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Retrieved June 28, 2018
  25. Robertson, M. J., Lyle, G., & Bowden, J. W. (2008). Within-field variability of wheat yield and economic implications for spatially variable nutrient management. Field Crops Research, 105, 211–220. Scholar
  26. Rosell-Polo, J. R., Llorens, J., Sanz, R., Arnó, J., Ribes-Dasi, M., Masip, J., et al. (2009). Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning. Agriculture and Forest Meteorology, 149, 1505–1515. Scholar
  27. Rosell-Polo, J. R., & Sanz, R. (2012). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124–141. Scholar
  28. Schumann, A. W., Hostler, K. H., Buchanon, S., & Zaman, Q. U. (2006a). Relating citrus canopy size and yield to precision fertilization. Proceedings of Florida State Horticultural Society, 119, 148–154.Google Scholar
  29. Schumann, A. W., Miller, W. M., Zaman, Q. U., Hostler, K. H., Buchanon, S., & Cugati, S. A. (2006b). Variable rate granular fertilization of citrus groves: Spreader performance with single-tree prescription zones. Applied Engineering in Agriculture, 22, 19–24.CrossRefGoogle Scholar
  30. Schumann, A. W., & Zaman, Q. U. (2005). Software development for real-time ultrasonic mapping of tree canopy size. Computers and Electronics in Agriculture, 47, 25–40. Scholar
  31. Siqueira, D. S., Marques, J., Jr., & Pereira, G. T. (2010). The use of landforms to predict the variability of soil and orange attributes. Geoderma, 155, 55–66.CrossRefGoogle Scholar
  32. Solanelles, F., Escolà, A., Planas, S., Rosell-Polo, J. R., Camp, F., & Gràcia, F. (2006). An electronic control system for pesticide application proportional to the canopy width of tree crops. Biosystems Engineering, 95, 473–481. Scholar
  33. Sutton, T. B., & Unrath, C. R. (1984). Evaluation of the Tree-Row-Volume concept with density adjuvants in relation to spray deposits in apple orchards. Plant Disease, 68, 480–484.CrossRefGoogle Scholar
  34. Sutton, T. B., & Unrath, C. R. (1988). Evaluation of the Tre-Row-Volume model for full-season pesticide application on apples. Plant Disease, 72, 629–632.CrossRefGoogle Scholar
  35. Tagarakis, A. C., Koundouras, S., Fountas, S., & Gemtos, T. (2018). Evaluation of the use of LIDAR laser scanner to map pruning wood in vineyards and its potential for management zones delineation. Precision Agriculture, 19, 334–347. Scholar
  36. Tisseyre, B., & McBratney, A. B. (2008). A technical opportunity index based on mathematical morphology for site-specific management: An application to viticulture. Precision Agriculture, 9, 101–113. Scholar
  37. Uribeetxebarria, A., Daniele, E., Escolà, A., Arnó, J., & Martínez-Casasnovas, J. A. (2018). Spatial variability in orchards after land transformation: Consequences for precision agriculture practices. Science of the Total Environment, 635, 343–352. Scholar
  38. Whitney, J. D., Miller, W. M., Wheaton, T. A., Salyani, M., & Schueller, J. K. (1999). Precision farming applications in Florida citrus. Applied Engineering in Agriculture, 15, 399–403.CrossRefGoogle Scholar
  39. Zaman, Q. U., & Schumann, A. W. (2005). Performance of an ultrasonic tree volume measurement system in commercial citrus groves. Precision Agriculture, 6, 467–480.CrossRefGoogle Scholar
  40. Zaman, Q. U., & Schumann, A. W. (2006). Nutrient management zones for citrus based on variation in soil properties and tree performance. Precision Agriculture, 7, 45–63. Scholar
  41. Zaman, Q. U., Schumann, A. W., & Miller, W. M. (2005). Variable rate nitrogen application in Florida citrus based on ultrasonically-sensed tree size. Applied Engineering in Agriculture, 21, 331–336.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • André F. Colaço
    • 1
    • 3
    Email author
  • José P. Molin
    • 1
  • Joan R. Rosell-Polo
    • 2
  • Alexandre Escolà
    • 2
  1. 1.Biosystems Engineering Department, ‘Luiz de Queiroz’ College of AgricultureUniversity of São PauloPiracicabaBrazil
  2. 2.Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, School of Agrifood and Forestry Science and EngineeringUniversity of Lleida – Agrotecnio CenterLleidaSpain
  3. 3.CSIROGlen OsmondAustralia

Personalised recommendations