Advertisement

Monitoring of Coffee Tree Growth Through Crop Surface Models and MGRVI with Images Obtained with RPA

  • Gabriel Araújo e Silva FerrazEmail author
  • Luana Mendes dos Santos
  • Marco Thulio Andrade
  • Letícia Aparecida Gonçalves Xavier
  • Diogo Tubertini Maciel
  • Patrícia Ferreira Ponciano Ferraz
  • Giuseppe Rossi
  • Matteo Barbari
Conference paper
  • 33 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 67)

Abstract

For precision agriculture to monitor the crops during the vegetative and reproductive period is very important. Currently, remote sensing platforms such as remotely piloted aircraft (RPA) have stood out. The aim of this work was to evaluate the application of Modified Green Red Vegetation Index (MGRVI) vegetation index and Crop Surface Models (CSM) with images obtained by an RPA, to monitor the growth of coffee trees in three different seasons. The experiment was carried out at the Federal University of Lavras, Lavras, Minas Gerais, Brazil, in an area cultivated with coffee species Coffea arabica L. A RPA equipped with a digital camera was used to take photos, and Agisoft PhotoScan software was used to build the mosaic of photos and CSM. QGIS was used to obtain the height of the plants, application of the index MGRVI and the preparation of the map layouts by images processing. It was possible to identify the crop failure areas with the CSM. Crop Surface Models (CSM) showed to be a promising technique for the monitoring of coffee tree growth, making it possible to identify crop failures and growth variations. The MGRVI index failed to identify crop failures. The index did not recognize the difference between soil and vegetation, possibly due to the light variations in the area.

Keywords

Precision agriculture Plant height Remote sensing UAS Vegetation index Crop monitoring 

Notes

Acknowledgements

The authors thank, the Foundation for Research of the State of Minas Gerais (FAPEMIG) to funding this research, the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), the Federal University of Lavras (UFLA) and University of Florence (UniFI).

References

  1. Ballesteros, R., Hernández, D., Ortega, J. F., & Moreno, M. A. (2014). Applications of georeferenced highresolution images obtained with unmanned aerial vehicles. Part II: Application to maize and onion crops of a semi-arid region in Spain. Precision Agriculture, 15, 593–614.CrossRefGoogle Scholar
  2. Barbosa, B. D. S., Ferraz, G. A. S., Gonçalves, L. M., Marin, D. B., Maciel, D. T., Ferraz, P. F. P., et al. (2019). RGB vegetation indices applied to grass monitoring: A qualitative analysis. Agronomy Research, 17(2), 349–357.Google Scholar
  3. Bareth, G., Bendig, J., Tilly, N., Hoffmeister, D., Aasen, H., and Bolten, A. (2016). A comparison of UAV-and TLS-derived plant height for crop monitoring: Using polygon grids for the analysis of crop surface models (CSMs). Photogrammetrie-Fernerkundung-Geoinformation, 2, 85–94.Google Scholar
  4. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6, 10395–10412.CrossRefGoogle Scholar
  5. Bendig, J., Willkomm, M., Tilly, N., Gnyp, M. L., Bennertz, S., Qiang, C., et al. (2013). Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring in Northeast China. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 45–50.CrossRefGoogle Scholar
  6. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., et al. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87.CrossRefGoogle Scholar
  7. Dandois, J., Olano, M., & Ellis, E. (2015). Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing, 7, 13895–13920.CrossRefGoogle Scholar
  8. Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114, 358–371.CrossRefGoogle Scholar
  9. Panagiotidis, D., Abdollahnejad, A., Surový, P., & Chiteculo, V. (2017). Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing, 38, 2392–2410.CrossRefGoogle Scholar
  10. Ramirez, G. M., & Zullo Júnior, J. (2010). Estimation of biophysical parameters of coffee fields based on high-resolution satellite images. Engenharia Agricola, 30, 468–479.CrossRefGoogle Scholar
  11. Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R. N., & Christensen, S. (2016). Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy, 74, 75–92.CrossRefGoogle Scholar
  12. Romeo, J., Pajares, G., Montalvo, M., Guerrero, J. M., Guijarro, M., & de la Cruz, J. M. (2013). A new Expert System for greenness identification in agricultural images. Expert Systems with Applications, 40, 2275–2286.CrossRefGoogle Scholar
  13. Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., et al. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112–123.CrossRefGoogle Scholar
  14. Xiao, J., & Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98, 237–250.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gabriel Araújo e Silva Ferraz
    • 1
    Email author
  • Luana Mendes dos Santos
    • 1
  • Marco Thulio Andrade
    • 1
  • Letícia Aparecida Gonçalves Xavier
    • 1
  • Diogo Tubertini Maciel
    • 1
  • Patrícia Ferreira Ponciano Ferraz
    • 1
  • Giuseppe Rossi
    • 2
  • Matteo Barbari
    • 2
  1. 1.Federal University of Lavras, Campus UniversitárioLavrasBrazil
  2. 2.Department of Agriculture, Food, Environment and Forestry (DAGRI)University of FlorenceFlorenceItaly

Personalised recommendations