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Unmanned aerial vehicle to evaluate frost damage in coffee plants

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Abstract

Damage caused by frost on coffee plants can impact significantly in the reduction of crop quality and productivity. Remote sensing can be used to evaluate the damage caused by frost, providing precise and timely agricultural information to producers, assisting in decision making, and consequently minimizing production losses. In this context, this study aimed to evaluate the potential use of multispectral images obtained by unmanned aerial vehicle (UAV) to analyze and identify damage caused by frost in coffee plants in different climatic favorability zones. Visual evaluations of frost damage and chlorophyll content quantification were carried out in a commercial coffee plantation in Southern Minas Gerais, Brazil. The images were obtained from a multispectral camera coupled to a UAV with rotating wings. The results obtained demonstrated that the vegetation indices had a strong relationship and high accuracy with the frost damage. Among the indices studied the normalized difference vegetation index (NDVI) was the one that had better performances (r = − 0.89, R2 = 0.79, MAE = 10.87 e RMSE = 14.35). In a simple way, this study demonstrated that multispectral images, obtained from UAV, can provide a fast, continuous, and accessible method to identify and evaluate frost damage in coffee plants. This information is essential for the coffee producer for decision-making and adequate crop management.

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References

  • Barbosa, B. D. S., Ferraz, G. A. S., Santos, L. M., Marin, D. B., Maciel, D. T., Ferraz, P. F. P., & Rossi, G. (2019). RGB vegetation indices applied to grass monitoring: A qualitative analysis. Agronomy Research, 17(2), 349–357

    Google Scholar 

  • Bigg, G. R., Wise, S. M., Hanna, E., Mansell, D., Bryant, R. G., & Howard, A. (2014). Synoptic climatology of cold air drainage in the Derwent Valley, Peak District, UK. Meteorological Applications, 21(2), 161–170. https://doi.org/10.1002/met.1317

    Article  Google Scholar 

  • Camargo, M. B. P. D. (2010). The impact of climatic variability and climate change on Arabic coffee crop in Brazil. Bragantia, 69(1), 239–247. https://doi.org/10.1590/S0006-87052010000100030

    Article  Google Scholar 

  • Caramori, P. H., Caviglione, J. H., Wrege, M. S., Gonçalves, S. L., Faria, R. T., Filho, A. A., Sera, T., Chaves, J. C. D., & Koguishi, M. S. (2001). Climatic risk zoning for coffee (Coffea arabica L.) in Paraná state, Brazil. Revista Brasileira de Agrometeorologia, 9(3), 486–494.

  • Carvalho, L. C., Silva, F. M. D., Ferraz, G. A., Stracieri, J., Ferraz, P. F., & Ambrosano, L. (2017). Geostatistical analysis of Arabic coffee yield in two crop seasons. Revista Brasileira De Engenharia Agrícola e Ambiental, 21(6), 410–414. https://doi.org/10.1590/1807-1929/agriambi.v21n6p410-414

    Article  Google Scholar 

  • Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229–242. https://doi.org/10.1080/07038992.1996.10855178

    Article  Google Scholar 

  • Chung, U., Seo, H. H., Hwang, K. H., Hwang, B. S., Choi, J., Lee, J. T., & Yun, J. I. (2006). Minimum temperature mapping over complex terrain by estimating cold air accumulation potential. Agricultural and Forest Meteorology, 137(1–2), 15–24. https://doi.org/10.1016/j.agrformet.2005.12.011

    Article  Google Scholar 

  • DaMatta, F. M., & Ramalho, J. D. C. (2006). Impacts of drought and temperature stress on coffee physiology and production: A review. Brazilian Journal of Plant Physiology, 18(1), 55–81. https://doi.org/10.1590/S1677-04202006000100006

    Article  CAS  Google Scholar 

  • Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25(23), 5403–5413. https://doi.org/10.1080/0143116042000274015

    Article  Google Scholar 

  • Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse, D. (2009). Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10(6), 459–470. https://doi.org/10.1007/s11119-008-9100-2

    Article  Google Scholar 

  • Duffy, J. P., Pratt, L., Anderson, K., Land, P. E., & Shutler, J. D. (2018). Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone. Estuarine, Coastal and Shelf Science, 200, 169–180. https://doi.org/10.1016/j.ecss.2017.11.001

    Article  Google Scholar 

  • Feng, G., Anderson, M. C., Zhang, X., Yang, Z., Alfieri, J. G., Kustas, W. P., Mueller, R., Johnson, D. M., & Prueger, J. H. (2017). Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, 188, 9–25. https://doi.org/10.1016/j.rse.2016.11.004

    Article  Google Scholar 

  • Feng, M., Guo, X., Wang, C., Yang, W., Shi, C., Ding, G., Zhang, X., Xiao, L., Zhang, M., & Song, X. (2018). Monitoring and evaluation in freeze stress of winter wheat (Triticum aestivum L.) through canopy hyperspectrum reflectance and multiple statistical analysis. Ecological Indicators, 84, 290–297. https://doi.org/10.1016/j.ecolind.2017.08.059

    Article  CAS  Google Scholar 

  • Feng, M. C., Yang, W. D., Cao, L. L., & Ding, G. W. (2009). Monitoring winter wheat freeze injury using multi-temporal MODIS data. Agricultural Sciences in China, 8(9), 1053–1062. https://doi.org/10.1016/S1671-2927(08)60313-2

    Article  Google Scholar 

  • Freitas, P., Vieira, G., Canário, J., Folhas, D., & Vincent, W. F. (2019). Identification of a threshold minimum area for reflectance retrieval from thermokarst lakes and ponds using full-pixel data from Sentinel-2. Remote Sensing, 11(6), 657. https://doi.org/10.3390/rs11060657

    Article  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7

    Article  Google Scholar 

  • Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology b: Biology, 22(3), 247–252. https://doi.org/10.1016/1011-1344(93)06963-4

    Article  CAS  Google Scholar 

  • Gobbett, D. L., Nidumolu, U., & Crimp, S. (2018). Modelling frost generates insights for managing risk of minimum temperature extremes. Weather and Climate Extremes, 27, 100176. https://doi.org/10.1016/j.wace.2018.06.003

  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337–352. https://doi.org/10.1016/j.rse.2003.12.013

    Article  Google Scholar 

  • Hellweger, F. L., Miller, W., & Oshodi, K. S. (2007). Mapping turbidity in the Charles River, Boston using a high-resolution satellite. Environmental Monitoring and Assessment, 132(1–3), 311–320. https://doi.org/10.1007/s10661-006-9535-8

    Article  PubMed  Google Scholar 

  • Hodges, D. M., & Forney, C. F. (2000). The effects of ethylene, depressed oxygen and elevated carbon dioxide on antioxidant profiles of senescing spinach leaves. Journal of Experimental Botany, 51(344), 645–655. https://doi.org/10.1093/jexbot/51.344.645

    Article  CAS  PubMed  Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Jamieson, P. D., Porter, J. R., & Wilson, D. R. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27(4), 337–350. https://doi.org/10.1016/0378-4290(91)90040-3

    Article  Google Scholar 

  • Ke, Y., Im, J., Park, S., & Gong, H. (2016). Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing, 8(3), 215. https://doi.org/10.3390/rs8030215

    Article  Google Scholar 

  • Kotikot, S. M., Flores, A., Griffin, R. E., Nyaga, J., Case, J. L., Mugo, R., Sedah, A., Adams, E., Limaye, A., & Irwin, D. E. (2020). Statistical characterization of frost zones: Case of tea freeze damage in the Kenyan highlands. International Journal of Applied Earth Observation and Geoinformation, 84, 101971. https://doi.org/10.1016/j.jag.2019.101971

  • Kotikot, S. M., & Onywere, S. M. (2014). Application of GIS and remote sensing techniques in frost risk mapping for mitigating agricultural losses in the Aberdare ecosystem, Kenya. Geocarto International, 30, 104–121. https://doi.org/10.1080/10106049.2014.965758

    Article  Google Scholar 

  • Larcher, W. (1981). Effects of low temperature stress and frost injury on plant productivity. In C. B. Johnson (Ed.), Physiological processes limiting plant productivity. (pp. 253–269). London, UK: Butterworths.

    Chapter  Google Scholar 

  • Li, X. Y., Liu, G. S., Yang, Y. F., Zhao, C. H., Yu, Q. W., & Song, S. X. (2007). Relationship between hyperspectral parameters and physiological and biochemical indexes of flue-cured tobacco leaves. Agricultural Sciences in China, 6(6), 665–672. https://doi.org/10.1016/S1671-2927(07)60098-4

    Article  CAS  Google Scholar 

  • Lou, W., Ji, Z., Sun, K., & Zhou, J. (2013). Application of remote sensing and GIS for assessing economic loss caused by frost damage to tea plantations. Precision Agriculture, 14(6), 606–620. https://doi.org/10.1007/s11119-013-9318-5

    Article  Google Scholar 

  • Lu, B., He, Y., & Liu, H. (2016). Investigating species composition in a temperate grassland using Unmanned Aerial Vehicle-acquired imagery. In 2016 4th international workshop on earth observation and remote sensing applications (EORSA). IEEE, (pp. 107–111). https://doi.org/10.1109/EORSA.2016.7552776.

  • Mao, W., Wang, Y., & Wang, Y. (2003). Real-time detection of between-row weeds using machine vision. Paper No. 031004, St Joseph, MI, USA: ASAE. https://doi.org/10.13031/2013.15381.

  • Marin, D. B., Alves, M. C., Pozza, E. A., Gandia, R. M., Cortez, M. L. J., & Mattioli, M. C. (2019). Multispectral remote sensing in the identification and mapping of biotic and abiotic coffee tree variables. Revista Ceres, 66(2), 142–153. https://doi.org/10.1590/0034-737x201966020009

    Article  Google Scholar 

  • Martins, M. Q., Partelli, F. L., Golynski, A., Sousa Pimentel, N., Ferreira, A., de Oliveira Bernardes, C., Ribeiro-Barros, A. I., & Ramalho, J. C. (2019). Adaptability and stability of Coffea canephora genotypes cultivated at high altitude and subjected to low temperature during the winter. Scientia Horticulturae, 252, 238–242. https://doi.org/10.1016/j.scienta.2019.03.044

    Article  Google Scholar 

  • MicaSense Sequoia. (2018). Sequoia User Guide. Drones Parrot SAS, (pp. 4–13). Paris, France. Retrieved 30 Mar, 2021 from www.micasense.com/sequoia.

  • Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M., & Pyšek, P. (2017). Timing is important: unmanned aircraft vs. satellite imagery in plant invasion monitoring. Frontiers in Plant Science, 8, 887. https://doi.org/10.3389/fpls.2017.00887

    Article  PubMed  PubMed Central  Google Scholar 

  • Nóia Júnior, R. N., Schwerz, F., Safanelli, J. L., Rodrigues, J. C., & Sentelhas, P. C. (2019). Eucalyptus rust climatic risk as affected by topography and ENSO phenomenon. Australasian Plant Pathology, 48(2), 131–141. https://doi.org/10.1007/s13313-018-0608-2

    Article  CAS  Google Scholar 

  • Nuttall, J. G., Perry, E. M., Delahunty, A. J., O’Leary, G. J., Barlow, K. M., & Wallace, A. J. (2019). Frost response in wheat and early detection using proximal sensors. Journal of Agronomy and Crop Science, 205(2), 220–234. https://doi.org/10.1111/jac.12319

    Article  Google Scholar 

  • Padilla, F. M., Souza, R., Peña, T., Gallardo, M., Gimenez, C., & Thompson, R. (2018). Different responses of various chlorophyll meters to increasing nitrogen supply in sweet pepper. Frontiers in Plant Science, 9, 1752. https://doi.org/10.3389/fpls.2018.01752

    Article  PubMed  PubMed Central  Google Scholar 

  • Oborne, M. (2018). Mission Planner. Retrieved 30 Mar, 2021 from https://ardupilot.org/planner/index.html

  • QGIS Development Team. (2017). QGIS geographic information system. Open Source Geospatial Foundation Project. Retrieved 30 Mar, 2021 from http://www.qgis.org

  • R Development Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

  • Rafaelli, D. R., Moreira, M. A., & Farias, R. (2006). Analysis of the MODIS data potential to monitor (state and local level) frost impact on coffee. Agricultura Em São Paulo, 53(1), 5–15

    Google Scholar 

  • Ramalho, J. C., DaMatta, F. M., Rodrigues, A. P., Scotti-Campos, P., Pais, I., Batista-Santos, P., Partelli, F. L., Ribeiro, A., Lidon, F. C., & Leitão, A. E. (2014). Cold impact and acclimation response of Coffea spp. plants. Theoretical and Experimental Plant Physiology, 26(1), 5–18. https://doi.org/10.1007/s40626-014-0001-7

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Deering, D. W., Schell, J. A., & Harlan, J. C. (1974). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. Greenbelt: NASA/GSFC, Type III, Final Report, 371p.

  • Ruzgienė, B., Berteška, T., Gečyte, S., Jakubauskienė, E., & Aksamitauskas, V. Č. (2015). The surface modelling based on UAV Photogrammetry and qualitative estimation. Measurement, 73, 619–627. https://doi.org/10.1016/j.measurement.2015.04.018

    Article  Google Scholar 

  • Santos, L. M. D., Andrade, M. T., Santana, L. S., Rossi, G., Maciel, D. T., Barbosa, B. D. S., Maciel, D. T., & Rossi, G. (2019). Analysis of flight parameters and georeferencing of images with different control points obtained by RPA. Agronomy Research, 17(5), 2054–2063

    Google Scholar 

  • She, B., Huang, J. F., Zhang, D. Y., & Huang, L. S. (2017). Assessing and characterizing oilseed rape freezing injury based on MODIS and MERIS data. International Journal of Agricultural and Biological Engineering, 10(3), 143–157

    Google Scholar 

  • Svensgaard, J., Jensen, S. M., Westergaard, J. C., Nielsen, J., Christensen, S., & Rasmussen, J. (2019). Can reproducible comparisons of cereal genotypes be generated in field experiments based on UAV imagery using RGB cameras? European Journal of Agronomy, 106, 49–57. https://doi.org/10.1016/j.eja.2019.03.006

    Article  Google Scholar 

  • Tan, Z., Ding, M., Wang, L., Yang, X., & Ou, Z. (2008). Monitoring freeze injury and evaluating losing to sugarcane using RS and GPS. In International Conference on Computer and Computing Technologies in Agriculture (pp. 307–316). Boston, USA: Springer.

  • Wang, H., Huo, Z., Zhou, G., Wu, L., & Feng, H. (2015). Monitoring and forecasting winter wheat freeze injury and yield from multi-temporal remotely sensed data. Intelligent Automation & Soft Computing, 22(2), 255–260. https://doi.org/10.1080/10798587.2015.1095475

    Article  Google Scholar 

  • Wei, C., Huang, J., Wang, X., Blackburn, G. A., Zhang, Y., Wang, S., & Mansaray, L. R. (2017). Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves. Remote Sensing of Environment, 195, 56–66. https://doi.org/10.1016/j.rse.2017.03.042

    Article  Google Scholar 

  • Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(2), 184–194. https://doi.org/10.1080/02723646.1981.10642213

    Article  Google Scholar 

  • Yang, Z., Willis, P., & Mueller, R. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. In Proceedings of the 17th william pecora memorial remote sensing symposium, (pp. 1–11). Bethesday, MD, USA: American Society for Photogrammetry & Remote Sensing.

  • Zhang, J., Hu, J., Lian, J., Fan, Z., Ouyang, X., & Ye, W. (2016). Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring. Biological Conservation, 198, 60–69. https://doi.org/10.1016/j.biocon.2016.03.027

    Article  Google Scholar 

  • Zhou, J., Pavek, M. J., Shelton, S. C., Holden, Z. J., & Sankaran, S. (2016). Aerial multispectral imaging for crop hail damage assessment in potato. Computers and Electronics in Agriculture, 127, 406–412. https://doi.org/10.1016/j.compag.2016.06.019

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Embrapa Café—Consórcio Pesquisa Café, project approved in the call n° 20/2018, 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 farm Bom Jardim.

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  1. Department of Agricultural Engineering, Federal University of Lavras, P.O BOX 3037, Lavras, MG, CEP 37200-000, Brazil

    Diego Bedin Marin, Gabriel Araújo e Silva Ferraz, Felipe Schwerz, Rafael Alexandre Pena Barata, Rafael de Oliveira Faria & Jessica Ellen Lima Dias

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  1. Diego Bedin Marin
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  2. Gabriel Araújo e Silva Ferraz
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Marin, D.B., Ferraz, G.A.e.S., Schwerz, F. et al. Unmanned aerial vehicle to evaluate frost damage in coffee plants. Precision Agric 22, 1845–1860 (2021). https://doi.org/10.1007/s11119-021-09815-w

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