Abstract
Bacterial blight of coffee caused by Pseudomonas syringae pv. garcae shows great destructive potential in the main coffee producing regions in Brazil and worldwide. Remote sensing technologies can be used as an inexpensive and effective method to identify and monitor the disease. This study evaluated the potential of the Landsat 8 OLI/TIRS multispectral sensor for the spatial and temporal monitoring of coffee (Coffea arabica) affected by the bacterial blight. In a commercial coffee field in Minas Gerais State, Brazil, samples were collected from a grid of 85 points spaced from 35 to 50 m apart. Each sampling point consisted of five plants, being four plants distributed surrounding a central plant. The analyzes of the plant foliage, disease incidence, and disease severity were performed from January to December 2014 and correlated with 15 vegetation indices derived from a time series of 11 multispectral images. The brightness temperature of these images was calculated in order to indicate the area of the field more favorable to the occurrence of the bacterial blight of coffee. Vegetation indices were highly correlated with the incidence (r = 0.76) and severity (r = 0.52) of the disease. The brightness temperature aided in the mapping of areas with optimal temperature conditions for the occurrence of the disease. In general, the study demonstrated the potential of Landsat 8 OLI/TIRS images to identify and monitor crops affected by the bacterial blight of coffee.
Similar content being viewed by others
References
Ahamed, T., Tian, L., Zhang, Y., & Ting, K. C. (2011). A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35(7), 2455–2469. https://doi.org/10.1016/j.biombioe.2011.02.028.
Alisaac, E., Behmann, J., Kuska, M. T., Dehne, H. W., & Mahlein, A. K. (2018). Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species. European Journal of Plant Pathology. https://doi.org/10.1007/s10658-018-1505-9.
Amaral, J. D., Teixeira, C., & Pinheiro, E. D. (1956). A bactéria causadora da mancha aureolada do cafeeiro. Arquivo do Institudo Biológico de São Paulo (Brasil), 23, 151.
Ashourloo, D., Mobasheri, M. R., & Huete, A. (2014). Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sensing, 6(6), 5107–5123. https://doi.org/10.3390/rs6065107.
Barbedo, J. G. A. (2013). Digital image processing techniques for detecting. Quantifying and classifying plant diseases. SpringerPlus, 2(1), 660. https://doi.org/10.1186/2193-1801-2-660.
Belan, L. L., Pozza, E. A., Freitas, M. L. D. O., Souza, R. M., Jesus Junior, W. C., & Oliveira, J. M. (2014). Diagrammatic scale for assessment of bacterial blight in coffee leaves. Journal of Phytopathology, 162(11–12), 801–810. https://doi.org/10.1111/jph.12272.
Birth, G. S., & McVey, G. R. (1968). Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640–643. https://doi.org/10.2134/agronj1968.00021962006000060016x.
Bispo, R. C. (2013). Using MODIS data to monitoring and mapping of coffee crops. Campinas, Brasil: University of Campinas.
Boechat, L. T., Pinto, F. A. C., Júnior, T. J. P., Queiroz, D. M., & Teixeira, H. (2014). Detection of white mold in dry beans using spectral characteristics. Revista Ceres, 61(6), 907–915.
Boldini, J. M. (2001). Epidemiologia da ferrugem e da cercosporiose em cafeeiro irrigado e fertirrigado. Lavras, Brasil: University of Lavras.
Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156–172. https://doi.org/10.1016/S0034-4257(00)00197-8.
Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll a. chlorophyll b. and carotenoids in soybean leaves. Remote Sensing of Environment, 39(3), 239–247. https://doi.org/10.1016/0034-4257(92)90089-3.
Chemura, A., Mutanga, O., & Dube, T. (2016). Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precision Agriculture, 18(5), 859–881. https://doi.org/10.1007/s11119-016-9495-0.
Chemura, A., Mutanga, O., & Odindi, J. (2017a). Empirical modeling of leaf chlorophyll content in coffee (Coffea Arabica) plantations with Sentinel-2 MSI data: Effects of spectral settings, spatial resolution, and crop canopy cover. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12), 5541–5550.
Chemura, A., Mutanga, O., Sibanda, M., & Chidoko, P. (2017b). Machine learning prediction of coffee rust severity on leaves using spectroradiometer data. Tropical Plant Pathology. https://doi.org/10.1007/s40858-017-0187-8.
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.
Chen, Z. (2002). Morphocultural and pathogenic comparisons between Colletotrichum kahawae and Colletotrichum gloeosporioides isolated from coffee berries. Lisboa, Portugal: Universidade Técnica de Lisboa. Instituto Superior de Agronomia.
De Biasi, M. (1970). Carta de declividade de vertentes: confecção e utilização. Geomorfologia, 21, 8–13.
Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3.
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.
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.
Honorato Júnior, J., Zambolim, L., Aucique-Pérez, C. E., Resende, R. S., & Rodrigues, F. A. (2015). Photosynthetic and antioxidative alterations in coffee leaves caused by epoxiconazole and pyraclostrobin sprays and Hemileia vastatrix infection. Pesticide Biochemistry and Physiology, 123, 31–39. https://doi.org/10.1016/j.pestbp.2015.01.016.
Huang, W., Guan, Q., Luo, J., Zhang, J., Zhao, J., Liang, D., et al. (2014). New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2516–2524. https://doi.org/10.1109/JSTARS.2013.2294961.
Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J. (2007). Identification of yellow rust in wheat using in situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8(4–5), 187–197. https://doi.org/10.1007/s11119-007-9038-9.
Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q., & Liu, X. (2012). Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis). Computers and Electronics in Agriculture, 82, 100–107. https://doi.org/10.1016/j.compag.2012.01.002.
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.
Irons, J. R., Dwyer, J. L., & Barsi, J. A. (2012). The next Landsat satellite: The Landsat data continuity mission. Remote Sensing of Environment, 122, 11–21. https://doi.org/10.1016/j.rse.2011.08.026.
Ithiru, J. M., Gichuru, E. K., Gitonga, P. N., Cheserek, J. J., & Gichimu, B. M. (2013). Methods for early evaluation for resistance to bacterial blight of coffee. African Journal of Agricultural Research, 8(21), 2450–2454. https://doi.org/10.5897/AJAR2013.6717.
Ito, D. S., Sera, T., Sera, G. H., Del Grossi, L., & Kanayama, F. S. (2008). Resistance to bacterial blight in arabica coffee cultivars. Crop Breeding and Applied Biotechnology, 8(2), 99–103. https://doi.org/10.12702/1984-7033.v08n02a01.
Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X., & Li, B. (2014). Land cover classification using Landsat 8 operational land imager data in Beijing, China. Geocarto International, 29(8), 941–951. https://doi.org/10.1080/10106049.2014.894586.
Jones, H. G., & Schofield, P. (2008). Thermal and other remote sensing of plant stress. General and Applied Plant Physiology, 34(1–2), 19–32.
Korobko, A., & Wondimagegne, E. (1997). Bacterial blight of coffee (Pseudomonas syringae pv. garcae) in Ethiopia. In K. Rudolph, T. J. Burr, J. W. Mansfield, D. Stead, A. Vivian, & J. von Kietzell (Eds.), Pseudomonas syringae pathovars and related pathogens (pp. 538–541). Dordrecht: Springer.
Lamichhane, J. R., Fabi, A., Ridolfi, R., & Varvaro, L. (2013). Epidemiological study of hazelnut bacterial blight in central Italy by using laboratory analysis and geostatistics. PLoS ONE, 8(2), e56298. https://doi.org/10.1371/journal.pone.0056298.
Liu, Z. Y., Huang, J. F., & Tao, R. X. (2008). Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Science, 15(3), 232–242. https://doi.org/10.1016/S1672-6308(08)60047-5.
Mahlein, A. K., Oerke, E. C., Steiner, U., & Dehne, H. W. (2012). Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133(1), 197–209. https://doi.org/10.1007/s10658-011-9878-z.
Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., et al. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019.
Marcon, M., Mariano, K., Braga, R. A., Paglis, C. M., Scalco, M. S., & Horgan, G. W. (2011). Estimation of total leaf area in perennial plants using image analysis. Revista Brasileira de Engenharia Agrícola e Ambiental, 15(1), 96–101. https://doi.org/10.1590/S1415-43662011000100014.
Martins, G. D., & Galo, M. D. L. B. T. (2014). Detection of infested areas by Nematodes and Migdolus Fryanus in sugarcane from Rapideye multispectral images. Revista Brasileira de Cartografia, 1(66/2), 285–301.
Moreira, M. A., Adami, M., & Rudorff, B. F. T. (2004). Spectral and temporal behavior analysis of coffee crop in Landsat images. Pesquisa Agropecuária Brasileira, 39(3), 223–231. https://doi.org/10.1590/S0100-204X2004000300004.
Moscetti, R., Haff, R. P., Stella, E., Contini, M., Monarca, D., Cecchini, M., et al. (2015). Feasibility of NIR spectroscopy to detect olive fruit infested by Bactrocera oleae. Postharvest Biology and Technology, 99, 58–62. https://doi.org/10.1016/j.postharvbio.2014.07.015.
Motomiya, A. V. A., Molin, J. P., Motomiya, W. R., & Baio, F. H. R. (2012). Mapping of the normalized difference vegetation index in cotton field. Pesquisa Agropecuária Tropical, 42(1), 112–118. https://doi.org/10.1590/S1983-40632012000100016.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009.
Oerke, E. C., & Steiner, U. (2010). Potential of digital thermographythermography for disease control. In E. C. Oerke, R. Gerhards, G. Menz, & R. Sikora (Eds.), Precision crop protection—The Challenge and use of heterogeneity (pp. 167–182). Dordrecht: Springer.
Oumar, Z., & Mutanga, O. (2014). Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 39–46. https://doi.org/10.1016/j.isprsjprs.2013.10.010.
Park, Y. L., Perring, T. M., Krell, R. K., Hashim-buckey, J. M., & Hill, B. L. (2011). Spatial distribution of Pierce’s disease related to incidence vineyard characteristics and surrounding land uses. American Journal of Enology and Viticulture. http://www.ajevonline.org/content/62/2/229.
Penuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221–230.
Pérez, C. D. P., Pozza, E. A., Pozza, A. A. A., Freitas, A. S., & Silva, M. G. D. (2017). Nitrogen and potassium in the intensity of bacteral blight of coffee seedlings in nutrient solution. Coffee Science, 12(1), 60–68.
Pinter, P. J., Jr., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S., et al. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647–664.
Pozza, E. A., Carvalho, V. L., & Chalfoun, S. M. (2010). Sintomas de injurias causadas por doenças do cafeeiro. In R. J. Guimarães, A. N. G. Mendes, & D. P. Baliza (Eds.), Semiologia do Cafeeiro (pp. 67–106). Brasil: Editora UFLA.
Prabhakar, M., Prasad, Y. G., Desai, S., Thirupathi, M., Gopika, K., Rao, G. R., et al. (2013). Hyperspectral remote sensing of yellow mosaic severity and associated pigment losses in Vigna mungo using multinomial logistic regression models. Crop Protection, 45, 132–140. https://doi.org/10.1016/j.cropro.2012.12.003.
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126. https://doi.org/10.1016/0034-4257(94)90134-1.
Qin, Z., & Zhang, M. (2005). Detection of rice sheath blight for in-season disease management using multispectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 7(2), 115–128. https://doi.org/10.1016/j.jag.2005.03.004.
Ramos, A. H., & Shavdia, L. D. (1976). A dieback of coffee in Kenya. Plant Disease Reporter, 60(10), 831–835.
Ray, S. S., Jain, N., Arora, R. K., Chavan, S., & Panigrahy, S. (2011). Utility of hyperspectral data for potato late blight disease detection. Journal of the Indian Society of Remote Sensing, 39(2), 161. https://doi.org/10.1007/s12524-011-0094-2.
Rodrigues, L. M. R., Almeida, I. M., Patrício, F. R., Beriam, L. O., Maciel, K. W., & Braghini, M. T. (2017). Aggressiveness of strains and inoculation methods for resistance assessment to bacterial halo blight on coffee seedlings. Journal of Phytopathology, 165(2), 105–114. https://doi.org/10.1111/jph.12543.
Rodrigues, L. M. R., Ameida, I. M. G., Patricio, F. R. A., Beriam, L. O. S., Maciel. K. W., Braghini, M. T., et al. (2013). Mancha aureolada do cafeeiro causada por Pseudomonas syringae pv. garcae. Brasil: Instituto Agronômico de Campinas.
Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375–384. https://doi.org/10.1016/0034-4257(94)00114-3.
Roumagnac, P., Pruvost, O., Chiroleu, F., & Hughes, G. (2004). Spatial and temporal analyses of bacterial blight of onion caused by Xanthomonas axonopodis pv. allii. Phytopathology, 94(2), 138–146. https://doi.org/10.1094/phyto.2004.94.2.138.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, 1, 309–317.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001.
Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72(1), 1–13. https://doi.org/10.1016/j.compag.2010.02.007.
Sera, T. (2001). Coffee genetic breeding at IAPAR. Crop Breeding and Applied Biotechnology, 1(2), 179–190. https://doi.org/10.13082/1984-7033.v01n02a08.
Shafri, H. Z., Anuar, M. I., Seman, I. A., & Noor, N. M. (2011). Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data. International Journal of Remote Sensing, 32(22), 7111–7129. https://doi.org/10.1080/01431161.2010.519003.
Silva, F. M. D., Alves, M. D. C., Souza, J. C. S., & Oliveira, M. S. D. (2010). Effects of manual harvesting on coffee (Coffea arabica L.) crop biannuality in Ijaci. Minas Gerais. Ciência e Agrotecnologia, 34(3), 625–632. https://doi.org/10.1590/s1413-70542010000300014.
Slaton, M. R., Hunt, E. R., & Smith, W. K. (2001). Estimating near-infrared leaf reflectance from leaf structural characteristics. American Journal of Botany, 88(2), 278–284.
Stoll, M., Schultz, H. R., & Berkelmann-Loehnertz, B. (2008). Exploring the sensitivity of thermal imaging for Plasmopara viticola pathogen detection in grapevines under different water status. Functional Plant Biology, 35(4), 281–288. https://doi.org/10.1071/FP07204.
USDA. (2017). Annual report coffee annual Brazil. Retrieved November 2, 2017, from http://usda.mannlib.cornell.edu/usda/fas/tropprod//2010s/2016/tropprod-12-16-2016.pdf.
USGS. (2017). Landsat 8 (L8) level 1 (L1) data format control book (DFCB). Retrieved March 22, 2017, from https://landsat.usgs.gov/sites/default/files/documents/LSDS-809-Landsat8-Level1DFCB.pdf.
Zhang, M., Qin, Z., Liu, X., & Ustin, S. L. (2003). Detection of stress in tomatoes induced by late blight disease in California. USA. using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4(4), 295–310. https://doi.org/10.1016/s0303-2434(03)00008-4.
Zoccoli, D. M., Takatsu, A., & Hidemi Uesugi, C. (2011). Occurrence of halo. Bragantia, 70(4), 843–849.
Acknowledgements
To the Neumman Kaffee Gruppe (NKG) for allowing implementing the experiments in their crops and providing the management logistics of these areas. To the technical support team and the Foundation for Supporting Research of the State of Minas Gerais (FAPEMIG) for funding the study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Marin, D.B., de Carvalho Alves, M., Pozza, E.A. et al. Multispectral radiometric monitoring of bacterial blight of coffee. Precision Agric 20, 959–982 (2019). https://doi.org/10.1007/s11119-018-09623-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-018-09623-9