New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery
DOI:
https://doi.org/10.25186/.v18i.2170Abstract
Coffee Rust (Hemileia vastatrix) is considered the primary coffee disease in the world. The pathogenic fungus can find favorable environmental conditions in different countries, constantly threatening coffee producers. The previous detection of the incidence of coffee rust in a region is crucial because it provides an overview of the disease’s progress aiding in coffee plantations management. The objective of this work was the development of a vegetation index for remote monitoring of coffee rust infestation. Using satellite images from the MSI/Sentinel-2 collection, the Machine Learning classifier algorithm - Random Forest, and the cloud processing platform - Google Earth Engine, the most sensitives bands in coffee rust detection were determined, namely B4 (Red), B7 (Red Edge 3) and B8A (Red Edge 4). Thus, the Triangular Vegetation Index method was used to create a new vegetative index for remote detection of coffee rust infestation on a regional scale, named Coffee Rust Detection Index (CRDI). A linear regression model was created to estimate rust infestation based on the performance of the new index. The model presented a coefficient of determination (R²) of 62.5%, and a root mean square error (RMSE) of 0.107. In addition, a comparison analysis of the new index with eight other vegetative indices commonly used in the literature was carried out. The CRDI obtained the best performance in coffee rust detection among the others. This study shows that the new index CRDI has the robustness and general capacity to be used in monitoring coffee rust infestation on a regional scale.
Key words: Hemileia vastatrix; disease monitoring; triangular vegetation index method; google earth engine.
References
ALVES, M. C. et al. Modeling spatial variability and pattern of rust and brown eye spot in coffee agroecosystem. Journal of pest science, 82(2):137-148, 2009.
BROGE, N. H.; LEBLANC, E. 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, 2001.
CHAERLE, L. et al. Presymptomatic visualization of plant–virus interactions by thermography. Nature biotechnology, 17(8):813-816, 1999.
CHEMURA, A.; MUTANGA, O.; DUBE, T. Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precision Agriculture, 18(5):859-881, 2017.
COSTA, J. M.; GRANT, O. M.; CHAVES, M. M. Thermography to explore plant: Environment interactions. Journal of Experimental Botany, 64(13):3937-3949, 2013.
DOMINGUES, T.; BRANDÃO, T.; FERREIRA, J. C. Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey. Agriculture, 12(9):1350, 2022.
EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA - EMBRAPA. Controle da ferrugem o cafeeiro (Hemileia vastatrix) no estado de Rondônia. 1999. Available in: <https://ainfo.cnptia.embrapa.br/digital/bitstream/item/76700/1/FOL-7730-0001.pdf>. Access in: 18 December 2023.
EUROPEAN SPACE AGENCY - ESA. Sentinel-2 user handbook. 2019. Available in: <https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook>. Access in: 18 December 2023.
FERREIRA, L. G.; FERREIRA, N. C.; FERREIRA, M. E. Sensoriamento remoto da vegetação: Evolução e estado da arte. Acta Scientiarum. Biological Sciences, 30(4):379-390, 2008.
FLETCHER, R. S.; REDDY, K. N. Random forest and leaf multispectral reflectance data to differentiate three soybean varieties from two pigweeds. Computers and Electronics in Agriculture, 128:199-206, 2016.
FUNK, C. et al. The climate hazards infrared precipitation with stations: A new environmental record for monitoring extremes. Scientific data, 2(1):1-21, 2015.
GITELSON, A.; MERZLYAK, M. N. 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, 1994.
GENUER, R.; POGGI, J.-M.; TULEAU-MALOT, C. VSURF: An R package for variable selection using random forests. The R Journal, 7(2):19-33, 2015.
GITELSON, A.; MERZLYAK, M. N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of plant physiology, 143(3):286-292, 1994.
GITELSON, A. A.; MERZLYAK, M. N. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5):689-692, 1998.
HUETE, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2):195-213, 2002.
JORDAN, C. F. Derivation of leaf‐area index from quality of light on the forest floor. Ecology, 50(4):663-666, 1969.
JUSTICE, C. O. et al. The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE transactions on geoscience and remote sensing, 36(4):1228-1249, 1998.
JENSEN, J. R. Sensoriamento remoto do ambiente: Uma perspectiva em recursos terrestres. Tradução José Carlos Neves Epiphanio et al., São José dos Campos/SP, 2009. 40p.
KUSHALAPPA A. C.; ESKES A. B. Coffee rust: Epidemiology, resistance, and management. CRC Press, Boca Raton. p. 171-292, 1989. 54p.
LIU, L. et al. A disease index for efficiently detecting wheat fusarium head blight using sentinel-2 multispectral imagery. IEEE Access, 8:52181-52191, 2020.
MAHLEIN, A.-K. et al. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128:21-30, 2013.
MARIN, D. B. et al. Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models. Computers and Electronics in Agriculture, 190:106476, 2021.
MATIELLO, J. B. et al. Cultura de café no Brasil: Manual de recomendações. Rio de Janeiro: MAPA/Procafé, 2010.
MORAES, S. A. A ferrugem do cafeeiro: Importância, condições predisponentes, evolução e situação no Brasil. Campinas: Instituto Agronômico, 1983. 50p.
MOTTRAM, R.; DE JAGER, J. M.; DUCKWORTH, J. R. Evaluation of a water stress index for maize using an infra-red thermometer. Gewasproduksie Crop production, 1983.
OMASA, K. Image instrumentation methods of plant analysis. Physical methods in plant sciences, 1990. 243p.
PEREIRA, A. R.; CAMARGO, A. P.; CAMARGO, M. P. Agrometeorologia de cafezais no Brasil. Campinas: Instituto Agronômico, 2008. 127p.
PONZONI, F. J.; SHIMABUKURO, Y. E.; KUPLICH, T. M Sensoriamento remoto no estudo da vegetação. 2. ed. São Paulo: 176p. 2012.
POZZA, E. A.; CARVALHO, V. L.; CHALFOUN, S. M. Sintomas de injúrias causadas por doenças em cafeeiro. In: GUIMARÃES, R. J., MENDES, A. N.; BALIZA, D. P. Semiologia do cafeeiro: Sintomas de desordens nutricionais, fitossanitárias e fisiológicas. Lavras: Editora UFLA, p. 68-106, 2010.
PINTER JR, P. J. et al. Remote detection of biological stresses in plants with infrared thermometry. Science, 205(4406):585-587, 1979.
QUEIROZ, D. M. et al. Agricultura digital. 2 ed. São Paulo: Oficina de Textos, 2022. 224p.
ROUSE, J. W. et al. Monitoring vegetation systems in the great plains with ERTS proceeding. Third Earth Reserves Technology Satellite Symposium, Greenbelt: NASA SP-351. 1974.
RUMPF, T. et al. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and electronics in agriculture, 74(1):91-99, 2010.
UNITED STATES DEPARTMENT OF AGRICULTURE - USDA. Coffee: World markets and trade. United States Department of Agriculture: Washington, DC, USA, 2023. Available in: https://apps.fas.usda.gov/psdonline/circulars/coffee.pdf. Access in: 21 December 2023.
ZAMBOLIM, L. Current status and management of coffee leaf rust in Brazil. Tropical Plant Pathology, 41(1):1-8, 2016.
ZHENG, Q.; HUANG, W.; CUI, X. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensors, 18(3):868, 2018.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Coffee Science - ISSN 1984-3909Os direitos autorais dos artigos publicados nesta revista pertencem aos autores, com os primeiros direitos de publicação pertencentes à revista. Como os artigos aparecem nesta revista com acesso aberto, eles podem ser usados livremente, com as devidas atribuições, em aplicativos educacionais e não comerciais.
