Insights for improving bacterial blight management in coffee field using spatial big data and machine learning

[1]  J. Deckers,et al.  Altitude and management affect soil fertility, leaf nutrient status and Xanthomonas wilt prevalence in enset gardens , 2021 .

[2]  Muhammad Ali Qureshi,et al.  Use of deep learning techniques for identification of plant leaf stresses: A review , 2020, Sustain. Comput. Informatics Syst..

[3]  Murilo Carneiro,et al.  The alarming vulnerability of Coffea canephora varieties and germplasm to a virulent strain of Pseudomonas syringae pv. garcae, the cause of bacterial halo blight of coffee in Brazil , 2020, Journal of Plant Pathology.

[4]  Juan Carlos Corrales,et al.  Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches , 2020, Comput. Electron. Agric..

[5]  Jonathan da Rocha Miranda,et al.  The use of machine learning in digital processing of satellite images applied to coffee crop. , 2020 .

[6]  G. Yashodha,et al.  An integrated approach for predicting and broadcasting tea leaf disease at early stage using IoT with machine learning – A review , 2020 .

[7]  Marcelo de Carvalho Alves,et al.  Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Glauco de Souza Rolim,et al.  Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases , 2020, International Journal of Biometeorology.

[9]  José G. M. Esgario,et al.  Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress , 2019, Comput. Electron. Agric..

[10]  D. Zylbersztajn,et al.  Business history and challenges for coffee cooperatives in Brazil: The case of Cooxupé Cooperative , 2020 .

[11]  Carolina Toledo Ferraz,et al.  Coffee Leaf Disease Recognition Based on Deep Learning and Texture Attributes , 2019, KES.

[12]  M. C. Alves,et al.  Geostatistical analysis of bacterial blight in coffee tree seedlings in the nursery , 2018, Summa Phytopathologica.

[13]  S. Destéfano,et al.  Bacterial halo blight of coffee crop: aggressiveness and genetic diversity of strains , 2017 .

[14]  G. H. Sera,et al.  IPR 102 - Dwarf Arabica coffee cultivar with resistance to bacterial halo blight , 2017 .

[15]  O. Mutanga,et al.  Remote sensing leaf water stress in coffee (Coffea arabica) using secondary effects of water absorption and random forests , 2017 .

[16]  P. Vitousek,et al.  Convergence and contrast in the community structure of Bacteria, Fungi and Archaea along a tropical elevation‐climate gradient , 2017, FEMS microbiology ecology.

[17]  J. Molnár,et al.  Bacterial Diseases of Wheat in the Southern Ural: Manifestations, Biological Characteristics and Monitoring Features , 2016 .

[18]  Ned Horning,et al.  Tools for Remote Sensing Data Analysis , 2015 .

[19]  Max Kuhn,et al.  Futility Analysis in the Cross-Validation of Machine Learning Models , 2014, ArXiv.

[20]  Qingxi Tong,et al.  Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance , 2014 .

[21]  C. Morris,et al.  The life history of Pseudomonas syringae: linking agriculture to earth system processes. , 2013, Annual review of phytopathology.

[22]  P. Gitonga,et al.  Methods for early evaluation for resistance to bacterial blight of coffee , 2013 .

[23]  S. Lindow,et al.  Thermo-Regulation of Genes Mediating Motility and Plant Interactions in Pseudomonas syringae , 2013, PloS one.

[24]  J. Lamichhane,et al.  Epidemiological Study of Hazelnut Bacterial Blight in Central Italy by Using Laboratory Analysis and Geostatistics , 2013, PloS one.

[25]  Marcelo de Carvalho Alves,et al.  Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust , 2011, Environ. Model. Softw..

[26]  Sarah C. Goslee,et al.  Analyzing Remote Sensing Data in R: The landsat Package , 2011 .

[27]  J. Macharia,et al.  Antimicrobial Activity of Foliar Fertilizer Formulations and their Effect on Ice Nucleation Activity of Pseudomonas syringae Pv. garcae Van Hall; the Causal Agent of Bacterial Blight of Coffee , 2011 .

[28]  Sassan Saatchi,et al.  Introduction: Elevation gradients in the tropics: laboratories for ecosystem ecology and global change research , 2010 .

[29]  E. Pozza,et al.  Modeling spatial variability and pattern of rust and brown eye spot in coffee agroecosystem , 2009, Journal of Pest Science.

[30]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[31]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[32]  G. H. Sera,et al.  Resistance to bacterial blight in arabica coffee cultivars , 2008 .

[33]  J. Avelino,et al.  Topography and crop management are key factors for the development of american leaf spot epidemics on coffee in costa rica. , 2007, Phytopathology.

[34]  D. Bieysse,et al.  Spatio-Temporal Dynamics of Arabica Coffee Berry Disease Caused by Colletotrichum kahawae on a Plot Scale. , 2007, Plant disease.

[35]  J. Qi,et al.  Remote Sensing for Grassland Management in the Arid Southwest , 2006 .

[36]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[37]  J. Böhner,et al.  Spatial Prediction of Soil Attributes Using Terrain Analysis and Climate Regionalisation , 2006 .

[38]  B. Pinty,et al.  GEMI: a non-linear index to monitor global vegetation from satellites , 1992, Vegetatio.

[39]  J. R. Jensen.  Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[40]  L. Lymburner,et al.  Estimation of Canopy-Average Surface-Specific Leaf Area Using Landsat TM Data , 2000 .

[41]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[42]  H. Wackernagle,et al.  Multivariate geostatistics: an introduction with applications , 1998 .

[43]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[44]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[45]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[46]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[47]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[48]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[49]  V. Caselles,et al.  Mapping burns and natural reforestation using thematic Mapper data , 1991 .

[50]  T. G. Freeman,et al.  Calculating catchment area with divergent flow based on a regular grid , 1991 .

[51]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[52]  S. S. Hirano,et al.  Population Biology and Epidemiology of Pseudomonas Syringae , 1990 .

[53]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[54]  C. Thorne,et al.  Quantitative analysis of land surface topography , 1987 .

[55]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[56]  C. Perry,et al.  Functional equivalence of spectral vegetation indices , 1984 .

[57]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[58]  D. Deering.  Measuring forage production of grazing units from Landsat MSS data , 1975 .

[59]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[60]  G. Birth,et al.  Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1 , 1968 .