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

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Abstract

Pseudomonas syringae pv. garcae, the causal agent of coffee disease bacterial blight, causes losses in nurseries and coffee fields. In this work, the objective was to evaluate integrated bacterial blight management in a coffee (Coffea arabica L.) field based on disease spatial pattern and ecological variables. The coffee field was composed by 85 georeferenced sample points, containing 5 plants representing a georeferenced point, being the spatial support of the experiment. Disease intensity classes were predicted in the field by machine learning algorithms fitted to big data on surface reflectance and spectral indices derived from digital image processing of Landsat-8 OLI/TIRS, as well as morphometric and hydrological attributes determined by geocomputation algorithms. Geostatistical modeling was used to characterize the spatial pattern and map the disease to gain epidemiological knowledge and precisely manage bacterial blight. Random forest algorithm enabled to detect the importance of relief morphometry associated with bacterial blight spatial progress in the coffee field, mainly according to the altitude and flow line curvature of the terrain. Probabilistic information on disease spatial pattern, modeled considering external trend effects of the topography variation, can be useful information for disease spatial prediction and integrated management based on georeferenced disease sampling. Multiple environmental variables may be carefully considered to evaluate mechanisms of interactions of bacterial blight with coffee plants and the physical environment.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thanks to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), the Instituto Brasileiro de Ciência e Tecnologia do Café (INCT-Coffee), and the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) for contributing with this study. The authors also thank the farmers for contributing with the establishment of the experiment.

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Authors and Affiliations

  1. Departamento de Engenharia Agrícola, Universidade Federal de Lavras, Lavras, MG, 37200-000, Brazil

    Marcelo de Carvalho Alves

  2. Departamento de Fitopatologia, Universidade Federal de Lavras, Lavras, MG, 37200-000, Brazil

    Edson Ampélio Pozza

  3. Departamento de Engenharia Sanitária E Ambiental, Universidade Federal de Mato Grosso, Cuiabá, MT, 78060-900, Brazil

    Luciana Sanches

  4. Centro de Ciências Agrárias, Universidade Estadual da Região Tocantina Do Maranhão, Imperatriz, MA, 65901-480, Brazil

    Leonidas Leoni Belan

  5. Instituto Federal de Minas Gerais, Bambuí, MG, 38900-000, Brazil

    Marcelo Loran de Oliveira Freitas

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  1. Marcelo de Carvalho Alves
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  2. Edson Ampélio Pozza
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  3. Luciana Sanches
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  4. Leonidas Leoni Belan
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  5. Marcelo Loran de Oliveira Freitas
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Contributions

Marcelo de Carvalho Alves and Edson Ampélio Pozza conceived of the current idea. The initial draft was written by Marcelo de Carvalho Alves and Luciana Sanches. Leonidas Leoni Belan and Marcelo Loran de Oliveira made disease assessment in the coffee field. All authors provided critical comments and helped shape the final manuscript.

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Correspondence to Marcelo de Carvalho Alves.

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de Carvalho Alves, M., Pozza, E.A., Sanches, L. et al. Insights for improving bacterial blight management in coffee field using spatial big data and machine learning. Trop. plant pathol. 47, 118–139 (2022). https://doi.org/10.1007/s40858-021-00474-w

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