Multispectral images for monitoring the physiological parameters of coffee plants under different treatments against nematodes View Full Text


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Article Info

DATE

2022-06-15

AUTHORS

Fernando Vasconcelos Pereira, George Deroco Martins, Bruno Sérgio Vieira, Gleice Aparecida de Assis, Vinicius Silva Werneck Orlando

ABSTRACT

The coffee crops are exposed to different pathogens that directly affect yield. These include nematodes, which attack the roots of plants and compromise their physiological development. Given the losses caused by this pathogen and the lack of information on spatial distribution in infested areas, it is important to adopt technologies that enable crops under different management systems to be monitored during their growth cycle. The remote sensing associated with machine learning algorithms is presented as a potential tool for monitoring agricultural crops. The present study assesses different machine learning algorithms, using radiometric values of multispectral images as input datasets, and identifies the best algorithms, to estimate the physiological agronomic parameters in coffee crops submitted to 11 treatments for nematode management. Based on the association between the images taken by a low-cost camera (bands: (R) red, (G) green and (B) blue) mounted on a remotely piloted aircraft (RPA), machine learning algorithms (Random Forest (RF) and support-vector machines (SVM)), the results made it possible to estimate with satisfactory accuracy (root mean square error (RMSE) less than 26.5% the main physical parameters of coffee plants: chlorophyll, plant height, branch length, number of branches and number of nodes per branch. With Planet satellite-derived multispectral bands, the SVM algorithm estimated plant canopy diameters with an RMSE of 7.74%. Based on the spatial distribution maps of the physical parameters, the application machine learning methods offered an opportunity to better use remote sensing data for monitoring coffee crop growth conditions and accurately guiding several management techniques. More... »

PAGES

1-33

References to SciGraph publications

  • 2008-01-01. Management of Meloidogyne spp. in Coffee Plantations in PLANT-PARASITIC NEMATODES OF COFFEE
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2008-01-01. Brazil in PLANT-PARASITIC NEMATODES OF COFFEE
  • 2013-10-02. The highly modified microcin peptide plantazolicin is associated with nematicidal activity of Bacillus amyloliquefaciens FZB42 in APPLIED MICROBIOLOGY AND BIOTECHNOLOGY
  • 2019-04-16. Biological Control of Nematodes by Plant Growth Promoting Rhizobacteria: Secondary Metabolites Involved and Potential Applications in SECONDARY METABOLITES OF PLANT GROWTH PROMOTING RHIZOMICROORGANISMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11119-022-09922-2

    DOI

    http://dx.doi.org/10.1007/s11119-022-09922-2

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