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Characterization of Recently Planted Coffee Cultivars from Vegetation Indices Obtained by a Remotely Piloted Aircraft System

Author

Listed:
  • Nicole Lopes Bento

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Gabriel Araújo e Silva Ferraz

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Rafael Alexandre Pena Barata

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Daniel Veiga Soares

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Luana Mendes dos Santos

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Lucas Santos Santana

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Patrícia Ferreira Ponciano Ferraz

    (Department of Engineering, Federal University of Lavras—UFLA, Aquenta Sol, 3037, Lavras 37200-900, MG, Brazil)

  • Leonardo Conti

    (Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura, 13, 50145 Florence, Italy)

  • Enrico Palchetti

    (Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura, 13, 50145 Florence, Italy)

Abstract

Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was to characterize three recently planted cultivars of Coffea arabica L. The study area is in Minas Gerais, Brazil, with a coffee plantation of the initial age of 5 months. The temporal behavior was determined based on monthly mean values. The spectral profile was obtained with mean values of the last month of dry and rainy periods. The statistical differences were obtained based on the non-parametric test of multiple comparisons. The estimation of the exponential equation was obtained through the Spearman correlation coefficient of determination and root mean square error. It was concluded that the seasons influence the behavior and development of cultivars, and significant statistical differences were detected for the variables, except for the chlorophyll variable. Due to the proximity and overlap of the reflectance values, spectral bands were not used to individualize cultivars. A correlation between the vegetation indices and leaf area index was observed and the exponential regression equation was estimated for each cultivar under study.

Suggested Citation

  • Nicole Lopes Bento & Gabriel Araújo e Silva Ferraz & Rafael Alexandre Pena Barata & Daniel Veiga Soares & Luana Mendes dos Santos & Lucas Santos Santana & Patrícia Ferreira Ponciano Ferraz & Leonardo , 2022. "Characterization of Recently Planted Coffee Cultivars from Vegetation Indices Obtained by a Remotely Piloted Aircraft System," Sustainability, MDPI, vol. 14(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1446-:d:735243
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    Citations

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    Cited by:

    1. Luana Mendes dos Santos & Gabriel Araújo e Silva Ferraz & Milene Alves de Figueiredo Carvalho & Sabrina Aparecida Teodoro & Alisson André Vicente Campos & Pedro Menicucci Neto, 2022. "Use of RPA Images in the Mapping of the Chlorophyll Index of Coffee Plants," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    2. Lucas Santos Santana & Gabriel Araújo e Silva Ferraz & Gabriel Henrique Ribeiro dos Santos & Nicole Lopes Bento & Rafael de Oliveira Faria, 2023. "Identification and Counting of Coffee Trees Based on Convolutional Neural Network Applied to RGB Images Obtained by RPA," Sustainability, MDPI, vol. 15(1), pages 1-17, January.

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