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Use of RPA Images in the Mapping of the Chlorophyll Index of Coffee Plants

Author

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  • Luana Mendes dos Santos

    (Agricultural Engineering Department, Federal University of Lavras, Lavras 37200-000, Minas Gerais, Brazil)

  • Gabriel Araújo e Silva Ferraz

    (Agricultural Engineering Department, Federal University of Lavras, Lavras 37200-000, Minas Gerais, Brazil)

  • Milene Alves de Figueiredo Carvalho

    (Embrapa Café, Brasília 70770-901, Distrito Federal, Brazil)

  • Sabrina Aparecida Teodoro

    (Agricultural Engineering Department, Federal University of Lavras, Lavras 37200-000, Minas Gerais, Brazil)

  • Alisson André Vicente Campos

    (Department of Agronomy/Phytotechnics, Federal University of Lavras, Lavras 37200-000, Minas Gerais, Brazil)

  • Pedro Menicucci Neto

    (Department of Agronomy/Phytotechnics, Federal University of Lavras, Lavras 37200-000, Minas Gerais, Brazil)

Abstract

Coffee trading is an important source of income for the Brazilian commercial balance. Chlorophyll (Chl) are pigments responsible for converting radiation into energy; these pigments are closely related to the photosynthetic efficiency of plants, and the evaluation of the nutritional status of the coffee tree. The inversion method can be used for estimating the canopy chlorophyll content (Chl canopy ) using the leaf chlorophyll content (Chl leaf ) and the leaf area index (LAI). The application of vegetation indices (VIs) in high spatial resolution images obtained from remotely piloted aircraft (RPA) can assist in the characterization of Chl canopy in addition to providing vital and fast information for monitoring crops and aiding decision-making. This study aimed to identify which VIs adequately explain the Chl and evaluate the relationships between the VIs obtained from remotely piloted aircraft (RPA) images and the Chl leaf and Chl canopy in coffee plants during the wet and dry seasons. The experiment was conducted on a Coffea arabica L. plantation in Lavras, Minas Gerais, Brazil. Images were collected on 26 November 2019 (wet), 11 August 2020 (dry), and 26 August 2021 (dry) by a multispectral camera embedded in a quadcopter. Plant height (H), crow diameter (D), and Chl leaf (a, b and total) data were collected in the field by a metre ruler (H and D) and sensor (Chl leaf ). The LAI was calculated based on H and D. The Chl canopy (a, b, and total) was calculated based on Chl leaf and LAI. The image processing was performed in Pix4D software, and postprocessing and calculation of the 21 VIs were performed in QGIS. Statistical analyses (descriptive, statistical tests, Pearson correlation, residuals calculation, and linear regression) were performed using the software R. The VIs from the RPA that best correlates to Chl canopy in the wet season were the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2 RPA ), Modified Simple Ratio (MSR RPA ) and Simple Ratio (SR RPA ). These VIs had high sensitivity and, therefore, were more affected by chlorophyll variability. For the two dry season studied days, there were no patterns in the relationships between Chl leaf , Chl canopy , and the VIs. It was possible to use the Chl inversion method for the coffee during the wet season.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13118-:d:941012
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    References listed on IDEAS

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    1. 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.
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    1. 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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