Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes

The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the...

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Main Authors: Patrícia Carvalho da Silva, Walter Quadros Ribeiro Junior, Maria Lucrecia Gerosa Ramos, Maurício Ferreira Lopes, Charles Cardoso Santana, Raphael Augusto das Chagas Noqueli Casari, Lemerson de Oliveira Brasileiro, Adriano Delly Veiga, Omar Cruz Rocha, Juaci Vitória Malaquias, Nara Oliveira Silva Souza, Henrique Llacer Roig
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Sensors
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Online Access:https://www.mdpi.com/1424-8220/24/22/7271
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author Patrícia Carvalho da Silva
Walter Quadros Ribeiro Junior
Maria Lucrecia Gerosa Ramos
Maurício Ferreira Lopes
Charles Cardoso Santana
Raphael Augusto das Chagas Noqueli Casari
Lemerson de Oliveira Brasileiro
Adriano Delly Veiga
Omar Cruz Rocha
Juaci Vitória Malaquias
Nara Oliveira Silva Souza
Henrique Llacer Roig
author_facet Patrícia Carvalho da Silva
Walter Quadros Ribeiro Junior
Maria Lucrecia Gerosa Ramos
Maurício Ferreira Lopes
Charles Cardoso Santana
Raphael Augusto das Chagas Noqueli Casari
Lemerson de Oliveira Brasileiro
Adriano Delly Veiga
Omar Cruz Rocha
Juaci Vitória Malaquias
Nara Oliveira Silva Souza
Henrique Llacer Roig
author_sort Patrícia Carvalho da Silva
collection DOAJ
description The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the inherent characteristics of vegetation and optimize the spatial and temporal evaluation of different crops. The experiment tested three coffee genotypes (Catuaí 62, E237 and Iapar 59) under five water regimes: (1) FI 100 (year-round irrigation with 100% replacement of evapotranspiration), (2) FI 50 (year-round irrigation with 50% evapotranspiration replacement), (3) WD 100 (no irrigation from June to September (dry season) and, thereafter, 100% evapotranspiration replacement), (4) WD 50 (no irrigation from June to September (water stress) and, thereafter, 50% evapotranspiration replacement) and (5) rainfed (no irrigation during the year). The irrigated treatments were watered with irrigation and precipitation. Most indices were highest in response to full irrigation (FI 100). The values of the NDVI ranged from 0.87 to 0.58 and the SAVI from 0.65 to 0.38, and the values of these indices were lowest for genotype E237 in the rainfed areas. The indices NDVI, OSAVI, MCARI, NDRE and GDVI were positively correlated very strongly with photosynthesis (A) and strongly with transpiration (E) of the coffee trees. On the other hand, temperature-based indices, such as canopy temperature and the TCARI index correlated negatively with A, E and stomatal conductance (gs). Under full irrigation, the tested genotypes did not differ between the years of evaluation. Overall, the index values of Iapar 59 exceeded those of the other genotypes. The use of VIs to evaluate coffee tree performance under different water managements proved efficient in discriminating the best genotypes and optimal water conditions for each genotype. Given the economic importance of coffee as a crop and its susceptibility to extreme events such as drought, this study provides insights that facilitate the optimization of productivity and resilience of plantations under variable climatic conditions.
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spelling doaj-art-7ffa2f6f273744eabb1136af6d837efc2025-08-20T02:27:39ZengMDPI AGSensors1424-82202024-11-012422727110.3390/s24227271Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation RegimesPatrícia Carvalho da Silva0Walter Quadros Ribeiro Junior1Maria Lucrecia Gerosa Ramos2Maurício Ferreira Lopes3Charles Cardoso Santana4Raphael Augusto das Chagas Noqueli Casari5Lemerson de Oliveira Brasileiro6Adriano Delly Veiga7Omar Cruz Rocha8Juaci Vitória Malaquias9Nara Oliveira Silva Souza10Henrique Llacer Roig11Faculdade de Agronomia e Medicina Veterinária, Universidade de Brasília, Brasília 70910970, BrazilEmbrapa Cerrados, Empresa Brasileira de Pesquisa Agropecuária, Planaltina 73310970, BrazilFaculdade de Agronomia e Medicina Veterinária, Universidade de Brasília, Brasília 70910970, BrazilFaculdade de Agronomia e Medicina Veterinária, Universidade de Brasília, Brasília 70910970, BrazilInstituto Tecnológico de Agropecuária de Pitangui (ITAP), Empresa de Pesquisa Agropecuária de Minas Gerais, Pitangui 35650000, BrazilLaboratório de Geoprocessamento,, Instituto de Geociências, Universidade de Brasília, Brasília 70910970, BrazilEmbrapa Cerrados, Empresa Brasileira de Pesquisa Agropecuária, Planaltina 73310970, BrazilEmbrapa Cerrados, Empresa Brasileira de Pesquisa Agropecuária, Planaltina 73310970, BrazilEmbrapa Café, Empresa Brasileira de Pesquisa Agropecuária, BR 020, Km 18, Brasília 73310970, BrazilEmbrapa Cerrados, Empresa Brasileira de Pesquisa Agropecuária, Planaltina 73310970, BrazilFaculdade de Agronomia e Medicina Veterinária, Universidade de Brasília, Brasília 70910970, BrazilLaboratório de Geoprocessamento,, Instituto de Geociências, Universidade de Brasília, Brasília 70910970, BrazilThe advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the inherent characteristics of vegetation and optimize the spatial and temporal evaluation of different crops. The experiment tested three coffee genotypes (Catuaí 62, E237 and Iapar 59) under five water regimes: (1) FI 100 (year-round irrigation with 100% replacement of evapotranspiration), (2) FI 50 (year-round irrigation with 50% evapotranspiration replacement), (3) WD 100 (no irrigation from June to September (dry season) and, thereafter, 100% evapotranspiration replacement), (4) WD 50 (no irrigation from June to September (water stress) and, thereafter, 50% evapotranspiration replacement) and (5) rainfed (no irrigation during the year). The irrigated treatments were watered with irrigation and precipitation. Most indices were highest in response to full irrigation (FI 100). The values of the NDVI ranged from 0.87 to 0.58 and the SAVI from 0.65 to 0.38, and the values of these indices were lowest for genotype E237 in the rainfed areas. The indices NDVI, OSAVI, MCARI, NDRE and GDVI were positively correlated very strongly with photosynthesis (A) and strongly with transpiration (E) of the coffee trees. On the other hand, temperature-based indices, such as canopy temperature and the TCARI index correlated negatively with A, E and stomatal conductance (gs). Under full irrigation, the tested genotypes did not differ between the years of evaluation. Overall, the index values of Iapar 59 exceeded those of the other genotypes. The use of VIs to evaluate coffee tree performance under different water managements proved efficient in discriminating the best genotypes and optimal water conditions for each genotype. Given the economic importance of coffee as a crop and its susceptibility to extreme events such as drought, this study provides insights that facilitate the optimization of productivity and resilience of plantations under variable climatic conditions.https://www.mdpi.com/1424-8220/24/22/7271water supply<i>Coffea arabica</i>UAVCerrado
spellingShingle Patrícia Carvalho da Silva
Walter Quadros Ribeiro Junior
Maria Lucrecia Gerosa Ramos
Maurício Ferreira Lopes
Charles Cardoso Santana
Raphael Augusto das Chagas Noqueli Casari
Lemerson de Oliveira Brasileiro
Adriano Delly Veiga
Omar Cruz Rocha
Juaci Vitória Malaquias
Nara Oliveira Silva Souza
Henrique Llacer Roig
Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes
Sensors
water supply
<i>Coffea arabica</i>
UAV
Cerrado
title Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes
title_full Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes
title_fullStr Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes
title_full_unstemmed Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes
title_short Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes
title_sort multispectral images for drought stress evaluation of arabica coffee genotypes under different irrigation regimes
topic water supply
<i>Coffea arabica</i>
UAV
Cerrado
url https://www.mdpi.com/1424-8220/24/22/7271
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