A Spatial Analysis of Coffee Plant Temperature and Its Relationship with Water Potential and Stomatal Conductance Using a Thermal Camera Embedded in a Remotely Piloted Aircraft

Coffee is a key agricultural product in national and international markets. Physiological parameters, such as plant growth indicators, can signal interruptions in these processes. This study aimed to characterize the temperature obtained by a thermal camera embedded in a remotely piloted aircraft (RPA) and evaluate its relationship with the water potential (WP) and stomatal conductance (gs) of an experimental coffee plantation using geostatistical techniques. The experiment was conducted at the Federal University of Lavras, Minas Gerais, Brazil. A rotary-wing RPA with an embedded thermal camera flew autonomously at a height of 10 m and speed of 10 m/s. Images were collected on 26 November 2019 (rainy season), and 11 August 2020 (dry season), between 9:30 am and 11:30 am. Data on gs and WP were collected in the field. The thermal images were processed using FLIR Tools 5.13, and temperature analysis and spatialization were undertaken using geostatistical tools and isocolor maps by Kriging interpolation in R 4.3.2 software. Field data were superimposed on final crop temperature maps using QuantumGIS version 3.10 software. The study found that with decreasing WP, stomatal closure and reduction in gs occurred, increasing the temperature due to water deficit. The temperature distribution maps identified areas of climatic variations indicating water deficit.

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Publication Details

Journal
Agronomy
Published
2024-10-18
DOI
https://doi.org/10.3390/agronomy14102414
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

A Spatial Analysis of Coffee Plant Temperature and Its Relationship with Water Potential and Stomatal Conductance Using a Thermal Camera Embedded in a Remotely Piloted Aircraft

Valentina Becciolini, Luana Mendes dos Santos, Letícia Aparecida Gonçalves Xavier, Giuseppe Rossi et al.
Agronomy
Remote Sensing in Agriculture
article

A Spatial Analysis of Coffee Plant Temperature and Its Relationship with Water Potential and Stomatal Conductance Using a Thermal Camera Embedded in a Remotely Piloted Aircraft

Valentina Becciolini, Luana Mendes dos Santos, Letícia Aparecida Gonçalves Xavier, Giuseppe Rossi, Gabriel Araújo e Silva Ferraz, Alisson André Vicente Campos, Milene Alves de Figueiredo Carvalho, Pedro Menicucci Neto, Arnaud de Mattia
article en

Abstract

Coffee is a key agricultural product in national and international markets. Physiological parameters, such as plant growth indicators, can signal interruptions in these processes. This study aimed to characterize the temperature obtained by a thermal camera embedded in a remotely piloted aircraft (RPA) and evaluate its relationship with the water potential (WP) and stomatal conductance (gs) of an experimental coffee plantation using geostatistical techniques. The experiment was conducted at the Federal University of Lavras, Minas Gerais, Brazil. A rotary-wing RPA with an embedded thermal camera flew autonomously at a height of 10 m and speed of 10 m/s. Images were collected on 26 November 2019 (rainy season), and 11 August 2020 (dry season), between 9:30 am and 11:30 am. Data on gs and WP were collected in the field. The thermal images were processed using FLIR Tools 5.13, and temperature analysis and spatialization were undertaken using geostatistical tools and isocolor maps by Kriging interpolation in R 4.3.2 software. Field data were superimposed on final crop temperature maps using QuantumGIS version 3.10 software. The study found that with decreasing WP, stomatal closure and reduction in gs occurred, increasing the temperature due to water deficit. The temperature distribution maps identified areas of climatic variations indicating water deficit.

AgronomyVol. 14(10)
Universidade Federal de Lavras (BR), Brazilian Agricultural Research Corporation (BR), Instituto Federal Farroupilha (BR), University of Florence (IT)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Università degli Studi di Firenze, Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Universidade Federal de Lavras, Consórcio Pesquisa Café
Openalex Percentile: Top 82%
Remote Sensing in Agriculture
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