Relationship between Sentinel-2 orbital data and in situ monitoring of coffee rust

Abstract

Coffee rust (Hemileia vastatrix Berkeley & Broome) is the main coffee disease in Brazil. Coffee rust control is calendarbased and performed by applying chemicals in order to avoid the outburst of disease epidemics. The aim of this work was to identify coffee rust using multispectral orbital sensing through analyses utilising vegetation indices and coffee rust incidence, defoliation and yield data obtained in situ. Field samples were georeferenced using a high-accuracy global navigation satellite system receiver in two plots, in a 42-year-old, leaf rust susceptible coffee cultivar. Conventional chemical control of rust was performed in only one of the plots to serve as control. Coffee rust incidence in areas with and without chemical control was assessed over 5 months, from December 2018 to April 2019, a period with optimal environmental conditions for disease occurrence. Following analyses of different vegetation indices and data sampled in the coffee crop, Pearson's correlations between the variables were verified. Reported correlations occurred mainly among coffee rust incidence levels in February 2019 and vegetation indices calculated using Sentinel-2 images from

Robust Coffee Rust Detection Using UAV-Based Aerial RGB Imagery

“…Remote sensing systems have been used to detect CLR. For example, Cortez et al [15] identified CLR using multispectral orbital sensors and vegetation indices (VIs). The authors concluded that VIs based on band ratios in the near-infrared and red-edge regions are more sensitive to spectral changes in vegetation due to variations in CLR occurrence.…”

Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil

“…The nutritional balance of the coffee crop makes it more difficult to achieve the highest productivity, as a result of the drain of plant nutrients and soil reserves, causing, therefore, a tendency to increase the disease intensity [6]. Under these growing conditions, to mitigate the risks of epidemics with high rates of rust progress (r), the maintenance of water and soil fertility, which influence the nutrition of the coffee trees and consequently constitute and reinforce resistance barriers against the pathogen, should be part of the disease management strategies [23][24][25][26][27][28][29][30]. Furthermore, periodic pruning and the use of disease resistant or tolerant varieties contribute to the disease control [31].…”

Identificação aprimorada de seringais infestados por fitonematoides no município de Prata, MG, por sensoriamento remoto

“…Têm surgido diversos avanços nas tecnologias de SR desde o surgimento da agricultura de precisão (MULLA, 2013). Estes avanços atraíram muitos pesquisadores para estudar o uso de técnicas com satélites de SR para monitoramento em diversos aspectos na agricultura (BELDA et al, 2020;d"ANDRIMONT et al, 2020;GAO et al, 2020;LUO et al, 2020;CORTEZ et al, 2020;KIM, 2020;NZIMANDE et al, 2020).…”

“…Alguns trabalhos têm negligenciado a importância de se fazer uma análise criteriosa da série temporal das imagens pesquisadas. No caso do café, cultura em que se tem observado diversas publicações acerca do uso do sensoriamento remoto para o diagnóstico de nematoides (MARTINS et al, 2017;CORTEZ et al, 2020;ABREU JÚNIOR et al, 2020;LE, 2020;SILVA et al, 2021), mesmo sabendo que o índice de área foliar apresenta alta variabilidade espacial e temporal, influenciando na relação entre as variáveis biofísicas (JARAMILLO- GIRALDO et al, 2019), tais circunstâncias são desconsideradas.…”