Leaf area index and radiation extinction coefficient of a coffee canopy under variable drip irrigation levels

Costa, Jéfferson de Oliveira; Coelho, Rubens Duarte; Barros, Timóteo Herculino da Silva; Fraga, E. F.; Fernandes, André Luís Teixeira

doi:10.4025/actasciagron.v41i1.42703

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…A área foliar é um importante fator da produção e determina o uso da água pelas plantas e seu potencial de produtividade, sendo prejudicada quando exposta ao déficit hídrico, fato observado por Pizetta et al (2016), Rodrigues et al (2015) e Costa et al (2019).…”

Section: Resultsunclassified

Fator crítico de disponibilidade hídrica para o crescimento inicial do cafeeiro Conilon

et al. 2021

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O objetivo deste estudo foi avaliar o comportamento da transpiração relativa e do crescimento inicial do cafeeiro conilon em função do decréscimo da fração de água transpirável no solo (FATS). Foram realizados três experimentos em casa de vegetação utilizando plantas de 30, 60 e 90 dias pós-plantio. Em cada experimento aplicou-se dois tratamentos (T0 - sem déficit hídrico e Td - déficit hídrico), conduzidos num delineamento inteiramente casualizado, contendo quatro repetições. O final do experimento foi determinado quando as plantas de Td atingiram 10% da transpiração relativa do tratamento T0. As variáveis analisadas foram: transpiração relativa, altura, área foliar e coeficiente de transpiração. Concluiu-se que plantas jovens tiveram maior sensibilidade ao déficit, iniciando a redução da transpiração em 0,85 da FATS, já as plantas de 60 e 90 dias apresentaram redução em valores de 0,76 e 0,65, respectivamente. Nos três experimentos as plantas Td apresentaram menor coeficiente de transpiração.

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Section: Resultsunclassified

Fator crítico de disponibilidade hídrica para o crescimento inicial do cafeeiro Conilon

et al. 2021

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“…The studies carried out by [8] reached values of LAI at the upper part of the plant of 1.78 and of 6.5, on average, at the lower portion. In another study, [47] concluded that the LAI value was between 0 and 6.…”

Section: Lai Obtained By the Uavmentioning

confidence: 88%

“…Thus, by using this parameter, it is possible to infer the photosynthetic efficiency, growth and development patterns of a crop, as well as growth variations caused by environmental and genetic differences and damage from pests, diseases and management [3]. LA is a key component of crop growth [4] and yield prediction models [5], [6], [7] in addition to being a visible aspect of water stress, in which plants under water deficit decrease theshow reduced leaf area [8].…”

Section: Introductionmentioning

confidence: 99%

Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images

Santos

Ferraz

Barbosa

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to a UAV (unmanned aerial vehicle). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R 2 and RMSE values of 89% and 3.41, respectively, while those for LAI had R 2 and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and centerwest regions presenting LAI values > 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.

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“…The leaf area index (LAI) was used in this study because this parameter reflects the physiological changes of a plant in its canopy, which in turn is related to the crop yield, thus, the LAI can be applied in modelling and management in general in PA [36]. For the coffee crop, according to [37], the LAI varies according to the management applied, thus changing its leaf volume. Thus, the aim of the present study was to select and evaluate the temporal behaviour of the VI with the highest correlation to the LAI.…”

Section: Estimated Leaf Area Indexmentioning

confidence: 99%

Application of RGB Images Obtained by UAV in Coffee Farming

et al. 2021

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The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way.

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Leaf area index and radiation extinction coefficient of a coffee canopy under variable drip irrigation levels

Cited by 10 publications

References 20 publications

Fator crítico de disponibilidade hídrica para o crescimento inicial do cafeeiro Conilon

Fator crítico de disponibilidade hídrica para o crescimento inicial do cafeeiro Conilon

Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images

Application of RGB Images Obtained by UAV in Coffee Farming

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