Vegetative Vigor of Maize Crop Obtained Through Vegetation Indexes in Orbital and Aerial Sensors Images

Santana, Lucas Santos; Ferraz, Gabriel Araújo e Silva; Santos, Luana Maria dos; Maciel, Daniel Andrade; Barata, Rafael Alexandre Pena; Reynaldo, Étore Francisco; Rossi, G.

doi:10.18011/bioeng2019v13n3p195-206

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The researchers related to the third cluster, in blue, are characterized by research in coffee-growing by remote sensing analysis. Mapping coffee plantations by remote sensing aims to contribute to the identification of spatial variability using spectral responses [57]. The researchers related to the third cluster, in blue, are characterized by research in coffee-growing by remote sensing analysis.…”

Section: R Authors Id H-i (Scopus) H-i (Wos) Nc Ndmentioning

confidence: 99%

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Advances in Precision Coffee Growing Research: A Bibliometric Review

et al. 2021

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Precision coffee-growing technologies contribute to increased yield, operational efficiency, and final product quality. In addition, they strengthen coffee growing in the global agricultural scenario, which makes this activity increasingly competitive. Scientific research is essential for technological development and offering security regarding its application. For relevant research identification, bibliometric revision methods expose the best studies and their relationships with countries and authors, providing a complete map of research directions. This study identified the main contributions and contributors to academic research generation about precision coffee growing from 2000 to 2021. Bibliometric analysis was performed in VOSViewer software from the referential bases Scopus and Web of Science that identified 150 articles. Based on the number of citations, publications about precision coffee-growing showed Brazilian institutions at the top of the list, and Brazil’s close relationships with North American and South African institutions. Geostatistical analysis, remote sensing and spatial variability mapping of cultivation areas were used in most experimental research. A trend in research exploring machine learning technologies and autonomous systems was evident. The identification of the main agents of scientific development in precision coffee growing contributes to objective advances in the development and application of new management systems. Overall, this analysis represents wide precision coffee growing research providing valuable information for farmers, policymakers, and researchers.

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Section: R Authors Id H-i (Scopus) H-i (Wos) Nc Ndmentioning

confidence: 99%

Section: R Authors Id H-i (Scopus) H-i (Wos) Nc Ndmentioning

confidence: 99%

Advances in Precision Coffee Growing Research: A Bibliometric Review

et al. 2021

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“…Some technologies have been tested in precision coffee farming. Emerging applications such as the capture of aerial images by means of RPA are increasingly common in coffee fields, and these include mapping pockets of invasive weeds, revealing irrigation and fertilization anomalies [10], determining the ripeness index of the crop [11,12], detecting faults in the coffee plant [13], improving geometric errors in images obtained by RPA [14], estimating vegetation volume [15], estimating indirect measures in coffee plants [16], evaluating planting quality in high-slope terrain [17], and determining biophysical parameters and measuring the height and diameter of coffee plants [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Water Conditions in Coffee Plantations Using RPA

et al. 2022

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The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants.

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“…Due to the limitations of the traditional methods, either because they are laborious or require the destruction of samples, the method proposed in this work seeks to demonstrate that it is possible to gather information to check the nutritional status of plants in an automated way. RPAs could be used to this end, which have undergone great technological advances regarding data communication speeds, load capacity and flight autonomy (Santana et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multispectral aerial images for the evaluation of maize crops

et al. 2020

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ABSTRACT The combination of multispectral aerial images and computational processing is emerging as one of the solutions used in precision agriculture to observe the nutritional status of plants. The objective of this study is therefore to associate the nitrogen content and dry weight of the aerial part of maize plants (DW) with the vegetation indices obtained by multispectral aerial images (NDVIA and NDREA), and with the SPAD index and the Greenseeker NDVI, in the vegetative stage V6. To this end, randomized blocks in a factorial scheme of 6x4 (six nitrogen doses at the base and four different flight altitudes) were used, with three replications. The collected data was submitted to ANOVA with the F test (p = 0,05) and subsequent regression analysis. The study showed that it is possible to estimate the dry weight of the aerial part of maize plants and the nitrogen content in the leaves through the processing of multispectral aerial images, using the NDVI and NDRE spectral vegetation indices. The portable chlorophyll meter SPAD (model SPAD-502) also had promissing results in the estimation of nitrogen content, while the Greenseeker NDVI sensor accurately estimated nitrogen content and dry weight.

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Vegetative Vigor of Maize Crop Obtained Through Vegetation Indexes in Orbital and Aerial Sensors Images

Cited by 5 publications

References 20 publications

Advances in Precision Coffee Growing Research: A Bibliometric Review

Advances in Precision Coffee Growing Research: A Bibliometric Review

Evaluation of the Water Conditions in Coffee Plantations Using RPA

Multispectral aerial images for the evaluation of maize crops

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