Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

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Spectral signature of brown rust and orange rust in sugarcane

Revista Facultad de Ingeniería Universidad de Antioquia ◽

10.17533/udea.redin.20191042 ◽

2020 ◽

pp. 9-20

Author(s):

Eniel Rodríguez-Machado ◽

Osmany Aday-Díaz ◽

Luis Hernández-Santana ◽

Jorge Luís Soca-Muñoz ◽

Rubén Orozco-Morales

Keyword(s):

Precision Agriculture ◽

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Brown Rust ◽

Spectral Signature ◽

Spatial And Temporal Variability ◽

Mean Values ◽

Index Value

Precision agriculture, making use of the spatial and temporal variability of cultivable land, allows farmers to refine fertilization, control field irrigation, estimate planting productivity, and detect pests and disease in crops. To that end, this paper identifies the spectral reflectance signature of brown rust (Puccinia melanocephala) and orange rust (Puccinia kuehnii), which contaminate sugar cane leaves (Saccharum spp.). By means of spectrometry, the mean values and standard deviations of the spectral reflectance signature are obtained for five levels of contamination of the leaves in each type of rust, observing the greatest differences between healthy and diseased leaves in the red (R) and near infrared (NIR) bands. With the results obtained, a multispectral camera was used to obtain images of the leaves and calculate the Normalized Difference Vegetation Index (NDVI). The results identified the presence of both plagues by differentiating healthy from contaminated leaves through the index value with an average difference of 11.9% for brown rust and 9.9% for orange rust.

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Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

Czech Polar Reports ◽

10.5817/cpr2018-2-21 ◽

2018 ◽

Vol 8 (2) ◽

pp. 249-259 ◽

Cited By ~ 2

Author(s):

Miloš Barták ◽

Kumud Bandhu Mishra ◽

Michaela Marečková

Keyword(s):

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Spectral Characteristics ◽

Structural Features ◽

Coefficient Of Determination ◽

Curvilinear Relationship ◽

Structural Deterioration ◽

Reflectance Indices

Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

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Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽

10.3390/w12092359 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2359 ◽

Cited By ~ 1

Author(s):

Robson Argolo dos Santos ◽

Everardo Chartuni Mantovani ◽

Roberto Filgueiras ◽

Elpídio Inácio Fernandes-Filho ◽

Adelaide Cristielle Barbosa da Silva ◽

...

Keyword(s):

Decision Making ◽

Precision Agriculture ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Low Cost ◽

Vegetation Indices ◽

Maize Crop ◽

Evaporative Fraction ◽

Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

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Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

Remote Sensing ◽

10.3390/rs12162542 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2542

Author(s):

Han Lu ◽

Tianxing Fan ◽

Prakash Ghimire ◽

Lei Deng

Keyword(s):

Precision Agriculture ◽

Vegetation Change ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Change Analysis ◽

Vegetation Monitoring ◽

Ground Point ◽

Highly Correlated

In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R2) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R2 > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis.

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Comparison of Spectral Reflectance and Multispectrally Induced Fluorescence to Determine Winter Wheat Nitrogen Deficit

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1059.127 ◽

2014 ◽

Vol 1059 ◽

pp. 127-133 ◽

Cited By ~ 1

Author(s):

Jana Galambošová ◽

Miroslav Macák ◽

Marek Živčák ◽

Vladimír Rataj ◽

Pavol Slamka ◽

...

Keyword(s):

Precision Agriculture ◽

Spectral Reflectance ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

N Uptake ◽

Health Effect ◽

Growth Stages ◽

Commercial Technology ◽

Reflectance Indices ◽

Crop Health

Technical and technological aspects of variable rate nitrogen fertilization receive much attention nowadays. Current commercial technology is based on the use of spectral reflectance of crop. However, these have some limitations as variety dependence, crop health effect and limited use in more developed growth stages. New parameters overcoming these problems need to be assessed and their potential in precision agriculture should be considered. Multispectrally induced fluorescence is a progressive method. In addition to chlorophyll content, it allows to determine phenolic compounds, which is a product of metabolism of the plant under nitrogen deficit and is considered as the most exact indicator of nitrogen deficit. Comparing the spectral reflectance indices (normalized difference vegetation index – NDVI and normalized difference red edge index – NDRE) and multispectral fluorescence index (nitrogen balance index – NBI), these performed similarly in terms of determining the leaves biomass and nitrogen content in %, NDRE and NBI reflected significantly also aboveground N; however, only the correlation of NDVI reflected with N uptake and with leaf area was highly significant.

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Normalized difference vegetation index for desiccation evaluation with glyphosate + 2,4-D in magnetized spray solution

Brazilian Journal of Biology ◽

10.1590/1519-6984.246579 ◽

2023 ◽

Vol 83 ◽

Author(s):

C. B. Alvarenga ◽

G. S. M. Mundim ◽

E. A. Santos ◽

R. B. A. Gallis ◽

R. Zampiroli ◽

...

Keyword(s):

Weed Control ◽

Weed Management ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Dispersion Analysis ◽

Control Treatment ◽

Biological Efficiency ◽

Water Characteristics ◽

Spray Solution ◽

Influence Of Water

Abstract Water magnetization and geoprocessing are increasingly utilized tools in weed management. Our objective was to study the influence of water magnetization on herbicide efficiency and to verify whether there is a relationship between control scores and the normalized difference vegetation index (NDVI). In the laboratory experiment, water was subjected to magnetization and evaluated with respect to four characteristics. In the field experiment, plots of Brachiaria grass were subjected to treatments in a factorial scheme (6 × 2 + 1). Six herbicidal factors (doses of glyphosate and glyphosate + 2,4-D) and the magnetization or absence of magnetization of the spray solution were evaluated and compared against the control treatment (without spraying). Weed control assessments were carried out six times. Images were obtained using an embedded multispectral camera to determine the NDVI values. Data related to water characteristics were analyzed using the t test. Weed control and NDVI data were subjected to analysis of variance and are presented in regression graphs. Dispersion analysis of NDVI data was performed according to the control scores. The magnetization process decreased the pH of the water and increased the surface tension, but it did not influence the control scores or the NDVI. As the glyphosate dose was increased, the control scores were higher and the NDVI values were lower. Magnetized water did not affect the biological efficiency of the herbicides, and there was a strong correlation between the control scores and the NDVI values.

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HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21

Author(s):

Faradina Marzukhi ◽

Nur Nadhirah Rusyda Rosnan ◽

Md Azlin Md Said

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Soil Nutrient ◽

Oil Palm Plantation ◽

Sustainable Environment ◽

Moisture Analysis ◽

The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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The Effect of Surrounding Greenness on Type 2 Diabetes Mellitus: A Nationwide Population-Based Cohort in Taiwan

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010267 ◽

2020 ◽

Vol 18 (1) ◽

pp. 267

Author(s):

Hui-Ju Tsai ◽

Chia-Ying Li ◽

Wen-Chi Pan ◽

Tsung-Chieh Yao ◽

Huey-Jen Su ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Population Based ◽

Research Database ◽

Individual Level ◽

The Relationship

This study determines whether surrounding greenness is associated with the incidence of type 2 diabetes Mellitus (T2DM) in Taiwan. A retrospective cohort study determines the relationship between surrounding greenness and the incidence of T2DM during the study period of 2001–2012 using data from the National Health Insurance Research Database. The satellite-derived normalized difference vegetation index (NDVI) from the global MODIS database in the NASA Earth Observing System is used to assess greenness. Cox proportional hazard models are used to determine the relationship between exposure to surrounding greenness and the incidence of T2DM, with adjustment for potential confounders. A total of 429,504 subjects, including 40,479 subjects who developed T2DM, were identified during the study period. There is an inverse relationship between exposure to surrounding greenness and the incidence of T2DM after adjustment for individual-level covariates, comorbidities, and the region-level covariates (adjusted HR = 0.81, 95% CI: 0.79–0.82). For the general population of Taiwan, greater exposure to surrounding greenness is associated with a lower incidence of T2DM.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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Nitrogen management in wheat based on the normalized difference vegetation index (NDVI)

Ciência Rural ◽

10.1590/0103-8478cr20170743 ◽

2018 ◽

Vol 48 (9) ◽

Cited By ~ 2

Author(s):

André Luis Vian ◽

Christian Bredemeier ◽

Marcos Alexandre Turra ◽

Cecília Paz da Silva Giordano ◽

Elizandro Fochesatto ◽

...

Keyword(s):

Technical Efficiency ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Field Experiments ◽

Shoot Biomass ◽

Sensor Field ◽

N Dose ◽

Plant Emergence ◽

The Moment ◽

The Relationship

ABSTRACT: Biomass production and nitrogen (N) accumulated in wheat shoots may be used for quantifying optimal topdressing nitrogen doses. The objective of this study was to develop and validate models for estimating the amount of biomass and nitrogen accumulated in shoots and the N topdressing dose of maximum technical efficiency in wheat using the normalized difference vegetation index (NDVI) measured by an active optical canopy sensor. Field experiments were carried out in two years and treatments consisted of N doses applied at plant emergence and as topdressing. NDVI, shoot biomass and N accumulated in shoots at the growth stage of six fully expanded leaves and grain yield were evaluated, being determined the topdressing N dose of maximum technical efficiency (DMTE). The NDVI was positively correlated to shoot biomass and N content in shoots and models for the relationship between these variables were developed and validated. The DMTE was negatively correlated with the NDVI value evaluated at the moment of N topdressing application. Thus, NDVI evaluation by an active optical canopy sensor can be used for nitrogen fertilization in variable rate, allowing the adjustment of applied N doses in different areas within a field.

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