Spectral signature of brown rust and orange rust in sugarcane

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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Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v38i630456 ◽

2020 ◽

pp. 1-9

Author(s):

H. R. Naveen ◽

B. Balaji Naik ◽

G. Sreenivas ◽

Ajay Kumar ◽

J. Adinarayana ◽

...

Keyword(s):

Remote Sensing ◽

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Hyperspectral Remote Sensing ◽

Nitrogen Stress ◽

Fixed Amount ◽

Nitrogen Levels ◽

Stress Environment

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.

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

Planta Daninha ◽

10.1590/s0100-83582012000200024 ◽

2012 ◽

Vol 30 (2) ◽

pp. 437-447 ◽

Cited By ~ 5

Author(s):

A. Merotto JR. ◽

C. Bredemeier ◽

R.A. Vidal ◽

I.C.G.R. Goulart ◽

E.D. Bortoli ◽

...

Keyword(s):

Weed Control ◽

Precision Agriculture ◽

Weed Management ◽

Dry Matter ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Weed Interference ◽

Reflectance Indices ◽

The Relationship

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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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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Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

Agronomy ◽

10.3390/agronomy9050226 ◽

2019 ◽

Vol 9 (5) ◽

pp. 226 ◽

Cited By ~ 7

Author(s):

Stefano Marino ◽

Arturo Alvino

Keyword(s):

High Resolution ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Growth Stages ◽

Spatial And Temporal Variability ◽

Area Index ◽

Aerial Vehicle ◽

High Resolution Images

An on-farm research study was carried out on two small-plots cultivated with two cultivars of durum wheat (Odisseo and Ariosto). The paper presents a theoretical approach for investigating frequency vegetation indices (VIs) in different areas of the experimental plot for early detection of agronomic spatial variability. Four flights were carried out with an unmanned aerial vehicle (UAV) to calculate high-resolution normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI) images. Ground agronomic data (biomass, leaf area index (LAI), spikes, plant height, and yield) have been linked to the vegetation indices (VIs) at different growth stages. Regression coefficients of all samplings data were highly significant for both the cultivars and VIs at anthesis and tillering stage. At harvest, the whole plot (W) data were analyzed and compared with two sub-areas characterized by high agronomic performance (H) yield 20% higher than the whole plot, and low performances (L), about 20% lower of yield related to the whole plot). The whole plot and two sub-areas were analyzed backward in time comparing the VIs frequency curves. At anthesis, more than 75% of the surface of H sub-areas showed a VIs value higher than the L sub-plot. The differences were evident also at the tillering and seedling stages, when the 75% (third percentile) of VIs H data was over the 50% (second percentile) of the W curve and over the 25% (first percentile) of L sub-plot. The use of high-resolution images for analyzing the frequency value of VIs in different areas can be a useful approach for the detection of agronomic constraints for precision agriculture purposes.

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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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Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland

Remote Sensing ◽

10.3390/rs12172677 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2677

Author(s):

Maya Deepak ◽

Sarita Keski-Saari ◽

Laure Fauch ◽

Lars Granlund ◽

Elina Oksanen ◽

...

Keyword(s):

Discriminant Analysis ◽

Chlorophyll Content ◽

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Leaf Traits ◽

Silver Birch ◽

Red Edge

The goal of this study was to investigate the variation in the leaf spectral reflectance and its association with other leaf traits from 12 genotypes among three provenances of origin (populations) in a common garden for Finnish silver birch trees in 2015 and 2016. The spectral reflectance was measured in the laboratory from the detached leaves in the wavelength range of visible and near-infrared (VNIR, 400–1000 nm) and shortwave infrared (SWIR, 1000–2500 nm). The variation among the provenance was initially visualized with principal component analysis (PCA) and a clear separation among the provenances was detected with the discriminant analysis of principal components (DAPC) and partial least squares discriminant analysis (PLS-DA) depicting a less strong variation among the genotypes within the provenances. Wavelengths contributing to the separation of the genotypes and provenances were identified from the contribution plot of DAPC and the red edge was strongly related to the differences. Chlorophyll content showed clear provenance variation and was associated with the separation among the genotypes and provenances in the DAPC space. The normalized difference vegetation index (NDVI705,750) and chlorophyll reflectance index (CRI) showed clear significance among the provenances, whereas NDVI670,780 showed no variation. The variation in the chlorophyll content and the CRI and red edge-based NDVI indices indicated seasonal variation as the chlorophyll content starts increasing in early June. The correlation of foliar chlorophyll content and the chlorophyll-related spectral indices for the discrimination of provenances and genotypes are reported for the first time in a naturally occurring tree species consecutively for two years.

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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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APPLICATION OF SOFT COMPUTING METHODS AND SPECTRAL REFLECTANCE DATA FOR WHEAT GROWTH MONITORING

IRAQI JOURNAL OF AGRICULTURAL SCIENCES ◽

10.36103/ijas.v50i4.751 ◽

2019 ◽

Vol 50 (4) ◽

Author(s):

Sharabiani l. & et a

Keyword(s):

Soft Computing ◽

Precision Agriculture ◽

Spectral Reflectance ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Computing Methods ◽

Growth Monitoring ◽

Inference System ◽

Climate Conditions ◽

Soft Computing Methods

Technology of precision agriculture has caused to the remote sensors development that compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artiﬁcial Neural Networks (ANNs), as a soft computing methods, are one of the most efﬁcient methods for computing as compared to the statistical and analytical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant analysis development (SPAD), protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in 2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS models.

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