Monitoring Growth Characters and Yield with Hyperspectral Remote Sensing in Wheat

This research aimed at studying the quantitative relationship between chlorophyll content, LAI, yield and spectral parameters of two species of winter wheat (Yangmai 13 and Xumai 31) with Free Air Temperature Increased facility. Four warming scenarios were designed in the experiment: all-day warming (AW, warming of 1.9°C, daytime warming (DW,6:00am-18:00pm,warming of 2.1°C), nighttime warming (NW,18:00 pm-6:00 am next day, warming of 1.7°C) and the control (CK, without warming). The results showed that reflectance of the four treatments at near infrared flat (nm-nm) had significant differences in booting stage and the sequence was: DW>AW>CK>NW. At visible band, the reflectance under NW treatment was much lower than that under other treatments. PND920 and P_Area920 were correlated with chlorophyll with high coefficient of determination. RVI and NDVI which constituted by the 810nm and 560nm reflectance were highly correlated with LAI. Booting stage was the optimum period for yield estimation. P_Depth560, NDVI (560,450) and RVI (560,450) were significantly related to yield and thus they could be utilized for yield prediction.

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Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions

Foods ◽

10.3390/foods10092057 ◽

2021 ◽

Vol 10 (9) ◽

pp. 2057

Author(s):

Ching-Wei Cheng ◽

Kun-Ming Lai ◽

Wan-Yu Liu ◽

Cheng-Han Li ◽

Yu-Hsun Chen ◽

...

Keyword(s):

Real Time ◽

Near Infrared ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Light Transmittance ◽

Coefficient Of Determination ◽

Alkaline Solutions ◽

Analysis Model ◽

Infrared Transmission ◽

Highly Correlated

Although many ultraviolet-visible-near-infrared transmission spectroscopy techniques have been applied to chicken egg studies, such techniques are not suitable for duck eggs because duck eggshells are much thicker than chicken eggshells. In this study, a high-transmission spectrometer using an equilateral prism as a dispersive element and a flash lamp as a light source was constructed to nondestructively detect the transmission spectrum of duck eggs and monitor the pickling of eggs. The evolution of egg transmittance was highly correlated with the albumen during pickling. The transmittance exponentially decays with time during this period, and the decay rate is related to the pickling rate. The colors of the albumen and yolk remain almost unchanged in the first stage. A multiple linear regression analysis model that realizes a one-to-one association between the days of pickling and the transmission spectra was constructed to determine the pickling duration in the second stage. The coefficient of determination reached 0.88 for a single variable, wavelength, at 590 nm. This method can monitor the maturity of pickled eggs in real time and does not require the evolution of light transmittance.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.141 ◽

1998 ◽

Vol 6 (1) ◽

pp. 229-234 ◽

Cited By ~ 27

Author(s):

William R. Windham ◽

W.H. Morrison

Keyword(s):

Fatty Acids ◽

Fatty Acid ◽

Near Infrared ◽

Fatty Acid Content ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Longissimus Dorsi ◽

Near Infrared Reflectance ◽

Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

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BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-7-3-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 3-8 ◽

Cited By ~ 2

Author(s):

M. Matsuoka ◽

M. Takagi ◽

S. Akatsuka ◽

R. Honda ◽

A. Nonomura ◽

...

Keyword(s):

Surface Properties ◽

Land Surface ◽

Goodness Of Fit ◽

Near Infrared ◽

Temporal Frequency ◽

Atmospheric Correction ◽

Time Of Day ◽

Coefficient Of Determination ◽

Near Infrared Reflectance ◽

Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

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Interrelation between body weights of sire, dam and their lambs at early stage of growth

Biotechnology in Animal Husbandry ◽

10.2298/bah2002205c ◽

2020 ◽

Vol 36 (2) ◽

pp. 205-214

Author(s):

Violeta Caro-Petrovic ◽

Milan Petrovic ◽

Dragana Ruzic-Muslic ◽

Nevena Maksimovic ◽

Irina Sycheva ◽

...

Keyword(s):

Body Weight ◽

Early Stage ◽

Descriptive Statistics ◽

Coefficient Of Determination ◽

Measures Of Association ◽

Paired Sample ◽

Body Weights ◽

Sample Test ◽

Highly Correlated

Records of female lambs and their parents of the Mis sheep breed have used. All animals are approximately have weaned at 90 days of age. Descriptive statistics, paired sample test, paired differences, measures of association, correlations and regression of body weights between female lambs and their parents have done. A complementary least body weights at 30 days and weaning between dams and lambs but utmost weight at 30 days, the lambs were higher while at weaning, the dams had higher weight. It can observe that the averages on body weights the rams were the highest, followed by lambs and the lowest the dams? body weights. The coefficient of determination of R2 varies from low to high, indicating that the lamb's body weight has more influenced by other factors that we have not considered. There were significant correlations between lamb body weight at birth and sire/dam body weight at birth. The results showed highly significant correlations of lamb's body weight at 30 days with dams but with sires, positive and very low. There had positive but no significant correlation between lamb body weight at weaning and sire body weight at weaning. Lamb body weight at weaning and dam body weight at weaning are highly correlated.

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Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.459 ◽

2005 ◽

Vol 13 (2) ◽

pp. 69-75 ◽

Cited By ~ 17

Author(s):

Roland Welle ◽

Willi Greten ◽

Thomas Müller ◽

Gary Weber ◽

Hartwig Wehrmann

Keyword(s):

Grain Yield ◽

Dry Matter ◽

Near Infrared ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Successful Implementation ◽

Grain Moisture ◽

Yield Determination ◽

Corn Grain

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

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Leka Ophiolite Complex as analogy to the serpentinization-carbonation system on Mars.

10.5194/epsc2021-230 ◽

2021 ◽

Author(s):

benjamin bultel ◽

Agata M. Krzesinska ◽

Damien Loizeau ◽

François Poulet ◽

Håkon O. Astrheim ◽

...

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Ground Truth ◽

Ultramafic Rocks ◽

Ophiolite Complex ◽

Spectral Parameters ◽

Thin Sections ◽

Accessory Minerals ◽

Mineral Assemblages ◽

Alteration Minerals

Serpentinization and carbonation have affected ultramafic rocks on Noachian Mars in several places called here serpentinization-carbonation systems (SCS). Among the most prominent SCS revealing mineral assemblages characteristic of serpentinization/carbonation is the Nili Fossae region [1]. Jezero crater &#8211; the target of the Mars 2020 rover &#8211;hosted a paleolake which constitutes a sink for sediments from Nili Fossae [1]. Thanks to the near infrared spectrometer onboard Mars2020 [2], the mission has the potential to offer ground truth measurement for other putative serpentinization/carbonation system documented on Mars. Several important aspects that may be addressed are: Do carbonates result from primary alteration of olivine-rich lithologies or are they derived by reprocessing of previous alteration minerals [3]? What is the composition? and nature of the protolith, which appear to be constituted of considerable amounts of olivine [4]? To reveal critical information regarding the conditions of serpentinization/carbonation, accessory minerals need detailed studies [1; 5]. In case of Jezero Crater, and serpentinization on Mars in general, the main alteration minerals are identified, but little is known about the accessory minerals. The Nili Fossae-Jezero system has potential analogues in terrestrial serpentinized and carbonated rocks, such as the Leka Ophiolite Complex, Norway (PTAL collection, https://www.ptal.eu). Here, distinct mineral assemblages record different stages of hydration and carbonation of ultramafic rocks [6]. We perform petrological and mineralogical analyses on thin sections to characterize the major and trace minerals and combine with Near Infrared (NIR) spectroscopy measurements. A set of spectral parameters are defined and compare to spectral parameters previously used on CRISM and OMEGA data [1, 4, 7, 8]. We study the significance of the mineralogical assemblages including nature of accessory minerals. Effect of the presence of accessory minerals on the NIR signal is investigated and their potential incidence on the amount of H2/CH4 production in mafic or ultramafic system is discussed [5]. We started to apply the newly defined spectral parameters on several SCS on Mars. Results confirm local carbonation of earlier serpentinized rocks and suggest that different protoliths could have led to diversity of mineralogical associations in SCS on Mars. Multiple detection of brucite are also suggested for the first time on Mars. Altogether our results help to better describe key geochemical conditions of the SCS on Mars for habitability potential of the martian crust and Mars&#8217;s evolution. &#160; References: <ul> <li>Brown, A. J., et al.&#160;EPSL1-2 (2010): 174-182.</li> <li>Wiens, R.C.,&#160;et al.&#160;&#160;Space Sci Rev217,&#160;4 (2021).</li> <li>Horgan, B., et al.&#160;Second International Mars Sample Return. Vol. 2071. 2018.</li> <li>Ody, A., et al.&#160;JGR: Planets2 (2013): 234-262.</li> <li>Klein, F., et al. Lithos178 (2013): 55-69.</li> <li>Bjerga, A., et al.&#160;Lithos227 (2015): 21-36.</li> <li>Viviano-Beck et al, JGR: Planets 118.9 (2013)</li> <li>Viviano-Beck et al, JGR: Planets 119.6&#160;(2014)</li> </ul>

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Using Visual Ozone Damage Scores and Spectroscopy to Quantify Soybean Responses to Background Ozone

Remote Sensing ◽

10.3390/rs12010093 ◽

2019 ◽

Vol 12 (1) ◽

pp. 93

Author(s):

Nichole Gosselin ◽

Vasit Sagan ◽

Matthew Maimaitiyiming ◽

Jack Fishman ◽

Kelley Belina ◽

...

Keyword(s):

Near Infrared ◽

Pearson Correlation ◽

Hyperspectral Data ◽

Coefficient Of Determination ◽

Biophysical Properties ◽

Yield Data ◽

Indicator Plants ◽

Snap Beans ◽

Visual Damage ◽

Ozone Stress

Remotely-sensed identification of ozone stress in crops can allow for selection of ozone resistant genotypes, improving yields. This is critical as population, food demand, and background tropospheric ozone are projected to increase over the next several decades. Visual scores of common ozone damage have been used to identify ozone-stress in bio-indicator plants. This paper evaluates the use of a visual scoring metric of ozone damage applied to soybeans. The scoring of the leaves is then combined with hyperspectral data to identify spectral indices specific to ozone damage. Two genotypes of soybean, Dwight and Pana, that have shown different sensitivities to ozone, were grown and visually scored for ozone-specific damage on multiple dates throughout the growing season. Leaf reflectance, foliar biophysical properties, and yield data were collected. Additionally, ozone bio-indicator plants, snap beans, and common milkweed, were investigated with visual scores and hyperspectral leaf data for comparison. The normalized difference spectral index (NDSI) was used to identify the significant bands in the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) that best correlated with visual damage score when used in the index. Results were then compared to multiple well-established indices. Indices were also evaluated for correlation with seed and pod weight. The ozone damage scoring metric for soybeans evaluated in August had a coefficient of determination of 0.60 with end-of-season pod weight and a Pearson correlation coefficient greater than 0.6 for photosynthetic rate, stomatal conductance, and transpiration. NDSI [R558, R563] correlated best with visual scores of ozone damage in soybeans when evaluating data from all observation dates. These wavelengths were similar to those identified as most sensitive to visual damage in August when used in NDSI (560 nm, 563 nm). NDSI [R560, R563] in August had the highest coefficient of determination for individual pod weight (R2 = 0.64) and seed weight (R2 = 0.54) when compared against 21 well-established indices used for identification of pigment or photosynthetic stress in plants. When evaluating use of spectral bands in NDSI, longer wavelengths in SWIR were identified as more sensitive to ozone visual damage. Trends in the bands and biophysical properties of the soybeans combined with evaluation of ozone data indicate likely timing of significant ozone damage as after late-July for this season. This work has implications for better spectral detection of ozone stress in crops and could help with efforts to identify ozone tolerant varieties to increase future yield.

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Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

Drones ◽

10.3390/drones4020025 ◽

2020 ◽

Vol 4 (2) ◽

pp. 25

Author(s):

Antoine Mury ◽

Antoine Collin ◽

Thomas Houet ◽

Emilien Alvarez-Vanhard ◽

Dorothée James

Keyword(s):

Salt Marsh ◽

Salt Marshes ◽

Crop Production ◽

Vegetation Index ◽

Near Infrared ◽

Wave Attenuation ◽

Coefficient Of Determination ◽

Coastal Protection ◽

Surface Model ◽

Red Edge

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm0) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R2): 0.85, root mean square error (RMSE): 0.20%/m).

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Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

Molecules ◽

10.3390/molecules24030428 ◽

2019 ◽

Vol 24 (3) ◽

pp. 428 ◽

Cited By ~ 12

Author(s):

Verena Wiedemair ◽

Dominik Langore ◽

Roman Garsleitner ◽

Klaus Dillinger ◽

Christian Huck

Keyword(s):

Data Analysis ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Analysis Software ◽

Infrared Spectrometer ◽

Automated Tools ◽

Near Infrared Spectrometer ◽

Data Analysis Software

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R2) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

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