Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat

Tan, Changyin; Du, Yanjun; Zhou, Jian; Wang, Dunliang; Luo, Min; Zhang, Yongjian; Guo, Wenshan

doi:10.3389/fpls.2018.00674

Cited by 37 publications

(17 citation statements)

References 40 publications

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“…Some authors have reported the close relationship among the indices with chlorophyll content [9]. In conjunction with a sampling field design, and a follow-up growing season practices program, they are a reliable alternative to estimate Nt to reduce cost and time in decision-making [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

et al. 2020

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Knowing the total Nitrogen content (Nt) of forage maize (Zea mays) is important so that decisions can be made quickly and efficiently to adjust the timing and amount of both irrigation and fertilizer. In 2017 and 2018 during three growing cycles in two study plots, leaf samples were collected and the Dumas method was used to estimate Nt. During the same growing seasons and on the same sampling plots, a Parrot Sequoia camera mounted on an unmanned aerial vehicle (UAV) was used to collect high resolution images of forage maize study plots. Thirteen multispectral indices were generated and, from these, a Random Forest (RF) algorithm was used to estimate Nt. RF is a machine-learning technique and is designed to work with extremely large datasets. Overall analysis showed five of the 13 indices as the most important. One of these five, the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index, was found to be the most important for estimation of Nt in forage maize (R2 = 0.76). RF handled the complex dataset in a time-efficient manner and Nt did not differ significantly when compared between traditional methods of evaluating Nt at the canopy level and using UAVs and RF to estimate Nt in forage maize. This result is an opportunity to explore many new research options in precision farming and digital agriculture.

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Section: Introductionmentioning

confidence: 99%

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

et al. 2020

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“…Multivariate regression is a method used to measure the degree of linear relationship between multiple independent variables (predictors) and dependent variables (responses). While many different multivariate regression algorithms exist, PLSR has proven to be most robust for N measurement using high-dimensional hyperspectral data [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. In a preliminary analysis, we tested several well-accepted multivariate regression algorithms which have previously been used for N measurement in plants, including support vector machine regression [ 16 ] and stepwise regression [ 15 ], and found that PLSR achieved equal or better results.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, for comparison, some well-known vegetation indices (VIs) previously used to measure the N content of plants were tested using the spectrometer data. Tan, et al [ 10 ] compared 18 VIs using hyperspectral data and found that the normalised red edge area index (NREAI) proposed by Gong, et al [ 25 ] had the best correlation with the leaf N accumulation (LNA) of wheat. NREAI has the form of ( SD r − SD b )/( SD r + SD b ), where SD b is the integration of the first-order derivative values in the blue-edge covering 490 nm to 530 nm and SD r is the integration of the those in the red-edge from 670 nm to 737 nm.…”

Section: Methodsmentioning

confidence: 99%

“…For each measurement, the output is a vector, usually called a spectral signature, recording the average value of intensity, reflectance or transmittance in the field of view (FOV) of the lens. Different spectrometers have been investigated to measure leaf N content in wheat [ 8 , 9 , 10 ], maize [ 11 ] and tobacco [ 12 ]. The fast-developing area of hyperspectral imaging integrates imaging and spectroscopy technologies and provides both visual representations of targets as well as the corresponding spectral information.…”

Section: Introductionmentioning

confidence: 99%

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The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Liu

Bruning

Garnett

et al. 2020

Sensors

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The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

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“…It has great application potential in large-scale crop growth monitoring, crop yield estimation, agricultural monitoring and forecasting, and agricultural resources survey [14][15][16] . In China and abroad, considerable work has been done on remote sensing estimation of crops, and great progress has been made 17,18 . Based on moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) data, a global agricultural monitoring system for crop monitoring and yield forecasting was built 19 .…”

mentioning

confidence: 99%

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Zhang

Zhou

Wang

et al. 2020

Sci Rep

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Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model's coefficients of determination (R 2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha −1 and 786.5 kg ha −1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion. Scientifically and accurately estimating crop yield is of significant importance for formulating plans for social and economic development, determining agricultural products import and export plans, ensuring national food security, guiding and regulating macroscopic planting structure, as well as improving the management skills of relevant agriculture-related enterprises and farmers 1-6. With the improvement of spatial, temporal and spectral resolutions of remote sensing data and the significant reduction of cost, currently remote sensing has been widely used in the estimation of production of all kinds of food crops, and it has become a research focus in the interdisciplinary field combining remote sensing and agriculture 7. At present, there were many methods and means for estimating crop yield, such as crop yield meteorological forecast, artificial sampling survey, statistical simulation model, remote sensing estimation and so on 8,9. Using a Criteria/Wofost simulation model that included the new numerical scheme for soil water balance, some researchers compared field data collected at the university of bologna's experimental farm in 1977-1987 with the median wheat yield, and the predicted value was consistent with the observed value 10. Other researches have suggested that the mars-crop ...

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Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat

Cited by 37 publications

References 40 publications

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

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