Predicting Winter Wheat Grain Quality Using Hyperspectral Data Based on Plant Nitrogen Status

2012 ◽  
Vol 524-527 ◽  
pp. 2132-2138 ◽  
Author(s):  
Hui Fang Wang ◽  
Ji Hua Wang ◽  
Mei Chen Feng ◽  
Qian Wang ◽  
Wen Jiang Huang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Grain Quality ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content ◽  
Wheat Grain ◽  
Quality Model ◽  
Plant Nitrogen

Quality of winter wheat from hyperspectral data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, two varieties winter wheat as the object, hyperspectral data were utilized to predict grain quality. Firstly, the leaf and stem nitrogen content at winter wheat anthesis stage was proved to be signification correctly with crude content and wet gluten. And the leaf relatedcoefficient more than stem at the anthesis. Then, spectral indices significantly correlated to plant nitrogen content at anthesis stage were potential indicators for grain qualities. The vegetation index, VI derived from the canopy spectral reflectance was signification correlated to the leaf nitrogen content at anthesis stage, and highly significantly correlated to the leaf nitrogen content. Based on above analysis, the predict grain quality model were build and the related coefficient were 0.86, 0.68, 0.84, 0.58 which were reached a very significant.The result demonstrated the model based on SIPI and RVI to predict different cultivars wheat grain quality were practical and feasible.

Monitoring of leaf nitrogen content of winter wheat using multi-angle hyperspectral data

2021 ◽  
Vol 42 (12) ◽  
pp. 4676-4696
Author(s):  
Tiansheng Li ◽  
Zhen Zhu ◽  
Jing Cui ◽  
Jianhua Chen ◽  
Xiaoyan Shi ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content

scholarly journals The use of NDVI to determine the nitrogen content in winter wheat plants in the Stavropol Territory

2019 ◽  
Vol 191 (12) ◽  
pp. 19-30
Author(s):  
I. STORCHAK ◽  
I. V. Chernova ◽  
F. Eroshenko ◽  
Tatiana Voloshenkova ◽  
Elena Shestakova
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Grain Quality ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Nitrogen Nutrition ◽  
Inverse Correlation ◽  
Research Centre ◽  
Federal State

Abstract. Lack of nitrogen leads to a decrease in yield and grain quality in winter wheat plants. Therefore, it is necessary to monitor nitrogen nutrition throughout the period of growth and development of plants, which will quickly assess the need for fertilizing to obtain high yields of quality grain. Therefore, the aim of the study was to establish the possibility of using the normalized difference vegetation index (NDVI) to control the nitrogen content in winter wheat plants in the Stavropol territory. Methods. The work was performed in federal state budgetary scientific institution “North-Caucasian Federal Agricultural Research Centre” at the production of winter crops. Selection of plant samples (sheaf material) was carried out according to the generally accepted method. Repeated – 4x. Determination of the chemical composition of plant organs was carried out by the method of V. T. Kurkaev with co-authors, and the content of chlorophyll – Milaeva and Primak. Results. Since the quality of winter wheat grain directly depends on the nitrogen supply of plants, the relationships between the nitrogen content in winter wheat plants and the values of the vegetation index NDVI were studied. High correlation coefficients between these indicators are obtained. Thus, the average of Rcorr fields.in 2012 it was equal to –0.89, and in 2013 and 2014 –0.82. In addition, due to the dependence of nitrogen content on the amount of chlorophyll, it was possible to analyze the correlation between these indicators and NDVI fields, which showed that a stable relationship (inverse) is observed in the case of the amount of chlorophyll per unit biomass (mg/g), which is estimated on average at –0.79. The interrelation between grain quality and earth remote sensing data is revealed. It is most clearly seen in the case of the maximum and average NDVI for the period from the resumption of spring vegetation to full ripeness of winter wheat. Scientific novelty. For the first time in the conditions of unstable humidification of the Stavropol territory, a high inverse correlation between the vegetation index NDVI and the nitrogen content in winter wheat plants was determined, which on average is estimated by the correlation coefficient equal to –0.84.

scholarly journals Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat

2016 ◽  
Vol 17 (6) ◽  
pp. 721-736 ◽  
Author(s):  
Xiao Song ◽  
Duanyang Xu ◽  
Li He ◽  
Wei Feng ◽  
Yonghua Wang ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content

scholarly journals Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

2021 ◽  
Vol 13 (19) ◽  
pp. 3991
Author(s):  
Raquel Peron-Danaher ◽  
Blake Russell ◽  
Lorenzo Cotrozzi ◽  
Mohsen Mohammadi ◽  
John Couture
Keyword(s):  
Winter Wheat ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Plant Nitrogen ◽  
Nitrogen Levels ◽  
Nitrogen Concentrations ◽  
Use Efficiency ◽  
Canopy Nitrogen ◽  
Wheat Lines

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

scholarly journals Leaf nitrogen content estimation using top-of-canopy airborne hyperspectral data

2021 ◽  
Vol 104 ◽  
pp. 102584
Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Rohit Pingale ◽  
Balaji Naik Banoth ◽  
Adinarayana Jagarlapudi
Keyword(s):  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content

scholarly journals Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 1940 ◽  
Author(s):  
Liang Liang ◽  
Liping Di ◽  
Ting Huang ◽  
Jiahui Wang ◽  
Li Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Leaf Nitrogen ◽  
Support Vector ◽  
Leaf Nitrogen Content ◽  
Random Forest Regression ◽  
First Derivative ◽  
Remote Sensing Mapping

Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.

scholarly journals Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2956
Author(s):  
Li Wang ◽  
Shuisen Chen ◽  
Dan Li ◽  
Chongyang Wang ◽  
Hao Jiang ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Regression Models ◽  
Growth Stage ◽  
Vegetation Index ◽  
Leaf Nitrogen ◽  
Paddy Rice ◽  
Growth Stages ◽  
Nitrogen Accumulation ◽  
Plant Nitrogen ◽  
Stage Models

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

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