Hyperspectral characteristic analysis for leaf nitrogen content in different growth stages of winter wheat

2016 ◽  
Vol 55 (34) ◽  
pp. D151 ◽  
Author(s):  
Liu Haiying ◽  
Zhu Hongchun
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
Leaf Nitrogen Content ◽  
Characteristic Analysis ◽  
Different Growth Stages

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.

scholarly journals Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

2021 ◽  
Vol 13 (3) ◽  
pp. 340
Author(s):  
Xingang Xu ◽  
Lingling Fan ◽  
Zhenhai Li ◽  
Yang Meng ◽  
Haikuan Feng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Nitrogen Content ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
Leaf Nitrogen Content ◽  
Multispectral Images ◽  
Mean Square ◽  
Rgb Images

With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI =VI/1+FVcover, where VI denotes the reference vegetation index and FVcover refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and FVcover was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best R2 value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.

PREDIKSI KANDUNGAN NITROGEN DAUN PADI DENGAN ANALISIS PERGESERAN TEPI KANAL MERAH (RED EDGE SHIFT) DATA HIPERSPEKTRAL

2013 ◽  
Vol 11 (3) ◽  
Author(s):  
Nadirah Nadirah ◽  
Bangun Muljosukojo ◽  
Teguh Hariyanto ◽  
M Sadly ◽  
M Evri ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Biochemical Parameter ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
Similar Response ◽  
Leaf Nitrogen Content ◽  
N Content ◽  
N Level ◽  
Red Edge ◽  
Leaf N

Canopy hyperspectral with various growth stages measured by using field spectroradiometer (350 - 1000 nm) corresponded to leaf Nitrogen content of three rice cultivars (Ciherang, Cilamaya and IR64) during growth season in Java Island,Indonesia. Coinciding with hyperspectral measurement, biochemical parameter such as leaf Nitrogen content (g/100 gr) was analyzed from destructive biomass sample through laboratory analysis. The potential narrow band in the red edgeregion was investigated to predict leaf nitrogen content (N content) with applying modified polynomial interpolation (MPI) and modified four points linear interpolation (MFLI) methods. First derivative reflectance derived from reflectance data andsubsequently used in analysis of Red Edge Position (REP). The correlation REPMFLI was generally stronger than REP-MPI attributed to leaf N content for several level of N application that indicated by value of R2. The response of REP-MFLItoward N level 69 kg/ha exhibited the most significant correlation (R2 = 0.754) than other correlations. Meanwhile, the response of REP-MPI toward N level 161 kg/ha denoted the most significant correlation (R2 = 0.8) than other correlations. The highest correlation using REP-MPI (R2 = 0.8) to predict leaf N contentdemonstrated slightly higher than that of REP- MFLI (R2 = 0.754). In general both REP-MFLI and REP-MPI represented somewhat similar response toward N levels, such as 103.5 kg/h, 115 kg/ha. The exploration of characteristics of red edge shiftis a fundamental point in developing rapid and precise prediction for biochemical parameter. In addition, its prediction capability was promising to support crop farming management.

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
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