Estimation of litchi ( Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra

2016 ◽  
Vol 80 ◽  
pp. 182-194 ◽  
Author(s):  
Dan Li ◽  
Congyang Wang ◽  
Wei Liu ◽  
Zhiping Peng ◽  
Siyu Huang ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Growth Stages ◽  
Canopy Reflectance ◽  
Leaf Nitrogen Content ◽  
Litchi Chinensis ◽  
Different Growth Stages

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.

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

scholarly journals Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice

2007 ◽  
Vol 10 (4) ◽  
pp. 400-411 ◽  
Author(s):  
Yan Zhu ◽  
Yongchao Tian ◽  
Xia Yao ◽  
Xiaojun Liu ◽  
Weixing Cao
Keyword(s):  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Canopy Reflectance ◽  
Nitrogen Concentrations

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

2006 ◽  
Vol 86 (4) ◽  
pp. 1037-1046 ◽  
Author(s):  
Yan Zhu ◽  
Yingxue Li ◽  
Wei Feng ◽  
Yongchao Tian ◽  
Xia Yao ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Nutrition Status ◽  
Nitrogen Accumulation ◽  
Canopy Reflectance ◽  
Leaf Nitrogen Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

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