scholarly journals Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum

2019 ◽  
Vol 12 (1) ◽  
pp. 95 ◽  
Author(s):  
Hongjun Li ◽  
Yuming Zhang ◽  
Yuping Lei ◽  
Vita Antoniuk ◽  
Chunsheng Hu
Keyword(s):  
Winter Wheat ◽  
Nitrate Nitrogen ◽  
Vegetation Indices ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Soil Nitrate ◽  
Nitrogen Status ◽  
Non Destructive ◽  
Estimation Models ◽  
Multispectral Camera

Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0–90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models.

Effect of nitrogen fertilizer rate on soil nitrate nitrogen content after harvesting winter wheat

1990 ◽  
Vol 114 (2) ◽  
pp. 171-176 ◽  
Author(s):  
K. Chaney
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Nitrate Nitrogen ◽  
Soil Horizon ◽  
Fertilizer Treatment ◽  
Soil Nitrate ◽  
Fertilizer Rate ◽  
Mineral N ◽  
Nitrate N ◽  
Nitrogen Fertilizer Rate

SUMMARYThe nitrate nitrogen content of the soil (0–90 cm) was measured immediately after the harvest of winter wheat at eight sites in central and eastern England in 1987 and 1988. On average, 50% of the total nitrate detected was in the 0–30 cm, 30% in the 30–60 cm and 20% in the 60–90 cm soil horizon. Although soil nitrate N increased with the amount of N fertilizer applied, it was not a linear relationship. There were small nonsignificant increases in soil nitrate up to the optimum fertilizer rate for yield but, once the optimum was reached, further addition of fertilizer increased nitrate contents significantly.Therefore, applying the correct quantity of N for high grain yield did not significantly increase soil nitrate residues after harvest compared with the no-fertilizer treatment. This emphasizes the importance of applying the appropriate rate of N for each crop, because applying too much is not only uneconomic but also significantly increases the amount of mineral N which could be subsequently leached over the winter.

scholarly journals Soil Nitrate Nitrogen Content and Grain Yields of Organically Grown Cereals as Affected by a Strip Tillage and Forage Legume Intercropping

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1453
Author(s):  
Aušra Arlauskienė ◽  
Viktorija Gecaitė ◽  
Monika Toleikienė ◽  
Lina Šarūnaitė ◽  
Žydrė Kadžiulienė
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Nitrate Nitrogen ◽  
Soil Nitrate ◽  
Forage Legume ◽  
Forage Legumes ◽  
Grain Yields ◽  
Strip Tillage ◽  
Perennial Legumes ◽  
Strip Intercropping

Reducing tillage intensity and increasing crop diversity by including perennial legumes is an agrotechnical practice that strongly affects the soil environment. Strip tillage may be beneficial in the forage legume–cereals intercropping system due to more efficient utilization of biological nitrogen. Field experiments were conducted on a clay loam Cambisol to determine the effect of forage legume–winter wheat strip tillage intercropping on soil nitrate nitrogen (N-NO3) content and cereal productivity in various sequences of rotation in organic production systems. Forage legumes (Medicago lupulina L., Trifolium repens L., T. alexandrinum L.) grown in pure and forage legume–winter wheat (Triticum aestivum L.) strip tillage intercrops were studied. Conventional deep inversion tillage was compared to strip tillage. Nitrogen supply to winter wheat was assessed by the change in soil nitrate nitrogen content (N-NO3) and total N accumulation in yield (grain and straw). Conventional tillage was found to significantly increase N-NO3 content while cultivating winter wheat after forage legumes in late autumn (0–30 cm layer), after growth resumption in spring (30–60 cm), and in autumn after harvesting (30–60 cm). Soil N-NO3 content did not differ significantly between winter wheat strip sown in perennial legumes or oat stubble. Winter wheat grain yields increased with increasing N-NO3 content in soil. The grain yield was not significantly different when comparing winter wheat–forage legume strip intercropping (without mulching) to strip sowing in oat stubble. In forage legume–winter wheat strip intercropping, N release from legumes was weak and did not meet wheat nitrogen requirements.

scholarly journals Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4155
Author(s):  
Haiyang Pang ◽  
Aiwu Zhang ◽  
Xiaoyan Kang ◽  
Nianpeng He ◽  
Gang Dong
Keyword(s):  
Aboveground Biomass ◽  
Inner Mongolia ◽  
Large Scale ◽  
Vegetation Indices ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Spectral Information ◽  
Spectral Features ◽  
Inner Mongolia Plateau ◽  
Sentinel 2

An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts further application in large-scale grassland monitoring. For this reason, a satellite-scale simulated spectra method was proposed to enhance the spectral information of the Sentinel-2 data, and a simulated spectrum (SS) was constructed using this algorithm. Then, the raw spectrum (RS) of Sentinel-2 and the SS were used as data sources to calculate the vegetation indices (RS-VIs and SS-VIs, which represent vegetation indices calculated using RS and SS data, respectively), and the multi-granularity spectral segmentation algorithm (MGSS) was employed to extract spectral segmentation features (RS-SF and SS-SF, which represent segmentation features extracted by RS and SS data, respectively). Following this, these spectral features (RS-SF, SS-SF, RS-VIs, and SS-VIs) were used to estimate AGB by partial least-squares regression (PLSR) and multiple stepwise regression (MSR) models. Finally, the spatial distribution law and the reasons for the latitude zone of the Inner Mongolia Plateau were analyzed, based on precipitation, the average temperature, topography, etc. The conclusions are as follows. Firstly, the SS has more spectral information and its sensitivity to biomass is higher than the RS of Sentinel-2 in some bands, and the correlation between the SS-VIs and biomass is higher than that of the RS-VIs. Secondly, among the spectral features, the most accurate AGB estimation was obtained by SS-SF, which gave R2 = 0.95. The root mean square error (RMSE) was 10.86 g/m2 and the estimate accuracy (EA) was 82.84% in the MSR model. Additionally, RMSE = 10.89 g/m2 and EA = 82.78% in the PLSR model. Compared with the traditional estimation methods using RS and VI, R2 was increased by at least 0.2, RMSE was reduced by at least 14.08 g/m2, and EA was increased by 22.26%. Therefore, the simulated spectra method can help improve the estimation accuracy of AGB, and a new idea about regional and global large-scale biomass acquisition is provided.

Characterization of winter wheat nitrogen status with vegetation indices under different availability of sulphur

2015 ◽  
pp. 59-66
Author(s):  
J. Groszyk ◽  
S. Samborski ◽  
D. Gozdowski ◽  
M. Stępień ◽  
E. Leszczyńska ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Nitrogen Status

scholarly journals Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images

2021 ◽  
Vol 25 (10) ◽  
pp. 689-695
Author(s):  
George D. Martins ◽  
Onésio F. da Silva Neto ◽  
Glecia J. dos S. Carmo ◽  
Renata Castoldi ◽  
Ludymilla C. S. Santos ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Parametric Models ◽  
Multispectral Images ◽  
Infrared Band ◽  
Physiological Variable ◽  
Minas Gerais State ◽  
Increase Productivity ◽  
Estimation Models ◽  
Multispectral Camera

ABSTRACT The formation of seedlings is one of the most important phases of lettuce cultivation. Therefore, any strategy that aims to obtain high-quality seedlings can increase productivity. One of these strategies is the prediction of morphophysiological attributes based on optical properties. The objective of this study was to quantitatively estimate the biometric variables of lettuce from parametric and non-parametric models based on the response of multispectral camera images. The experiment was conducted in a greenhouse in the municipality of Uberaba, Minas Gerais State, Brazil. Twenty days after sowing, multispectral images of the plants were captured using a MAPIR Survey 3 camera. To compose the estimation models, along with the original bands of the camera, the multispectral vegetation indices were calculated using the calibrated original camera bands. Bands B550, B660, and B850 and the near-infrared indices contributed significantly to estimating the physiological variable models, with B850 contributing the most to the biometric and nutritional variables. From the near-infrared band (B850) and derived indices, it was possible to estimate all the agronomic variables from the models generated by the M5 algorithm, with an accuracy of up to 1.6% for the maximum quantum yield. Thus, it is possible to quantify the biometric, physiological, and nutritional variables of lettuce using a multispectral camera. Among the Mapir camera bands, B660 exhibited the greatest variability, showing that the red range was the most sensitive.

scholarly journals Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroto Yamashita ◽  
Rei Sonobe ◽  
Yuhei Hirono ◽  
Akio Morita ◽  
Takashi Ikka
Keyword(s):  
Short Wave ◽  
Machine Learning Algorithms ◽  
Estimation Methods ◽  
Chemical Information ◽  
Tea Quality ◽  
The Mean ◽  
Processing Techniques ◽  
Spectral Patterns ◽  
Physical And Chemical ◽  
Non Destructive

AbstractSpectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

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