scholarly journals Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 957 ◽  
Author(s):  
Hengbiao Zheng ◽  
Jifeng Ma ◽  
Meng Zhou ◽  
Dong Li ◽  
Xia Yao ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Spectral Information ◽  
Growth Stages ◽  
Nitrogen Accumulation ◽  
Multispectral Imagery ◽  
Plant Nitrogen ◽  
N Nutrition ◽  
Aerial Vehicle

This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

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.

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

INFLUENCE OF SINGLE STRAINS AND A COMMERCIAL MIXTURE OF Bradyrhizobium japonicum ON GROWTH, NITROGEN ACCUMULATION AND NODULATION OF TWO EARLY-MATURING SOYBEAN CULTIVARS

1988 ◽  
Vol 68 (2) ◽  
pp. 411-418 ◽  
Author(s):  
L. D. BAILEY
Keyword(s):  
Glycine Max ◽  
Soil Temperature ◽  
Bradyrhizobium Japonicum ◽  
Nodule Formation ◽  
Growth Stages ◽  
Nitrogen Accumulation ◽  
Plant Nitrogen ◽  
Commercial Mixture ◽  
Early Maturing ◽  
Glycine Max L

Seven single strains and a commercial mixture of Bradyrhizobium japonicum were evaluated in association with two early-maturing Canadian soybean (Glycine max (L.) Merrill) cultivars, Maple Presto and Maple Amber. Inoculated and uninoculated plants were grown in pails outdoors. Soil temperature at 15 cm depth was monitored throughout the experiment. At the V2, V3, R2 and R4 growth stages, whole plants were removed from the pails. Nodules were counted and weighed; roots and tops were separated, weighed and analyzed for total nitrogen. Bradyrhizobium japonicum strains 61A148, 61A196, 61A194 and 61A155 were similar in effectiveness, but superior to strains 61A124a, 61A118b, 61A101c and the commercial mixture in earliness of nodule formation, number and weight of nodules per plant, and in promoting greater root and top growth and plant nitrogen accumulation. There were indications that soil temperature may have affected nodulation. Maple Amber showed the greater potential for symbiotic nitrogen fixation. This cultivar supported earlier nodulation, had a greater number of nodules, accumulated more nitrogen in the tops and roots and had greater growth than Maple Presto.Key words: Soybean, Glycine max (L.) Merrill, soil temperature, soybean growth stages, Bradyrhizobium, nodulation

scholarly journals Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

2020 ◽  
Vol 12 (22) ◽  
pp. 3684
Author(s):  
Jie Jiang ◽  
Zeyu Zhang ◽  
Qiang Cao ◽  
Yan Liang ◽  
Brian Krienke ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Dry Matter ◽  
Vegetation Index ◽  
Nutrition Status ◽  
Growth Stages ◽  
Growth Indices ◽  
N Accumulation ◽  
N Nutrition ◽  
Red Edge ◽  
Aerial Vehicle

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

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