scholarly journals Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3712 ◽  
Author(s):  
Lukas Prey ◽  
Urs Schmidhalter
Keyword(s):  
Winter Wheat ◽  
N Uptake ◽  
Vegetation Indices ◽  
N Fertilization ◽  
Yield Potential ◽  
Growth Stages ◽  
Red Edge ◽  
Wide Range ◽  
N Use ◽  
N Status

Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat (Triticum aestivum L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years’ data with nine measurement dates from early stem elongation until flowering in eight N regimes (0–420 kg N ha−1) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R2 = 0.47, p < 0.001) and no index was useful for NC across dates, NNI and Nup could be better estimated (REIP: R2 = 0.59, p < 0.001 for both traits). Tested within growth stages across N levels, the order of the estimation of the traits was mostly Nup ≈ NNI > NC ≈ DM. Depending on the number (n = 1–3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high–excessive N fertilization, the range of the target traits was divided into two intervals with NNI values < 0.8 (interval 1: low N status) and with NNI values > 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R2 = 0.35, RMSE = 567 kg ha−1; NC: REIP: average R2 = 0.40, RMSE = 0.25%; NNI: REIP: average R2 = 0.46, RMSE = 0.10; Nup: REIP: average R2 = 0.48, RMSE = 21 kg N ha−1). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming.

scholarly journals Evaluating Two Crop Circle Active Canopy Sensors for In-Season Diagnosis of Winter Wheat Nitrogen Status

Agronomy ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 201 ◽  
Author(s):  
Qiang Cao ◽  
Yuxin Miao ◽  
Jianning Shen ◽  
Fei Yuan ◽  
Shanshan Cheng ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Triticum Aestivum L ◽  
Coefficient Of Determination ◽  
Growth Stages ◽  
Canopy Reflectance ◽  
Red Edge ◽  
N Management ◽  
N Status

Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop Circle ACS-430 (CC-430). The objective of this study was to evaluate the performances of CC-470 and CC-430 sensors for estimating winter wheat (Triticum aestivum L.) N status at different measurement heights (40 cm, 70 cm and 100 cm) and growth stages. Results indicated that the canopy reflectance values of CC-470 were more affected by height compared to the CC-430 sensor. The normalized difference red edge (NDRE) and red edge chlorophyll index (CIRE) of CC-430 were stable at the three different measuring heights. The relationships between these indices and the N status indicators were stronger at the Feekes 9–10 stages than the Feekes 6–7 stages for both sensors; however, the CC-430 sensor-based vegetation indices had higher coefficient of determination (R2) values for both stages. It is concluded that the CC-430 sensor is more reliable than CC-470 for winter wheat N status estimation due to its capability of making height-independent measurements. These results demonstrated the importance of considering the influences of height when using active canopy sensors in field measurements.

scholarly journals Temporal and Organ-specific Responses in NUE Traits to N Fertilization, Fungicide Intensity and Early Sowing in Winter Wheat Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 313 ◽  
Author(s):  
Lukas Prey ◽  
Moritz Germer ◽  
Urs Schmidhalter
Keyword(s):  
Winter Wheat ◽  
Harvest Index ◽  
N Uptake ◽  
N Fertilization ◽  
N Use Efficiency ◽  
Control Treatment ◽  
Total N ◽  
Organ Specific ◽  
Use Efficiency ◽  
N Use

Fungicide intensity and sowing time influence the N use efficiency (NUE) of winter wheat but the underlying mechanisms, interactions of plant traits, and the temporal effects are not sufficiently understood. Therefore, organ-specific responses in NUE traits to fungicide intensity and earlier sowing were compared at two nitrogen (N) levels for six winter wheat cultivars in 2017. Plants were sampled at anthesis and at maturity and separated into chaff, grain, culms, and three leaf layers to assess their temporal contribution to aboveground dry matter (DM) and N uptake (Nup). Compared to the control treatment, across cultivars, the treatment without fungicide mostly exerted stronger and inverse effects than early sowing, on grain yield (GY, −12% without fungicide, +8% n.s. for early sowing), grain Nup (GNup, −9% n.s., +5% n.s.) as well as on grain N concentration (+4%, −2% n.s.). Grain yield in the treatment without fungicide was associated with similar total DM, as observed in the control treatment but with lower values in harvest index, thousand kernel weight, N use efficiency for GY (NUE) and N utilization efficiency. Lower GNup was associated with similar vegetative N uptake but lower values in N translocation efficiency and N harvest index. In contrast, early sowing tended to increase total DM at anthesis and maturity as well as post-anthesis assimilation, at similar harvest index and increased the number of grains per spike and total N use efficiency. Total N uptake increased after the winter season but was similar at anthesis. Although the relative N response in many traits was lower without fungicide, few fungicide x interactions were significant, and the sowing date did not interact either with N fertilization for any of the N and DM traits. The results demonstrate the positive effects of fungicides and earlier sowing on various traits related to yield formation and the efficient use of nitrogen and are discussed based on various concepts.

Yield, N uptake, and apparent N-use efficiency of winter wheat and winter barley grown in different cropping systems

1998 ◽  
Vol 131 (4) ◽  
pp. 375-387 ◽  
Author(s):  
K. SIELING ◽  
H. SCHRÖDER ◽  
M. FINCK ◽  
H. HANUS
Keyword(s):  
Winter Wheat ◽  
N Uptake ◽  
Wheat Yield ◽  
Mineral Fertilizer ◽  
N Fertilization ◽  
N Use Efficiency ◽  
Fertilizer N ◽  
Mineral N ◽  
Use Efficiency ◽  
N Use

Increasing the efficiency with which crops use supplied nitrogen (N) can minimize the impact on the environment. In the growing seasons 1990/91 to 1992/93, the effects of different cropping systems on yield, N uptake by the grain and apparent N-use efficiency (NUE) of the grain of winter wheat and winter barley were investigated in a factorial field experiment at Hohenschulen Experimental Station near Kiel in NW Germany. The crop rotation was oilseed rape–winter wheat–winter barley, and soil tillage (conservation tillage without ploughing, conventional tillage), application of pig slurry (none, autumn, spring, autumn+spring), mineral N fertilization (0–240 kg N ha−1) and application of fungicides (none, applications against pathogens of the stems, leaves and ears) were all varied. Each year, the treatments were applied to all three crops of the rotation and were located on the same plots.Averaged over all factors, wheat yield was >7 t ha−1 dry matter in all years and N uptake of the harvested grain varied between 140 and 168 kg N ha−1. Pig slurry application in autumn increased grain yield and N uptake more than spring slurry in two out of three years. Mineral N unfertilized wheat yielded only 5·3–6·3 t ha−1 depending on the year, mineral N fertilization increased wheat yield up to 8 t ha−1. Barley yield was lower than wheat yield, ranging from 4·5 t ha−1 in 1993 to 6·3 t ha−1 in 1992. Unlike wheat, spring slurry N affected barley yield and N uptake more than autumn slurry.Wheat apparently utilized 12–21% and barley up to 13% of the applied slurry N for its grain development. In 1991, the highest apparent slurry N-use efficiency (SNUE) of wheat and barley occurred after the late spring slurry application. However, in the following years, autumn SNUE of wheat was similar to (1992) or higher than (1993) spring SNUE, presumably because of vigorous tiller growth before winter. Additionally applied mineral fertilizer N decreased SNUE.Apparent mineral fertilizer N-use efficiency (FNUE) was higher than SNUE and ranged in wheat from 40 to 59% and in barley between 19 and 37% of the applied mineral fertilizer N. FNUE decreased with increasing N fertilization.To improve the N-use efficiency of both slurry N and mineral fertilizer N, more information is needed about the combined use of both N sources, with special emphasis on split applications of slurry as is common practice for mineral N fertilizer.

scholarly journals Improving Nitrogen Status Estimation in Malting Barley Based on Hyperspectral Reflectance and Artificial Neural Networks

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2592
Author(s):  
Karel Klem ◽  
Jan Křen ◽  
Ján Šimor ◽  
Daniel Kováč ◽  
Petr Holub ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
N Uptake ◽  
Vegetation Indices ◽  
Malting Barley ◽  
N Nutrition ◽  
Red Edge ◽  
Artificial Neural ◽  
N Status ◽  
N Nutrition Index

Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.

scholarly journals Temporal and Spectral Optimization of Vegetation Indices for Estimating Grain Nitrogen Uptake and Late-Seasonal Nitrogen Traits in Wheat

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4640 ◽  
Author(s):  
Lukas Prey ◽  
Urs Schmidhalter
Keyword(s):  
Near Infrared ◽  
N Uptake ◽  
Vegetation Indices ◽  
Triticum Aestivum L ◽  
N Fertilization ◽  
Cultivar Differences ◽  
Red Edge ◽  
N Concentration ◽  
Organ Level ◽  
Grain Nitrogen

Grain nitrogen (N) uptake (GNup) in winter wheat (Triticum aestivum L.) is influenced by multiple components at the plant organ level and by pre- and post-flowering N uptake (Nup). Although spectral proximal high-throughput sensing is promising for field phenotyping, it was rarely evaluated for such N traits. Hence, 48 spectral vegetation indices (SVIs) were evaluated on 10 measurement days for the estimation of 34 N traits in four data subsets, representing the variation generated by six high-yielding cultivars, two N fertilization levels (N), two sowing dates (SD), and two fungicide (F) intensities. Close linear relationships (p < 0.001) were found for GNup both in response to cultivar differences (Cv; R2 = 0.52) and other agronomic treatments (R2 = 0.67 for Cv*F*N, R2 = 0.53 for Cv*SD*N and R2 = 0.57 for the combined treatments), notably during milk ripeness. Especially near-infrared (NIR)/red edge SVIs, such as the NDRE_770_750, outperformed NIR/visible light (VIS) indices. Index rankings and seasonal R2 values were similar for total Nup, while the N harvest index, which expresses the partitioning to the grain, was moderately estimated only during dough ripeness, primarily from indices detecting contrasting senescence between different fungicide intensities. Senescence-sensitive indices, including R787_R765 and TRCARI_OSAVI, performed best for N translocation efficiency and some organ-level N traits at maturity. Even though grain N concentration was best assessed by the red edge inflection point (REIP), the blue/green index (BGI) was more suited for leaf-level N traits at anthesis. When SVIs were quantitatively ranked by data subsets, a better agreement was found for GNup, total Nup, and grain N concentration than for several contributing N traits. The results suggest (i) a good general potential for estimating GNup and total Nup by (ii) red edge indices best used (iii) during milk and early dough ripeness. The estimation of contributing N traits differs according to the agronomic treatment.

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.

scholarly journals Use of Landsat images for yield evaluation within a small plot

2014 ◽  
Vol 60 (No. 11) ◽  
pp. 501-506 ◽  
Author(s):  
J. Kumhálová ◽  
F. Zemek ◽  
P. Novák ◽  
O. Brovkina ◽  
M. Mayerová
Keyword(s):  
Winter Wheat ◽  
Water Distribution ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Moisture Stress ◽  
Stress Index ◽  
Growth Stages ◽  
Landsat Images ◽  
Topographic Attributes ◽  
Small Plot

Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was &ndash;0.816 in the image acquisition date of 4. 6. 2011.

scholarly journals Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn

2020 ◽  
Vol 12 (13) ◽  
pp. 2071
Author(s):  
Hwang Lee ◽  
Jinfei Wang ◽  
Brigitte Leblon
Keyword(s):  
Linear Regression ◽  
Unmanned Aerial Vehicle ◽  
Random Forests ◽  
Vegetation Indices ◽  
N Fertilization ◽  
Yield Potential ◽  
Support Vector ◽  
Aerial Vehicle ◽  
Marginal Improvement ◽  
Canopy Nitrogen

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by the farmer. The drawbacks of N deficiency on crops include poor plant growth, ultimately reducing the final yield potential. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery to predict canopy nitrogen weight (g/m2) of corn fields in south-west Ontario, Canada. Simple/multiple linear regression, Random Forests, and support vector regression (SVR) were established to predict the canopy nitrogen weight from individual multispectral bands and associated vegetation indices (VI). Random Forests using the current techniques/methodologies performed the best out of all the models tested on the validation set with an R2 of 0.85 and Root Mean Square Error (RMSE) of 4.52 g/m2. Adding more spectral variables into the model provided a marginal improvement in the accuracy, while extending the overall processing time. Random Forests provided marginally better results than SVR, but the concepts and analysis are much easier to interpret on Random Forests. Both machine learning models provided a much better accuracy than linear regression. The best model was then applied to the UAV images acquired at different dates for producing maps that show the spatial variation of canopy nitrogen weight within each field at that date.

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