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 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.

Sensitivity of sensor-based nitrogen rates to selection of within-field calibration strips in winter wheat

2017 ◽  
Vol 68 (2) ◽  
pp. 101 ◽  
Author(s):  
Stanisław M. Samborski ◽  
Dariusz Gozdowski ◽  
Olga S. Walsh ◽  
Peter Kyveryga ◽  
Michał Stłpieł
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Representative Sample ◽  
Stem Elongation ◽  
Triticum Aestivum L ◽  
Canopy Reflectance ◽  
Sample Strip ◽  
Vegetative Indices ◽  
N Status ◽  
Selection Of

Active optical sensors (AOSs) are used for in-season variable-rate application of nitrogen (N). The sensors measure crop reflectance expressed as vegetative indices (VIs). These are transformed into N recommendations during on-site calibration of AOSs—‘familiarising’ the sensors with the crop N status of the representative part of a field. The ‘drive-first’ method is often used by growers to calibrate AOSs. Due to large spatial variation of crop N status within fields, it is difficult to identify the most representative sample strip for AOS calibration. Seven site-years were used to evaluate the sensitivity of sensor-based N prescriptions for winter wheat (Triticum aestivum L.) to selection of sample strips for AOS calibration that fall into extreme, very low or very high values of 95th percentiles of amber normalised difference VI (NDVI) values. A Crop Circle ACS-210 sensor was used to collect canopy reflectance values, expressed as amber NDVI, at the beginning of wheat stem elongation. Our study showed that the sample-strip selection significantly affected sensor-based N prescriptions. The drive-first method may result in under- or over-applications of N and in lower N-use efficiency. One way to overcome this problem is to collect whole field NDVI values during pesticide application before sensor-based N application. The NDVI values from the entire field then can be used to choose the most representative sample strips for AOS calibration.

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.

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

2010 ◽  
Vol 11 (4) ◽  
pp. 335-357 ◽  
Author(s):  
Fei Li ◽  
Yuxin Miao ◽  
Simon D. Hennig ◽  
Martin L. Gnyp ◽  
Xinping Chen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Different Growth Stages

scholarly journals Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5579
Author(s):  
Jie Jiang ◽  
Cuicun Wang ◽  
Hui Wang ◽  
Zhaopeng Fu ◽  
Qiang Cao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Accurate Estimation ◽  
Growth Stages ◽  
N Accumulation ◽  
N Nutrition ◽  
N Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.

scholarly journals Variation Characteristics in Growth Stages of Winter Wheat (Triticum Aestivum L.) And Prediction Model

2021 ◽  
pp. 737-746
Author(s):  
Weili Wang ◽  
Xuhui Zhang ◽  
Zhaotang Shang
Keyword(s):  
Climate Change ◽  
Triticum Aestivum ◽  
Winter Wheat ◽  
Prediction Model ◽  
Late Stage ◽  
Growth Stage ◽  
Triticum Aestivum L ◽  
Meteorological Conditions ◽  
Growth Stages ◽  
Variation Characteristics

The variation characteristics of growth stages of winter wheat (Triticum aestivum L.) with the climate change were measured by designing its stability and prediction model. Results showed the trend of stability of growth stage of winter wheat in Jiangsu province of China was an S-shaped curve indicating the growth of winter wheat was more stable in late stage. The lengths of early and late stages of growth were in inverse proportion. Specifically, when the early stage was prolonged, the late stage was shortened, which ensured the relative stability of the length of growth stage. The length of growth stage was correlated with the meteorological conditions. Thus, favorable meteorological conditions contributed to the stability of growth stages of winter wheat. Along with the climate change, the basic statistical characteristics of growth stage remained stable. Each stage drifted moderately under the variation of meteorological conditions, typically during the stage of vegetative growth. The growth process can be regulated by means of variety improvement, adjustment of sowing time and density, reasonable fertilization, and the use of growth regulators. These measures are able to counteract the influences of climate change on winter wheat production and ensure the production security. Bangladesh J. Bot. 50(3): 737-746, 2021 (September) Special

scholarly journals Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1296 ◽  
Author(s):  
Huilin Tao ◽  
Haikuan Feng ◽  
Liangji Xu ◽  
Mengke Miao ◽  
Huiling Long ◽  
...  
Keyword(s):  
Growth Parameters ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Growth Stages ◽  
Complex Data ◽  
Least Squares Regression ◽  
Area Index ◽  
Red Edge ◽  
Highly Correlated

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

Sign in / Sign up

Export Citation Format

Share Document