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 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 Estimating the Growth Indices and Nitrogen Status Based on Color Digital Image Analysis During Early Growth Period of Winter Wheat

2021 ◽  
Vol 12 ◽  
Author(s):  
Ben Zhao ◽  
Yonghui Zhang ◽  
Aiwang Duan ◽  
Zhandong Liu ◽  
Junfu Xiao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Growth Period ◽  
Early Growth ◽  
Individual Growth ◽  
Growth Stages ◽  
Growth Indices ◽  
Indirect Methods ◽  
N Nutrition ◽  
Direct And Indirect Methods ◽  
N Status

The non-destructive estimation of plant nitrogen (N) status is imperative for timely and in-season crop N management. The objectives of this study were to use canopy cover (CC) to establish the empirical relations between plant growth indices [shoot dry matter (SDM), leaf area index (LAI), shoot N accumulation (SNA), shoot nitrogen concentration (SNC)], and CC as well as to test the feasibility of using CC to assess N nutrition index (NNI) from Feekes 3 to Feekes 6 stages of winter wheat. Four multi-locational (2 sites), multi-cultivars (four cultivars), and multi-N rates (0–300 kg N ha–1) field experiments were carried out during 2016 to 2018 seasons. The digital images of the canopy were captured by a digital camera from Feekes 3 to Feekes 6 stages of winter wheat, while SDM, LAI, SNA, and SNC were measured by destructive plant sampling. CC was calculated from digital images developed by self-programmed software. CC showed significant correlations with growth indices (SDM, LAI, and SNA) across the different cultivars and N treatments, except for SNC. However, the stability of these empirical models was affected by cultivar characteristics and N application rates. Plant N status of winter wheat was assessed using CC through two methods (direct and indirect methods). The direct and indirect methods failed to develop a unified linear regression to estimate NNI owing to the high dispersion of winter wheat SNC during its early growth stages. The relationships of CC with SDM, SNC and NNI developed at individual growth stages of winter wheat using both methods were highly significant. The relationships developed at individual growth stages did not need to consider the effect of N dilution process, yet their stability is influenced by cultivar characteristics. This study revealed that CC has larger limitation to be used as a proxy to manage the crop growth and N nutrition during the early growth period of winter wheat despite it is an easily measured index.

scholarly journals Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

2019 ◽  
Vol 11 (4) ◽  
pp. 387 ◽  
Author(s):  
Ke Zhang ◽  
Xiaokang Ge ◽  
Pengcheng Shen ◽  
Wanyu Li ◽  
Xiaojun Liu ◽  
...  
Keyword(s):  
Grain Yield ◽  
Real Time ◽  
Dynamic Model ◽  
Vegetation Index ◽  
N Fertilizer ◽  
Growth Stages ◽  
Rice Grain ◽  
N Nutrition ◽  
Red Edge ◽  
Vegetation Indexes

Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.

scholarly journals Sensor based calibration study for in-season nitrogen management of winter wheat in Turkey

2020 ◽  
Vol 6 (2) ◽  
pp. 204-211
Author(s):  
Erdinc Savasli ◽  
Oguz Onder ◽  
Cemal Cekic ◽  
Hasan Mufit Kalayci ◽  
Ramis Dayioglu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Agricultural Research ◽  
Nitrogen Nutrition ◽  
Stem Elongation ◽  
Block Design ◽  
Vegetation Indices ◽  
Nutrition Status ◽  
Growth Stages ◽  
Yield Estimation

The aims of this study were to compare the responses of four winter wheat cultivars to nitrogen fertilization with vegetation indices calculated using spectral reflection (GreenSeeker hand-held sensor) and to estimate in-season yield (INSEY) using the vegetation indices. The field experiment was conducted at Transitional Zone Agricultural Research Institute of Eskisehir province, Turkey in 2007-2008, 2008-2009 and 2009-2010 growing seasons. The experimental layout was a 2factor factorial in the randomized complete block design. Nitrogen rates were 0, 40, 80, 120, 160 and 200 kg N ha-1. Vegetation Index (NDVI) was obtained at growth stages of Zadoks 24 (tillering stage), Zadoks stage 30 (stem elongation), Zadoks stage 31 (the first node is visible) and Zadoks stage 32 (the second node is visible). The results revealed that Zadoks stage 30 was the most realistic reading time. NDVI had the advantage of providing information on biomass, in addition to nitrogen nutrition status of crops, enabling in-season yield estimation possible. Therefore, NDVI based calibration equations were preferred for testing in the fields of actual farmers for the last year of study. A comparison of the system with traditional farmer applications indicated that yield estimation obtained by the new system was quite similar yields with 13.2 kg ha-1 less N in the spring (ZD 3.0), showing its economically promising value. Asian J. Med. Biol. Res. June 2020, 6(2): 204-211

scholarly journals Physiological and developmental traits associated with the grain yield of winter wheat as affected by phosphorus fertilizer management

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiu-Xiu Chen ◽  
Wei Zhang ◽  
Xiao-Yuan Liang ◽  
Yu-Min Liu ◽  
Shi-Jie Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Dry Matter ◽  
Photosynthetic Capacity ◽  
Growth Stages ◽  
Photosynthetic Parameters ◽  
Dry Matter Accumulation ◽  
Grain Yields ◽  
P Concentration ◽  
Spike Number

Abstract Although researchers have determined that attaining high grain yields of winter wheat depends on the spike number and the shoot biomass, a quantitative understanding of how phosphorus (P) nutrition affects spike formation, leaf expansion and photosynthesis is still lacking. A 3-year field experiment with wheat with six P application rates (0, 25, 50, 100, 200, and 400 kg P ha−1) was conducted to investigate this issue. Stem development and mortality, photosynthetic parameters, dry matter accumulation, and P concentration in whole shoots and in single tillers were studied at key growth stages for this purpose. The results indicated that spike number contributed the most to grain yield of all the yield components in a high-yielding (>8 t/ha) winter wheat system. The main stem (MS) contributed 79% to the spike number and tiller 1 (T1) contributed 21%. The 2.7 g kg−1 tiller P concentration associated with 15 mg kg−1 soil Olsen-P at anthesis stage led to the maximal rate of productive T1s (64%). The critical shoot P concentration that resulted in an adequate product of Pn and LAI was identified as 2.1 g kg−1. The thresholds of shoot P concentration that led to the maximum productive ability of T1 and optimal canopy photosynthetic capacity at anthesis were very similar. In conclusion, the thresholds of soil available P and shoot P concentration in whole plants and in single organs (individual tillers) were established for optimal spike formation, canopy photosynthetic capacity, and dry matter accumulation. These thresholds could be useful in achieving high grain yields while avoiding excessive P fertilization.

scholarly journals The Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

scholarly journals New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (24) ◽  
pp. 4170
Author(s):  
Pengfei Chen ◽  
Fangyong Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Green Band ◽  
Validation Data ◽  
Texture Parameters ◽  
Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

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.

Winter wheat growth and nitrogen demand in south coastal British Columbia

1994 ◽  
Vol 74 (4) ◽  
pp. 443-451 ◽  
Author(s):  
A. A. Bomke ◽  
W. D. Temple ◽  
S. Yu
Keyword(s):  
British Columbia ◽  
Winter Wheat ◽  
Dry Matter ◽  
N Uptake ◽  
Growth Stages ◽  
Dry Matter Accumulation ◽  
Soil N ◽  
Low Input ◽  
Crop Development ◽  
Coastal British Columbia

Winter wheat, Triticum aestivum, is a new crop in south coastal British Columbia. The purposes of this study were to characterize plant development, dry matter accumulation and N uptake under low input and intensively managed systems as well as to assess the capability of some of the region’s soils to supply N to the crop. Grain yields, crop development and dry matter and N accumulation were similar to those reported from southern England. High amounts of winter rainfall (November–April precipitation ranged from 523 to 1111 mm) leach virtually all residual NO3 from south coastal B.C. soils and, without N fertilization, result in uniformly N deficient winter wheat. The low input N regime, 75 kg N ha−1 at Zadoks growth stage 31, plus soil N mineralized subsequent to the winter leaching period were sufficient in this study to maximize grain and total aboveground crop dry matter yields, but not to achieve adequate grain protein contents. The soils in the study were capable of supplying N in amounts sufficient to support only 30–53% of the maximum N uptake between growth stages 31 and 78. Appropriate quantities and timing of N are critical to successful production of high-yielding, good-quality wheat in south coastal British Columbia. Nitrogen management is likely to be most efficient when guided by the stage of crop development and demand and not by spring soil sampling and mineral N analysis. Key words: Winter wheat, N demand, soil N supply, crop development, intensive crop management, low input

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