scholarly journals Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

2020 ◽  
Vol 12 (22) ◽  
pp. 3778
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Zhenhai Li ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Gaussian Processes ◽  
Image Features ◽  
Partial Least Square ◽  
Color Parameters ◽  
Model Based ◽  
N Concentration ◽  
Rgb Images ◽  
N Status ◽  
Leaf N

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

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 Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

2020 ◽  
Vol 12 (7) ◽  
pp. 1139
Author(s):  
Rui Dong ◽  
Yuxin Miao ◽  
Xinbing Wang ◽  
Zhichao Chen ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Fluorescence Sensor ◽  
Accurate Estimation ◽  
Growth Stages ◽  
Fluorescence Sensors ◽  
N Application ◽  
N Concentration ◽  
Leaf N Concentration ◽  
N Status ◽  
Leaf N

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

scholarly journals LEAF TOTAL NITROGEN CONCENTRATION AS AN INDICATOR OF NITROGEN STATUS FOR PLANTLETS AND YOUNG PLANTS OF EUCALYPTUS CLONES

2015 ◽  
Vol 39 (4) ◽  
pp. 1127-1140 ◽  
Author(s):  
Eric Victor de Oliveira Ferreira ◽  
Roberto Ferreira Novais ◽  
Bruna Maximiano Médice ◽  
Nairam Félix de Barros ◽  
Ivo Ribeiro Silva
Keyword(s):  
Total Nitrogen ◽  
Block Design ◽  
Nitrogen Concentration ◽  
Soil N ◽  
Total N ◽  
Chlorophyll Meter ◽  
N Concentration ◽  
Total Nitrogen Concentration ◽  
N Status ◽  
Leaf N

The use of leaf total nitrogen concentration as an indicator for nutritional diagnosis has some limitations. The objective of this study was to determine the reliability of total N concentration as an indicator of N status for eucalyptus clones, and to compare it with alternative indicators. A greenhouse experiment was carried out in a randomized complete block design in a 2 × 6 factorial arrangement with plantlets of two eucalyptus clones (140 days old) and six levels of N in the nutrient solution. In addition, a field experiment was carried out in a completely randomized design in a 2 × 2 × 2 × 3 factorial arrangement, consisting of two seasons, two regions, two young clones (approximately two years old), and three positions of crown leaf sampling. The field areas (regions) had contrasting soil physical and chemical properties, and their soil contents for total N, NH+4-N, and NO−3-N were determined in five soil layers, up to a depth of 1.0 m. We evaluated the following indicators of plant N status in roots and leaves: contents of total N, NH+4-N, NO−3-N, and chlorophyll; N/P ratio; and chlorophyll meter readings on the leaves. Ammonium (root) and NO−3-N (root and leaf) efficiently predicted N requirements for eucalyptus plantlets in the greenhouse. Similarly, leaf N/P, chlorophyll values, and chlorophyll meter readings provided good results in the greenhouse. However, leaf N/P did not reflect the soil N status, and the use of the chlorophyll meter could not be generalized for different genotypes. Leaf total N concentration is not an ideal indicator, but it and the chlorophyll levels best represent the soil N status for young eucalyptus clones under field conditions.

Diagnostic nitrogen concentrations for tomatoes grown in sand culture

1988 ◽  
Vol 28 (3) ◽  
pp. 401 ◽  
Author(s):  
DO Huett ◽  
G Rose
Keyword(s):  
Critical Values ◽  
Sand Culture ◽  
Nutrient Concentrations ◽  
Total N ◽  
Nitrate N ◽  
N Concentration ◽  
Leaf N Concentration ◽  
Petiole Sap ◽  
N Status ◽  
Leaf N

The tomato cv. Flora-Dade was grown in sand culture with 4 nitrogen (N) levels of 1.07-32.14 mmol L-1 applied as nitrate each day in a complete nutrient solution. The youngest fully opened leaf (YFOL) and remaining (bulked) leaves were harvested at regular intervals over the 16-week growth period. Standard laboratory leaf total and nitrate N determinations were conducted in addition to rapid nitrate determinations on YFOL petiole sap. The relationships between plant growth and leaf N concentration, which were significantly affected by N application level, were used to derive diagnostic leaf N concentrations. Critical and adequate concentrations in petiole sap of nitrate-N, leaf nitrate-N and total N for the YFOL and bulked leaf N were determined from the relationship between growth rate relative to maximum at each sampling time and leaf N concentration. YFOL petiole sap nitrate-N concentration, which can be measured rapidly in the field by using commercial test strips, gave the most sensitive guide to plant N status. Critical values of 770-1 120 mg L-I were determined over the 10-week period after transplanting (first mature fruit). YFOL (leaf + petiole) total N concentration was the most consistent indicator of plant N status where critical values of4.45-4.90% were recorded over the 4- 12 week period after transplanting (early harvests at 12 weeks). This test was less sensitive but more precise than the petiole sap nitrate test. The concentrations of N, potassium, phosphorus, calcium and magnesium in YFOL and bulked leaf corresponding to the N treatments producing maximum growth rates are presented, because nutrient supply was close to optimum and the leaf nutrient concentrations can be considered as adequate levels.

scholarly journals Does the Organ-Based N Dilution Curve Improve the Predictions of N Status in Winter Wheat?

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 500
Author(s):  
Ke Zhang ◽  
Xue Wang ◽  
Xiaoling Wang ◽  
Syed Tahir Ata-Ul-Karim ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
High Efficiency ◽  
Eastern China ◽  
Relative Yield ◽  
N Fertilizer ◽  
Growth Stages ◽  
Dilution Curve ◽  
Area Index ◽  
N Concentration ◽  
N Status

Accurately summarizing Nitrogen (N) content as a prelude to optimal N fertilizer application is complicated during the vegetative growth period of all the crop species studied. The critical nitrogen (N) concentration (Nc) dilution curve is a stable diagnostic indicator, which performs plant critical N concentration trends as crop grows. This study developed efficient technologies for different organ-based (plant dry matters (PDM), leaf DM (LDM), stem DM (SDM), and leaf area index (LAI)) estimation of Nc curves to enrich the practical applications of precision N management strategies. Four winter wheat cultivars were planted with 10 different N treatments in Jiangsu province of eastern China. Results showed the SDM-based curve had a better performance than the PDM-based curve in N nutrition index (NNI) estimation, accumulated N deficit (AND) calculation, and N requirement (NR) determination. The regression coefficients ‘a’ and ‘b’ varied among the four critical N dilution models: Nc = 3.61 × LDM–0.19, R2 = 0.77; Nc = 2.50 × SDM–0.44, R2 = 0.89; Nc = 4.16 × PDM–0.41, R2 = 0.87; and Nc = 3.82 × LAI–0.36, R2 = 0.81. In later growth periods, the SDM-based curve was found to be a feasible indicator for calculating NNI, AND, and NR, relative to curves based on the other indicators. Meanwhile, the lower LAI-based curve coefficient variation values stated that leaf-related indicators were also a good choice for developing the N curve with high efficiency as compared to other biomass-based approaches. The SDM-based curve was the more reliable predictor of relative yield because of its low relative root mean square error in most of the growth stages. The curves developed in this study will provide diverse choices of indicators for establishing an integrated procedure of diagnosing wheat N status, and improving the accuracy and efficiency of wheat N fertilizer management.

scholarly journals Estimation of Critical Nitrogen Concentration Based on Leaf Dry Matter in Drip Irrigation Spring Maize Production in Northern China

2021 ◽  
Author(s):  
biao jia ◽  
Jiangpeng Fu ◽  
Huifang Liu ◽  
Zhengzhou Li ◽  
Yu Lan ◽  
...  
Keyword(s):  
Nutritional Status ◽  
Drip Irrigation ◽  
Dry Matter ◽  
Utilization Efficiency ◽  
Dilution Curve ◽  
N Concentration ◽  
Spring Maize ◽  
Maize Varieties ◽  
N Status ◽  
Leaf N

Abstract Background: The application of nitrogen (N) fertilizer not only increases crop yield but also improves the N utilization efficiency. The critical N concentration (Nc) can be used to diagnose crops N nutritional status. The Nc dilution curve model of maize was calibrated with leaf dry matter (LDM) as the indicator, and the performance of the model for diagnosing maize N nutritional status was further evaluated. Three field experiments were carried out in two sites between 2018 and 2020 in Ningxia Hui Autonomous Region with a series of N levels (application of N from 0 to 450 kg N ha-1). Two spring maize cultivars, i.e., Tianci19 (TC19) and Ningdan19 (ND19), were utilized in the field experiment. Results: The results showed that a negative power function relationship existed between LDM and leaf N concentration (LNC) for spring maize under drip irrigation. The Nc dilution curve equation was divided into two parts: when the LDM < 1.11 t ha-1, the constant leaf Nc value was 3.25%; and when LDM > 1.11 t ha-1, the Nc curve was 3.33*LDM-0.24. Conclusion: The LDM based Nc curve can well distinguish data the N-limiting and non-N-limiting N status of maize, which was independent with maize varieties, growing seasons and stages. Additionally, the N nutrition index (NNI) had a significant linear correlation with the relative leaf dry matter (RLDM). This study revealed that the LDM based Nc dilution curve could accurately identify spring maize N status under drip irrigation. NNI can thus, be used as a robust and reliable tool to diagnose N nutritional status of maize.

scholarly journals Use of an Active Canopy Sensor and SPAD Chlorophyll Meter to Quantify Geranium Nitrogen Status

HortScience ◽  
2012 ◽  
Vol 47 (1) ◽  
pp. 45-50 ◽  
Author(s):  
Yun-wen Wang ◽  
Bruce L. Dunn ◽  
Daryl B. Arnall ◽  
Pei-sheng Mao
Keyword(s):  
Green Leaf ◽  
Fertilizer Treatment ◽  
N Content ◽  
Chlorophyll Meter ◽  
N Concentration ◽  
Flower Traits ◽  
Leaf N Content ◽  
N Rates ◽  
N Status ◽  
Leaf N

This research was conducted to investigate the potentials of normalized difference vegetation index (NDVI), a Soil-Plant Analyses Development (SPAD) chlorophyll meter, and leaf nitrogen (N) concentration [% dry matter (DM)] for rapid determination of N status in potted geraniums (Pelargonium ×hortorum). Two F1 cultivars were chosen to represent a dark-green leaf cultivar, Horizon Deep Red, and a light-green leaf cultivar, Horizon Tangerine, and were grown in a soilless culture system. All standard 6-inch (15.24-cm) pots filled with a medium received an initial top-dress application of 5 g controlled-release fertilizer (15N–9P–12K), then plants were supplemented with additional N in the form of urea at 0, 50, 100, or 200 mg·L−1 N every few days to produce plants ranging from N-deficient to N-sufficient. The NDVI readings of individual plants from a NDVI pocket sensor developed by Oklahoma State University were collected weekly until the flowering stage. Data on flower traits, including number of pedicels (NOP), number of full umbels per pot (NOFU), total number of flowers per pot (TNF), number of flowers per pedicel (NOF), and inflorescences diameter (IFD), were collected 3 months after initial fertilizer treatment. After measuring flower traits, pedicels were removed from each pot, and SPAD value, NDVI, and leaf N concentration (g·kg−1 DM) were measured simultaneously. Cultivar and N rate significantly affected all but two flower and one N status parameters studied. The coefficient of determination R2 showed that NOP, NOFU, and TNF traits were more related to the N rates and the status parameters studied for ‘Horizon Tangerine’ than for ‘Horizon Deep Red’. For the latter cultivar, NOP and TNF traits were highly related to NDVI and SPAD values than N rates and leaf N content parameters. Correlation analysis indicated that the NDVI readings (R2 = 0.848 and 0.917) and SPAD values (R2 = 0.861 and 0.950) were significantly related to leaf N content (g·kg−1 DM) between cultivars. However, sensitivity of the NDVI and chlorophyll values to N application rate in geranium was slightly less than leaf N content. Strong correlations (R2 = 0.974 and 0.979, respectively) between NDVI and SPAD values were found within cultivars. The results demonstrated NDVI and SPAD values can be used to estimate N status in geranium. Because the pocket NDVI sensor will be cheaper than the SPAD unit, it has an advantage in determining N content in potted ornamentals.

Effects of strobilurin fungicide programmes and fertilizer nitrogen rates on winter wheat: severity ofSeptoria tritici, leaf senescence and yield

2011 ◽  
Vol 150 (4) ◽  
pp. 411-426 ◽  
Author(s):  
S. ISHIKAWA ◽  
M. C. HARE ◽  
P. S. KETTLEWELL
Keyword(s):  
Winter Wheat ◽  
Plant Height ◽  
Leaf Senescence ◽  
Field Experiments ◽  
Fertilizer Nitrogen ◽  
N Concentration ◽  
Progress Curve ◽  
Strobilurin Fungicide ◽  
Leaf N Concentration ◽  
Leaf N

SUMMARYFour field experiments were conducted over 3 years to study whether adding a strobilurin fungicide to a triazole fungicide programme for disease control in winter wheat had any influence, in combination with different rates of fertilizer nitrogen (N), on the severity of foliar diseases, the degree of leaf senescence and consequently on yield.Septoria triticiwas the dominant foliar disease observed in all experiments. The area under the disease progress curve (AUDPC) tended to be greater for untreated plots than those treated with fungicides; however, the performance of the programme containing a strobilurin fungicide did not always exceed that of the triazole-only programme. Fitting a quadratic equation to relationships between leaf N concentration and the proportion of leaf area covered withS. triticion a relative scale across the four experiments indicated a possibility that there could be an optimum N concentration in host plants forS. triticito develop, rather than a simple increase or decrease with a rise in plant N concentration. Plant height tended to be reduced following an application of a mixture of epoxiconazole and trifloxystrobin; however, it was not clear whether there was any association between plant height and the severity ofS. tritici. S. triticicaused a reduction in mean grain weight (MGW) in most of the experiments. It was concluded that an optimum leaf N concentration may exist forS. triticiin winter wheat.

scholarly journals A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Qi ◽  
Yanan Zhao ◽  
Yufang Huang ◽  
Yang Wang ◽  
Wei Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Mean Value ◽  
Wheat Cultivars ◽  
N Content ◽  
Color Parameters ◽  
The Neural Network ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

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