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Timely and accurate estimation of plant nitrogen (N) status is crucial to the successful implementation of precision N management. It has been a great challenge to non-destructively estimate plant N status across different agro-ecological zones (AZs). The objective of this study was to use random forest regression (RFR) models together with multi-source data to improve the estimation of winter wheat (Triticum aestivum L.) N status across two AZs. Fifteen site-year plot and farmers' field experiments involving different N rates and 19 cultivars were conducted in two AZs from 2015 to 2020. The results indicated that RFR models integrating climatic and management factors with vegetation index (R2 = 0.72–0.86) outperformed the models by only using the vegetation index (R2 = 0.36–0.68) and performed well across AZs. The Pearson correlation coefficient-based variables selection strategy worked well to select 6–7 key variables for developing RFR models that could achieve similar performance as models using full variables. The contributions of climatic and management factors to N status estimation varied with AZs and N status indicators. In higher-latitude areas, climatic factors were more important to N status estimation, especially water-related factors. The addition of climatic factors significantly improved the performance of the RFR models for N nutrition index estimation. Climatic factors were important for the estimation of the aboveground biomass, while management variables were more important to N status estimation in lower-latitude areas. It is concluded that integrating multi-source data using RFR models can significantly improve the estimation of winter wheat N status indicators across AZs compared to models only using one vegetation index. However, more studies are needed to develop unmanned aerial vehicles and satellite remote sensing-based machine learning models incorporating multi-source data for more efficient monitoring of crop N status under more diverse soil, climatic, and management conditions across large regions. [ABSTRACT FROM AUTHOR]

Lignin is an important cell wall component that provides plants with mechanical support and improved tolerance to pathogen attacks. Previous studies have shown that plants rich in S-lignin content or with a higher S/G ratio always exhibit higher efficiency in the utilization of lignocellulosic biomass. Ferulate 5-hydroxylase, or coniferaldehyde 5-hydroxylase (F5H, or CAld5H), is the critical enzyme in syringyl lignin biosynthesis. Some F5Hs have been characterized in several plant species, e.g., Arabidopsis , rice, and poplar. However, information about F5H s in wheat remains unclear. In this study, a wheat F5H gene, TaF5H1 , together with its native promoter (pTaF5H1), was functionally characterized in transgenic Arabidopsis. Gus staining results showed that TaF5H1 could be expressed predominantly in the highly lignified tissues in transgenic Arabidopsis plants carrying pTaF5H1:Gus. qRT-PCR results showed that TaF5H1 was significantly inhibited by NaCl treatment. Ectopic expression of TaF5H1 driven by pTaF5H1 (i.e., pTaF5H1:TaF5H1) could increase the biomass yield, S-lignin content, and S/G ratio in transgenic Arabidopsis plants, which could also restore the traces of S-lignin in fah1-2 , the Arabidopsis F5H mutant, to an even higher level than the wild type (WT), suggesting that TaF5H1 is a critical enzyme in S lignin biosynthesis, and pTaF5H1:TaF5H1 module has potential in the manipulation of S-lignin composition without any compromise on the biomass yield. However, expression of pTaF5H1:TaF5H1 also led to decreased salt tolerance compared with the WT. RNA-seq analysis showed that many stress-responsive genes and genes responsible for the biosynthesis of cell walls were differentially expressed between the seedlings harboring pTaF5H1:TaF5H1 and the WT, hinting that manipulation of the cell wall components targeting F5H may also affect the stress adaptability of the modified plants due to the interference to the cell wall integrity. In summary, this study demonstrated that the wheat pTaF5H1: TaF5H1 cassette has the potential to modulate S-lignin composition without any compromise in biomass yield in future engineering practice. Still, its negative effect on stress adaptability to transgenic plants should also be considered. • pTaF5H1:TaF5H1 module can be used to improve S-lignin composition in transgenic Arabidopsis plants without biomass loss. • Expression of pTaF5H1 : TaF5H1 in Arabidopsis led to decreased salt tolerance compared to the wild type. • Alteration of cell wall component may lead to decreased salt tolerance of the Arabidopsis plants expressing pTaF5H1:TaF5H1. [ABSTRACT FROM AUTHOR]

Diagnosing the status of crop nitrogen (N) helps to optimize crop yield, improve N use efficiency, and reduce the risk of environmental pollution. The objectives of the present study were to develop a critical N (Nc) dilution curve for winter wheat (based on spike dry matter [SDM] during the reproductive growth period), to compare this curve with the existing Nc dilution curve (based on plant dry matter [DM] of winter wheat), and to explore its ability to reliably estimate the N status of winter wheat. Four field experiments, using varied N fertilizer rates (0–375 kg ha-1) and six cultivars (Yangmai16, Ningmai13, Ningmai9, Aikang58, Yangmai12, Huaimai 17), were conducted in the Jiangsu province of eastern China. Twenty plants from each plot were sampled to determine the SDM and spike N concentration (SNC) during the reproductive growth period. The spike Nc curve was described by Nc = 2.85×SDM-0.17, with SDM ranging from 0.752 to 7.233 t ha-1. The newly developed curve was lower than the Nc curve based on plant DM. The N nutrition index (NNI) for spike dry matter ranged from 0.62 to 1.1 during the reproductive growth period across the seasons. Relative yield (RY) increased with increasing NNI; however, when NNI was greater than 0.96, RY plateaued and remained stable. The spike Nc dilution curve can be used to correctly identify the N nutrition status of winter wheat to support N management during the reproductive growth period for winter wheat in eastern China. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop an effective method for predicting ecotype-specific cultivar parameters for the large-scale application of wheat crop simulation models. The Markov Chain Monte Carlo (MCMC) technique was explored for parameter calibration based on an existing model. We estimated the posterior probability distribution of ecotype-specific cultivar parameters at four ecosites (Huai'an, Zhengzhou, Weifang, and Shijiazhuang in China) by using the historical phenological stages of wheat and daily weather data from 1980 to 1995. 1000 sets of cultivar parameters which were randomly sampled from the posterior probability distribution at each ecosite from 1996 to 2005 were used to evaluate the MCMC-based method. Optimal 50 sets of parameters were chosen from the 1000 sets of parameters at each ecosite to represent the ecotype-specific cultivar parameters of the Jiangsu, Henan, Shandong and Hebei provinces to evaluate the MCMC-based method for the year 2005 at the regional scale. The results showed that the coefficients of determination (R 2 ) between the observed and estimated phenological stages ranged from 0.61 to 0.72, with a root mean square error (RMSE) of less than 3.6 days and a root mean square deviation ( RMSD ¯ ) of less than 3.7 days at the site scale. All of the RMSE and RMSD ¯ values for the three phenological stages obtained using the posterior probability distribution at the four ecosites were significantly lower than those based on the prior probability distribution. At the regional scale, R 2 between the observed and estimated phenological stages was greater than 0.86, with RMSE less than 3.4 days and RMSD ¯ less than 4.0 days in most of the grids for year 2005. The estimated phenological stages agreed well with the observations, suggesting that the MCMC technique has high reliability of for estimating multiple parameter combinations in a wheat phenology model. The combination of the present MCMC technique and a phenology model could be used for estimating the ecotype-specific cultivar parameters for the main wheat growing regions of China, which can be used to predict progress of wheat development at the regional scale. [ABSTRACT FROM AUTHOR]

Leaf equivalent water thickness (LEWT) is an important indicator of crop water status. Effectively monitoring the water status of wheat under different nitrogen treatments is important for effective water management in precision agriculture. Trends in the variation of LEWT in wheat plants during plant growth were analyzed based on field experiments in which wheat plants under various water and nitrogen treatments in two consecutive growing seasons. Two-band spectral indices [normalized difference spectral indices (NDSI), ratio spectral indices (RSI), different spectral indices (DSI)], and then three-band spectral indices were established based on the best two-band spectral index within the range of 350–2500 nm to reduce the noise caused by nitrogen and saturation. Then, optimal spectral indices were selected to construct models of LEWT monitoring in wheat. The results showed that the two-band spectral index NDSI(R1204, R1318) could be used for LEWT monitoring throughout the wheat growth season, but the model performed differently before and after anthesis. Therefore, further two-band spectral indices NDSIb(R1445, R487), NDSIa(R1714, R1395), and NDSI(R1429, R416), were constructed for the two developmental phases, with NDSI(R1429, R416) considered to be the best index. Finally, a three-band index (R1429−R416−R1865)/(R1429+R416+R1865), which was superior for monitoring LEWT and reducing the noise caused by nitrogen, was formed on the best two-band spectral index NDSI(R1429, R416) by adding the 1,865 nm wavelenght as the third band. This produced more uniformity and stable performance compared with the two-band spectral indices in the LEWT model. The results are of technical significance for monitoring the water status of wheat under different nitrogen treatments in precision agriculture. [ABSTRACT FROM AUTHOR]

Abstract: Wheat is the second primary crop in China. Wheat production in China is an important component for national food security. The combination of high-resolution Global Climate Model (GCM) and WheatGrow model was used to assess the effects of climate change on wheat yields in the main wheat production regions of China. With the application of many techniques including the downscaling of meteorological data, rasterizing of sowing date, parameterization of region cultivar and vectorization of soil data, the spatial data in study area is divided into homogeneous grids with the resolution of 0.1°×0.1°. The grid is taken as the basic simulation unit, and each grid has a complete set of input data (meteorological, soil, management and varieties). Regional productivities are simulated with WheatGrow for each grid cell under scenarios of climate-change. There is an advance in flowering date in future climate compare to 2000s, but with a more homogeneous pattern for the whole producing region. The changes in grain filling period are relatively stable. Under rain-fed conditions, wheat yield is reduced in the north regions of China in three future periods, while wheat yield increases in the south regions of China. Under full-irrigation conditions, irrigated wheat yields will increase in almost all regions of whole producing region. The spatial pattern of evapotranspiration change is quite similar to that of yield change under rain-fed and full-irrigation conditions. The correlation between wheat yield and evapotranspiration (ET) increases to 0.96 and 0.51 (p <0.01) under rain-fed and full-irrigation conditions, respectively. The irrigation water use efficiency (IWUE) will decrease under three time slices in 2030s, 2050s and 2070s in western Shandong, southern Sichuan, as well as northern Henan, Shanxi and Shaanxi, while IWUE will increase under scenarios of climate-change in other areas. The results revealed that the increase in effective irrigation in the future would help to increase the ET and further improve the wheat yield in the northern regions of China, and the limited water should be mad full use of in the regions with relatively high IWUE under scenarios of climate-change. [Copyright &y& Elsevier]

Abstract: The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Critical nitrogen (N) dilution curves (CNDCs) have been developed to describe the dilution dynamic of N and to diagnose N status in plants. In this study, to develop a convenient alternative CNDC determination method, four field experiments involving different N rates (0–360 kg N ha-1) and six wheat varieties were performed at different eco-sites from 2014 to 2019. The normalised difference red-edge (NDRE) index extracted from the RapidSCAN CS-45 (Holland Scientific Inc., Lincoln, NE, USA) sensor was used as a driving factor instead of plant dry matter (PDM) to establish a new alternative winter wheat CNDC. The newly developed CNDC was described by the equation Nc = 0.90NDRE−0.88, when NDRE values were ≤ 0.19 and constant Nc = 3.81%, which was independent of the NDRE values. Compared to PDM-derived CNDC (R2 = 0.73) developed with the same dataset, a comparable precision was obtained using NDRE-derived CNDC (R2 = 0.76) and both CNDCs could accurately discriminate wheat N status. Moreover, the NDRE could be inexpensively and rapidly measured using the active sensor. The relationship between NDRE-derived CNDC and grain yield was also analysed to facilitate in-season N management, and the R2 value reached 0.79 and 0.87 at jointing and booting stages, respectively. The NDRE-based CNDC can be used to effectively diagnose wheat N status and as an alternative approach for non-destructive determination of crop N levels. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation. [ABSTRACT FROM AUTHOR]

• Extending dualex indicators to canopy level by LAI raised N diagnosis accuracy. • Fusing multi-source data helps build dynamic and reliable N diagnosis models. • Independent data and integrated assessment ensured the optimal N diagnosis model. • Advising on the priority of obtaining external data under different targets. Rapid and accurate nitrogen (N) diagnosis plays a crucial role in precise N fertilizer management of wheat. However, most existing N diagnosis models based on proximal fluorescence sensors' indicators are confined to a single growth stage, thereby limiting the accuracy and universality of models. Therefore, this study aimed to construct and evaluate wheat N diagnosis models based on proximal fluorescence sensors across growth stages by fusing multi-source data. Six field experiments conducted over four years, involved diverse planting densities, wheat varieties, and N rates. These experiments were performed to investigate the relationship between optical indicators obtained from Dualex 4 and Multiplex 3, and both the plant N accumulation (PNA) and the N nutrition index (NNI). The dualex indicators were extended to the canopy level (canopy_dualex indicators) by the leaf area index (LAI). In addition to formulating N nutrition diagnostic models based on the three optical indicators solely, we further integrated meteorological factors, soil basic fertility, and cultivation practices to construct dynamic N diagnosis models coupling with machine learning algorithms. Fusing multi-source data significantly improved the R2, resulting in an increase of 0.20 and 0.29 when predicting PNA for dualex and multiplex, respectively. The canopy_dualex indicator consistently performed best in predicting both PNA (R2 = 0.75, RRMSE = 28.71 %) and NNI (R2 = 0.60, RRMSE = 24.62 %) among the three optical indicators. Moreover, the inclusion of LAI effectively addressed the overfitting issue observed in dualex when fusing multi-source data to construct the models. Consistency tests and ROC curve analyses provided robust evidence that canopy_dualex exhibited the highest consistency and the most powerful diagnostic ability. Additionally, multiplex demonstrated superior performance compared to dualex in predicting PNA and NNI, with higher R2 (0.46–0.50) and lower RRMSE (28.57 %-40.84 %). The results underscored that multi-source data fusion significantly improved the accuracy of universal N nutrition models for wheat across growth stages, leveraging proximal fluorescence sensors to cover the entire wheat growth process. This approach allows for the identification of N nutrition status at any given time, facilitating timely adjustments in N fertilizer management. From the two aspects of feature selection results of multi-source data fusion and the difficulty of obtaining data in the application, it is recommended that the first variable to be added in N nutrition diagnosis of is N application amount. If the target is PNA, the accumulated precipitation (APP) data is collected first. In order to obtain NNI, the soil total N content is considered first. Furthermore, this flexibility offers a convenient and promising option for practical agricultural production. [ABSTRACT FROM AUTHOR]

China's winter wheat (Triticum aestivum L.) production heavily relies on nitrogen (N) fertilizer application, which poses challenges due to the inability of most smallholders to accurately apply N according to site-specific conditions. Difficulty in accurately estimating the nitrogen demands of wheat is a barrier to precise nitrogen fertilizer management. We hypothesized that assessing plant N uptake at different sites can potentially improve site-specific N management based on the theoretical N rate method. To address this issue, this study proposes a cost-effective and user-friendly method to accurately predict plant nitrogen uptake for improving N management decisions. The study used a meta-analysis method, and modeling by integrating datasets from published papers. Then, the model's performance was evaluated using nine different nitrogen input and yield level scenarios built from published paper datasets. A nitrogen fertilizer recommendation model under a specific target yield using both the linear mixed effects model and random forest model was established, with the latter demonstrating superior performance. The model predicts plants' N demand index (NDI) considering geographic, varietal, and climatic characteristics as model input variables. In the scenario suitable for the model, the model reduces the N fertilizer rate by 17.91–110.48 kg ha−1 N, resulting in improved economic benefits by 14.15–137.59 $ ha−1, reduced N 2 O emissions by 15.23∼53.7%, and enhanced NEEB benefits by 0.18∼1.78%. Notably, the medium-level N input with low-level yield output scenario closely resembles the smallholder production scenario. Improving wheat plants' grain protein content and plant height through breeding can improve their nitrogen requirements by analyzing the factors affecting NDI. The model's efficacy in terms of N recommendation, economic benefits, N 2 O emissions, and the net ecosystem economic benefit (NEEB) across the nine evaluated scenarios was assessed. Based on the model performance, the factors influencing NDI and providing insights for wheat breeding improvements were identified. Additionally, model suitability assessment demonstrates that, under a typical smallholder production scenario, the model offers economic benefits while reducing pollution. By implementing this approach, smallholders can achieve optimized N management, improving their economic outcomes and contributing to environmental sustainability. This study partially filled the gap between blanket fertilization recommendations and fertilization based on site-specific soil testing. • Estimating the nitrogen demands of wheat plants in different scenarios for nitrogen fertilizer recommendation. • A model for predicting the nitrogen demands of wheat by variety and environmental factors was constructed. • Factors affecting wheat N demands and directions for wheat breeding improvement were identified. • The scenarios suitable for the application of the model are the actual production scenarios of most smallholders. • The model is easily used by smallholders, improving their economic benefits and reducing environmental risks. [ABSTRACT FROM AUTHOR]

Real-time and non-destructive nitrogen (N) status diagnosis is needed to support in-season N management decision-making for modern wheat production. For this purpose, satellite sensor imaging can act as an effective tool for collecting crop growth information across large areas, but they can be challenging to calibrate with ground reference data. This research aimed to calibrate satellite remote sensing-derived models for crop growth estimation and N status diagnosis based on fine-resolution unmanned aerial vehicle (UAV) images, thus, map wheat growth and N status at the county scale. Seven wheat field experiments involving multi cultivars and different N applications were conducted at four farms of Xinghua county from 2017 to 2021. A fixed-wing UAV sensing system and the Sentinel 2 (S2) satellite were used to collect wheat canopy multispectral images; three growth variables (plant dry matter (PDM), plant N accumulation (PNA) and N nutrition index (NNI)) and weather data, synchronized with spectral imagery, were obtained at the jointing and booting stages. The farm-scale PDM (UAV-PDM) and PNA (UAV-PNA) maps can be derived from the UAV images at the four farms, which were further upscaled to grids to match the S2 image resolution using pixel aggregation method. Then, satellite-based prediction models were constructed by fitting four machine learning algorithms to the relationships between satellite spectral indices, upscaled PDM (PNA) and weather data. Amongst the four methods tested, the random forest (RF) achieved the greatest prediction accuracy for PDM (R 2 = 0.69–0.93) and PNA (R 2 = 0.60–0.77). Meanwhile, an indirect diagnosis method was used to calculate the NNI. The results indicated that the model derived from the S2 imagery performed well for predicting NNI (R 2 = 0.46–0.54) at the jointing and booting stages. Thereby, the NNI was used to map winter wheat N nutrition status at the county scale. In summary, this research demonstrated and evaluated an approach to combine UAV and satellite sensor images to diagnose wheat growth and N status across large areas. • The farm-scale PDM and PNA maps were upscaled to grids to match S2 image resolution. • The satellite-based prediction models were constructed by fitting four ML algorithms. • The NNI diagnosis model and satellite images were used to map winter wheat N nutrition status at the county scale. [ABSTRACT FROM AUTHOR]

Accurate and transferable wheat nitrogen status estimation is very important to plant phenotyping and smart agricultural management. The goal of this study was to establish a wheat nitrogen status estimation model across all growth stages by combining proximal sensing and meteorological data. From 2010–2020, nine multi-nitrogen rates field trials were conducted at five sites involving different wheat varieties. Proximal sensing data were acquired from a Crop Circle sensor at key growth stages and meteorological data were aggregated from planting to the corresponding sensing date. Deep neural network (DNN) and long short-term memory (LSTM) were adopted to estimate above-ground biomass, plant nitrogen uptake, plant nitrogen concentration, and the nitrogen nutrition index. Random forest (RF) was used as a benchmark regression model. Multi-task learning (MTL) based on DNN was conducted to estimate the four nitrogen indicators simultaneously. A genetic algorithm (GA) was tested to optimize the hyperparameters, connection weights, and loss function weights (for MTL) of neural networks separately. The results revealed that DNN (R2 =0.83–0.96) and MTL (R2 =0.81–0.96) achieved an overall comparable high accuracy with RF (R2 =0.83–0.97), whereas LSTM (R2 =0.76–0.93) did not improve the nitrogen status estimation in our dataset. This study presented a concise and efficient framework dedicated to exploring the transferability of phenotypic predictions and provided insights into understanding crop growth and nitrogen dynamics in response to environmental conditions. • A framework combined deep learning and evolutionary algorithm was proposed. • The proposed model performed high transferability in different space and time. • Features with cumulative effect greatly impacted the wheat N status estimation. • Water-related features were essential for wheat nitrogen content estimation. [ABSTRACT FROM AUTHOR]

• Ensemble learning achieved the best performance for field-scale wheat yield prediction. • Normalized Difference Red Edge Index was the most contributory features. • Average temperature, minimum temperature, and relative humidity were key weather data. • The low temperature and dry air in winter and spring are limiting factors in wheat yield. Accurate and timely wheat yield prediction is of great importance to global food security. Early prediction of wheat yield at a field scale is essential for site-specific precision management. This study aimed to develop an in-season wheat yield prediction model at field-scale by integrating proximal sensing and weather data. Nine multi-N rates field experiments were conducted at five sites involving different wheat cultivars from 2010 to 2020. Proximal sensing data were collected from a Crop Circle sensor at the stem elongation stage and weather data were collected from 30 days before planting to the flowering date. Eleven statistical and machine learning (ML) regression algorithms were adopted, along with two aggregation intervals (disaggregated or aggregated data) and two feature selection methods (based on Pearson Correlation Coefficient or Recursive Feature Elimination). The results revealed that the ensemble learning models (Random Forest, eXtreme Gradient Boosting) achieved the best overall performance (R2 = 0.74 ∼ 0.78, RMSE = 0.78 ∼ 0.85 t ha−1). Feature importance analysis showed that Normalized Difference Red Edge Index (NDRE), average temperature, minimum temperature, and relative humidity were the most contributory features, especially from the planting date to the stem elongation date (for weather features). The aggregation approach and feature selection method did not significantly affect the yield prediction performance for the seven ML methods. This study introduced a promising framework that complements county-scale models and provided insights into understanding yield responses to environmental conditions. The best prediction model can be applied for guiding real-time sensor-based precision fertilization. [ABSTRACT FROM AUTHOR]

• The NDVI T s constructed by spectral and textural information performed well in wheat LNC monitoring and GPC estimation. • Textural information can assist spectral information to monitor wheat LNC and GPC effectively. • ANN model combining spectra, texture and ecological factor performed well in wheat GPC estimation. • Effective ecological factors are good predictors to improve the prediction accuracy of wheat GPC. Nitrogen is an essential element of wheat growth and grain quality. Leaf nitrogen content (LNC), a critical monitoring indicator of crop nitrogen status, plays a reference role for later estimations of grain protein content (GPC). Developments in unmanned aerial vehicle (UAV) platforms and multispectral sensors have provided new approaches for LNC monitoring and GPC estimation, with great convenience for assessing the nutritional status of plants and grains without traditional destructive sampling. The objective of this study was to evaluate the feasibility of wheat LNC monitoring and GPC estimation based on UAV multispectral imagery. Wheat experiments were carried out in Xinghua, Kunshan and Suining of Jiangsu Province during 2018−2019 and in Rugao of Jiangsu Province during 2020−2021 with different varieties and nitrogen application rates. Remote sensing images were obtained by a multi-rotor UAV carrying a multispectral camera. The destructive sampling method was used to collect LNC, GPC and other field data. Wheat LNC monitoring and GPC estimation models were established after selection of the optimal indicators. Different modelling methods were used for the comparative analysis, including unitary linear regression, multiple linear regression and artificial neural network (ANN) methods. Three techniques were adopted to improve the GPC prediction accuracy: (1) multiple factors were substituted for single factor for the prediction; (2) texture information was added through further imagery mining; and (3) ecological factors were considered to improve the prediction mechanism. The results showed that the use of UAV-based Airphen multispectral imagery had a good effect on wheat LNC monitoring and GPC estimation. The vegetation indices constructed by red-edge and near-infrared bands had good performances in LNC monitoring and GPC estimation. The addition of texture information and ecological factors further improved the modelling accuracy. In this study, the optimal wheat GPC estimation model was established by NDVI (675, 730) at the jointing stage, NDVI T (730 mea. , 850) at the booting stage, NDVI T (730 mea. , 850) at the flowering stage and NDVI (730, 850) at the early filling stage. The modelling R2, validation R2 and relative root mean square error (RRMSE) reached 0.662, 0.7445 and 0.0635, respectively. The results provide a reference for crop LNC monitoring and GPC estimation based on UAV multispectral imagery. [ABSTRACT FROM AUTHOR]

Abstract: Real-time and nondestructive monitoring of crop nitrogen (N) status is of significant importance for precision N management in rice and wheat production. In eight field experiments with different N rates, water regimes and cultivars in rice and wheat crops, a new form of three-band vegetation indices was constructed to reduce saturation in two-band vegetation indices, and the optimal common three-band vegetation index was selected to establish models for canopy leaf N concentration (LNC) monitoring in rice and wheat. The results showed that the linear models for LNC monitoring with (R 924 − R 703 +2× R 423)/(R 924 + R 703 −2× R 423) were stable and accurate, with coefficient of determination (R 2) of 0.870 and 0.857, and SE of 0.052 and 0.148 in rice and wheat, respectively. Testing of the models with independent data gave R 2 of 0.866 and 0.883, RRMSE of 13.1% and 16.9%, and slope of 0.741 and 0.980 in rice and wheat, respectively. Further analysis of the influence of bandwidth change on LNC accuracy indicated that the allowable bandwidths for the central bands were 36nm for 924nm, 15nm for 703nm and 21nm for 423nm. The new three-band vegetation indices with narrow bands and broad bands in the present study are generally more effective for LNC monitoring compared with the other published vegetation indices. [Copyright &y& Elsevier]

Critical nitrogen (N c) dilution curves have been widely used for the plant N status diagnosis, N management, crop modeling, and remote sensing. Wheat is a major crop worldwide whose N c dilution curves developed under different conditions (genotype × environment × management) show large parameter variations. Herein, a dataset of 19 nitrogen fertilizer experiments (n = 656) from five wheat planting sites in China was used to evaluate the uncertainty in these curves and explain the sources of differences using the Bayesian theory method. The uncertainty of the fitted curve decreased with the increase in plant biomass. The parameter A 2 of fitted curves of genotype × environment × management combinations showed a greater variation than A 1 , with slight parameter differences among different genotypes and planting sites. The variables related to genotype and growth environment, i.e., maximum N concentration, maximum biomass, and accumulated growing degree days during the vegetative growth period, were the sources of differences in curve parameters. Though the N c curve differences between genotype × environment × management were statistically significant, the nitrogen nutrition index (NNI) differences remained relatively small. This study provides insights for developing a more diverse N c dilution curve to aid in optimal nitrogen fertilization management in wheat. [ABSTRACT FROM AUTHOR]

• We computed Normalized Difference Texture Index (NDTI) from fixed-wing UAS images. • Random Forest was employed to combine NDTI, vegetation index, and color index. • The optimal image resolution for texture extraction may depend on crop size. Precision crop management in modern agriculture requires timely and effective acquisition of crop growth information. Recently, unmanned aerial systems (UASs) have rapidly developed and are now widely used in crop remote sensing (RS). Vegetation index (VI) and color index (CI) are commonly used RS methods to monitor crops. Texture is intrinsic information of the images, which can reflect the crop canopy structure and be used for vegetation classification. The objective of this study was to explore the potential of combining VI, CI, and texture to improve the estimation accuracy of wheat growth parameters based on fixed-wing UAS imagery. Wheat field experiments were carried out at the Xinghua Experimental Station for two consecutive years of 2017–2019 on three wheat cultivars under five nitrogen fertilization rates. Two commonly used wheat growth parameters, leaf area index (LAI) and leaf dry matter (LDM), synchronized with wheat field UAS images, were obtained at key growth stages. Simple regression (SR) was used to determine quantitative relationships between RS variables (VI, CI, and texture) and LAI, LDM. The data showed that individual texture does not correlate well with wheat growth parameters, while a texture index (TI), containing two texture measurements, showed stronger correlation with LAI and LDM. With the utilization of simple regression (SR), VI (R2 > 0.65, RRMSE < 21.87%) exhibited the best accuracy in estimating LAI and LDM, followed by TI (R2 > 0.51, RRMSE < 26.28%) and CI (R2 > 0.34, RRMSE < 27.74%). Multiple linear regression (MLR) and random forest (RF) were further employed to develop LAI and LDM estimation models using different input variable sets (VIs, VIs + CIs, and VIs + CIs + TIs). Compared with SR and MLR, the RF models that combined VIs, CIs, and TIs greatly improved the estimation accuracy of LAI and LDM, and the validated R2 of the best RF models for LAI and LDM estimation reached 0.78 and 0.78 (RRMSE = 17.32% and 13.83%) in pre-heading stages, 0.81 and 0.77 (RRMSE = 17.86% and 16.08%) in post-heading stages, and 0.76 and 0.75 (RRMSE = 18.13% and 16.79%) in all stages, respectively. This study demonstrated that image textures can assist wheat monitoring to achieve higher estimation accuracy of LAI and LDM, and fixed-wing UAS is a promising platform that can provide reliable data for large-scale crop management. [ABSTRACT FROM AUTHOR]