Effects of dense planting patterns on photosynthetic traits of different vertical layers and yield of wheat under different nitrogen rates

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted wheat.Inter-plant and inter-row distances were used to define ratefixed pattern(RR)and row-fixed pattern(RS)density treatments.Meanwhile,four nitrogen(N)rates(0,144,192,and 240 kg N ha-1,termed N0,N144,N192,and N240)were applied with three densities(225,292.5,and 360×10^(4)plants ha^(-1),termed D225,D292.5,and D360).The wheat canopy was clipped into three equal vertical layers(top,middle,and bottom layers),and their chlorophyll density(Ch D)and photosynthetically active radiation interception(FIPAR)were measured.Results showed that the response of Ch D and FIPAR to N rate,density,and pattern varied with different layers.N rate,density,and pattern had significant interaction effects on Ch D.The maximum values of whole-canopy Ch D in the two seasons appeared in N240 combined with D292.5 and D360 under RR,respectively.Across two growing seasons,FIPAR values of RR were higher than those of RS by 29.37%for the top layer and 5.68%for the middle layer,while lower than those of RS by 20.62%for the bottom layer on average.With a low N supply(N0),grain yield was not significantly affected by density for both patterns.At N240,increasing density significantly increased yield under RR,but D360 of RS significantly decreased yield by 3.72%and 9.00%versus D225 in two seasons,respectively.With an appropriate and sufficient N application,RR increased the yield of densely planted wheat more than RS.Additionally,the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns,suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

Development of a novel critical nitrogen concentration-cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

Accurate nitrogen(N)nutrition diagnosis is essential for improving N use efficiency in crop production.The widely used critical N(Nc)dilution curve traditionally depends solely on agronomic variables,neglecting crop water status.With three-year field experiments with winter wheat,encompassing two irrigation levels(rainfed and irrigation at jointing and anthesis)and three N levels(0,180,and 270 kg ha1),this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration(T),providing a comprehensive analysis of the interaction between N and water availability.The Nc curves derived from both crop dry matter(DM)and T demonstrated N concentration dilution under different conditions with different parameters.The equation Nc=6.43T0.24 established a consistent relationship across varying irrigation regimes.Independent test results indicated that the nitrogen nutrition index(NNI),calculated from this curve,effectively identifies and quantifies the two sources of N deficiency:insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption.Additionally,the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit(Nand)and a positive correlation with relative grain yield(RGY).The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY,as indicated by larger R2 values and smaller AIC.The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status,predicting grain yield,and optimizing N fertilizer management across varying irrigation conditions.These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture:A case study of lettuce production

Aerial imagery is regularly used by crop researchers,growers and farmers to monitor crops during the growing season.To extract meaningful information from large-scale aerial images collected from the field,high-throughput phenotypic analysis solutions are required,which not only produce high-quality measures of key crop traits,but also support professionals to make prompt and reliable crop management decisions.Here,we report AirSurf,an automated and open-source analytic platform that combines modern computer vision,up-to-date machine learning,and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery.To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index(NDVI)sensors,we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals.The tailored platform,AirSurf-Lettuce,is capable of scoring and categorising iceberg lettuces with high accuracy(>98%).Furthermore,novel analysis functions have been developed to map lettuce size distribution across the field,based on which associated global positioning system(GPS)tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest.

Modeling curve dynamics and spatial geometry characteristics of rice leaves

The objective of this work was to develop a dynamic model for describing leaf curves and a the rice leaf （including sub-models for unexpanded leaf blades, expanded leaf blades, and dimensional （3D） dynamic visualization of rice leaves by combining relevant models detailed spatial geometry model of leaf sheaths）, and to realize three- Based on the experimental data of different cultivars and nitrogen （N） rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology. Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline （NURBS） method. Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively. The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology. The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages. These results provide a technical support for related research on virtual rice.

Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index （LAI） is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen （N） status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters （e.g., tiller number （TN） and plant height （H）） were investigated simultaneously. Two N indicators of plant N accumulation （NA） and N nutrition index （NNI） were measured as well. A calibration equation （LAI=1.7787LAI2o00-0.8816, R2=0.870＂） was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI （R2=0.863^＊＊）. For the NNI, the relative LAI （R2=0.808-） was a relatively unbiased variable in the regression than the LAI （R^2=0.33^＊＊）. The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice （NA=29.778LAI-5.9397; NNI=0.7705RLAI＋0.2764）. Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H （LAI=-0.3375（THxHx0.01）2＋3.665（TH×H×0.01）-1.8249, R2=0.875＊＊）. With these models, the N status of rice can be diagnosed conveniently in the field.

Modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat

Extreme high-temperature stress(HTS) associated with climate change poses potential threats to wheat grain yield and quality. Wheat grain protein concentration(GPC) is a determinant of wheat quality for human nutrition and is often neglected in attempts to assess climate change impacts on wheat production. Crop models are useful tools for quantification of temperature impacts on grain yield and quality.Current crop models either cannot simulate or can simulate only partially the effects of HTS on crop N dynamics and grain N accumulation. There is a paucity of observational data on crop N and grain quality collected under systematic HTS scenarios to develop algorithms for model improvement as well as evaluate crop models. Two-year phytotron experiments were conducted with two wheat cultivars under HTS at anthesis, grain filling, and both stages. HTS significantly reduced total aboveground N and increased the rate of grain N accumulation, while total aboveground N and the rate of grain N accumulation were more sensitive to HTS at anthesis than at grain filling. The observed relationships between total aboveground N, rate of grain N accumulation, and HTS were quantified and incorporated into the WheatGrow model. The new HTS routines improved simulation of the dynamics of total aboveground N, grain N accumulation, and GPC by the model. The improved model provided better estimates of total aboveground N, grain N accumulation, and GPC under HTS(the normalized root mean square error was reduced by 40%, 85%, and 80%, respectively) than the original WheatGrow model. The improvements in the model enhance its applicability to the assessment of climate change effects on wheat grain quality by reducing the uncertainties of simulating N dynamics and grain quality under HTS.

Quantifying the effects of short-term heat stress at booting stage on nonstructural carbohydrates remobilization in rice

Extreme heat stress events are becoming more frequent under anticipated climate change,which can have devastating impacts on rice growth and yield.To quantify the effects of short-term heat stress at booting stage on nonstructural carbohydrates(NSC)remobilization in rice,two varieties(Nanjing 41 and Wuyunjing 24)were subjected to 32/22/27°C(maximum/minimum/mean),36/26/31°C,40/30/35°C,and 44/34/39°C for 2,4 and 6 days in phytotrons at booting stage during 2014 and 2015.Yield and yield components,dry matter partitioning index(DMPI),NSC accumulation and translocation were measured and calculated.The results showed that the increase of high-temperature level and duration significantly reduced grain yield by suppressing spikelet number per panicle,seed-setting rate,and grain weight.Heat stress at booting decreased DMPI in panicles,increased DMPI in stems,but had no significant effect on photosynthetic rate.Stem NSC concentration increased whereas panicles NSC concentration,stem NSC translocation efficiency,and contribution of stem NSC to grain yield decreased.Severe heat stress even transformed the stem into a carbohydrate sink during grain filling.The heat-tolerant Wuyunjing 24 showed a higher NSC transport capacity under heat stress than the heat-sensitive Nanjing 41.Heat degree-days(HDD),which combines the effects of the intensity and duration of heat stress,used for quantifying the impacts of heat stress indicates the threshold HDD for the termination of NSC translocation is 9.82°C day.Grain yield was negatively correlated with stem NSC concentration and accumulation at maturity,and yield reduction was tightly related to NSC translocation reduction.The results suggest that heat stress at booting inhibits NSC translocation due to sink size reduction.Therefore,genotypes with higher NSC transport capacity under heat stress could be beneficial for rice yield formation.

Collision detection of virtual plant based on bounding volume hierarchy: A case study on virtual wheat

Visualization of simulated crop growth and development is of significant interest to crop research and production. This study aims to address the phenomenon of organs cross-drawing by developing a method of collision detection for improving vivid 3D visualizations of virtual wheat crops. First, the triangular data of leaves are generated with the tessellation of non-uniform rational B-splines surfaces. Second, the bounding volumes（BVs） and bounding volume hierarchies（BVHs） of leaves are constructed based on the leaf morphological characteristics and the collision detection of two leaves are performed using the Separating Axis Theorem. Third, the detecting effect of the above method is compared with the methods of traditional BVHs, Axis-Aligned Bounding Box（AABB） tree, and Oriented Bounding Box（OBB） tree. Finally, the BVs of other organs（ear, stem, and leaf sheath） in virtual wheat plant are constructed based on their geometric morphology, and the collision detections are conducted at the organ, individual and population scales. The results indicate that the collision detection method developed in this study can accurately detect collisions between organs, especially at the plant canopy level with high collision frequency. This collision detection-based virtual crop visualization method could reduce the phenomenon of organs cross-drawing effectively and enhance the reality of visualizations.

Key variable for simulating critical nitrogen dilution curve of wheat:Leaf area ratio-driven approach

Nitrogen(N)dilution curves,a pivotal tool for N nutrition diagnosis,have been developed using different winter wheat(Triticum aestivum L.)tissues.However,few studies have attempted to establish critical nitrogen(N_(c))dilution curves based on the leaf area ratio(LAR)to improve the monitoring accuracy of N status.In this study,three field experiments using eight N treatments and four wheat varieties were conducted in Jiangsu Province of China from 2013 to 2016.The empirical relationship of LAR with shoot biomass(expressed as dry matter)was developed under different N conditions.The results showed that LAR was a reliable index,which reduced the effects of wheat varieties and years compared with the traditional indicators.The N nutrition index(NNI)based on the LAR approach(NNI-LAR)produced equivalent results to that based on shoot biomass.Moreover,the NNI-LAR better predicted accumulated N deficit and best estimated the relative yield compared with the other two indicator-based NNI models.Therefore,the LAR-based approach improved the prediction accuracy of N_(c),accumulated N deficit,and relative yield,and it would be an optimal choice to conveniently diagnose the N status of winter wheat under field conditions.

SPSI:A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

Rapid and accurate estimation of panicle number per unit ground area(PNPA)in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield.The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored.This study proposed a spectral-textural PNPA sensitive index(SPSI)from unmanned aerial vehicle(UAV)multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading.The effect of background materials on PNPA estimated by textural indices(TIs)was examined,and the composite index SPSI was constructed by integrating the optimal spectral index(SI)and TI.Subsequently,the performance of SPSI was evaluated in comparison with other indices(SI and TIs).The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_([HOM]),TI_([ENT]),and TI_([SEM])among all indices from 8 types of textural features.SPSI,which was calculated by the formula DATT_([850,730,675])+NDTICOR_([850,730]),exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_([850,730,675]),TINDRE_([MEA]),and NDTICOR_([850,730]).For the unified models assembling 2 experimental datasets,the RV^(2) values of SPSI increased by 0.11 to 0.23,and both RMSE and RRMSE decreased by 16.43%to 38.79%as compared to the suboptimal index on each date.These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

Interaction of Genotype,Environment,and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat

The organ-specific critical nitrogen(N_(c))dilution curves are widely thought to represent a new approach for crop nitrogen(N)nutrition diagnosis,N management,and crop modeling.The N_(c) dilution curve can be described by a power function(N_(c)=A_(1)·W^(−A2)),while parameters A_(1) and A_(2) control the starting point and slope.This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions.By using hierarchical Bayesian theory,parameters A_(1) and A_(2) of the organ-specific N_(c) dilution curves for wheat were derived and evaluated under 14 different genotype×environment×management(G×E×M)N fertilizer experiments.Our results show that parameters A_(1) and A_(2) are highly correlated.Although the variation of parameter A_(1) was less than that of A_(2),the values of both parameters can change significantly in response to G×E×M.Nitrogen nutrition index(NNI)calculated using organ-specific N_(c) is in general consistent with NNI estimated with overall shoot N_(c),indicating that a simple organ-specific N_(c) dilution curve may be used for wheat N diagnosis to assist N management.However,the significant differences in organ-specific N_(c) dilution curves across G×E×M conditions imply potential errors in N_(c) and crop N demand estimated using a general N_(c) dilution curve in crop models,highlighting a clear need for improvement in N_(c) calculations in such models.Our results provide new insights into how to improve modeling of crop nitrogen–biomass relations and N management practices under G×E×M.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming.Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes.However,wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background.Existing models for wheat spike detection are typically designed for a specific growth stage.Their adaptability to other growth stages or field scenes is limited.Such models cannot detect wheat spikes accurately caused by the difference in color,size,and morphological features between growth stages.This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage.WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy.Moreover,a Detection Network is designed to improve wheat spike detection capability.A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery.A new micro-scale detection layer is added to the network to extract the features of small spikes.Localization loss is improved by Complete Intersection over Union to reduce the impact of the background.The results show that WheatNet can achieve greater accuracy than classical detection methods.The detection accuracy with average precision of spike detection at the filling stage is 90.1%,while it is 88.6%at the maturity stage.It suggests that WheatNet is a promising tool for detection of wheat spikes.