Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.

Transient response of a concrete tunnel in an elastic rock with imperfect contact

The two-dimensional transient response of an imperfect bonded circular lined pipeline lying in an elastic infinite medium is investigated.Imperfect boundary conditions between the surrounding elastic rock and the tunnel are modelled with a two-linear-spring design.The novelty of the manuscript consists in studying at the same time transient regimes and imperfect bonded interfaces for simulating the dynamic response of a tunnel embedded in an elastic infinite rock.Wave propagation fields in tunnel and rock are expressed in terms of infinite Bessel and Hankel series.To solve the transient problem,the Laplace transform and the associated Durbin’s algorithm are performed.To exhibit the dynamic responses,influences of various parameters such as the quality of the interface conditions and the thickness of the lining are presented.The dynamic hoop stresses and the solid displacements of both the tunnel and the rock are also proposed.

Using irrigation intervals to optimize water-use efficiency and maize yield in Xinjiang,northwest China

Worldwide, scarce water resources and substantial food demands require efficient water use and high yield.This study investigated whether irrigation frequency can be used to adjust soil moisture to increase grain yield and water use efficiency(WUE) of high-yield maize under conditions of mulching and drip irrigation.A field experiment was conducted using three irrigation intervals in 2016: 6, 9, and 12 days(labeled D6, D9, and D12) and five irrigation intervals in 2017: 3, 6, 9, 12, and 15 days(D3, D6, D9, D12, and D15).In Xinjiang, an optimal irrigation quota is 540 mm for high-yield maize.The D3, D6, D9, D12, and D15 irrigation intervals gave grain yields of 19.7, 19.1–21.0, 18.8–20.0, 18.2–19.2, and 17.2 Mg ha^-1 and a WUE of 2.48, 2.53–2.80, 2.47–2.63, 2.34–2.45, and 2.08 kg m-3, respectively.Treatment D6 led to the highest soil water storage, but evapotranspiration and soil-water evaporation were lower than other treatments.These results show that irrigation interval D6 can help maintain a favorable soil-moisture environment in the upper-60-cm soil layer, reduce soilwater evaporation and evapotranspiration, and produce the highest yield and WUE.In this arid region and in other regions with similar soil and climate conditions, a similar irrigation interval would thus be beneficial for adjusting soil moisture to increase maize yield and WUE under conditions of mulching and drip irrigation.

Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model:Impact of the Spatial Resolution

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices.The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput,accuracy,and access to plant localization.However,high-resolution images are required to detect the small plants present at the early stages.This study explores the impact of image ground sampling distance(GSD)on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm.Data collected at high resolution(GSD≈0:3 cm)over six contrasted sites were used for model training.Two additional sites with images acquired both at high and low(GSD≈0:6 cm)resolutions were used to evaluate the model performances.Results show that Faster-RCNN achieved very good plant detection and counting(rRMSE=0:08)performances when native high-resolution images are used both for training and validation.Similarly,good performances were observed(rRMSE=0:11)when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images.Conversely,poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution.Training on a mix of high-and low-resolution images allows to get very good performances on the native high-resolution(rRMSE=0:06)and synthetic low-resolution(rRMSE=0:10)images.However,very low performances are still observed over the native low-resolution images(rRMSE=0:48),mainly due to the poor quality of the native low-resolution images.Finally,an advanced super resolution method based on GAN(generative adversarial network)that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images.Results show some significant improvement(rRMSE=0:22)compared to bicubic upsampling approach,while still far below the performances achieved over the native high-resolution images.

Scoring Cercospora Leaf Spot on Sugar Beet:Comparison of UGV and UAV Phenotyping Systems

Selection of sugar beet(Beta vulgaris L.)cultivars that are resistant to Cercospora Leaf Spot(CLS)disease is critical to increase yield.Such selection requires an automatic,fast,and objective method to assess CLS severity on thousands of cultivars in the field.For this purpose,we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle(UGV)under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle(UAV)under passive illumination.Several variables are extracted from the images(spot density and spot size for UGV,green fraction for UGV and UAV)and related to visual scores assessed by an expert.Results show that spot density and green fraction are critical variables to assess low and high CLS severities,respectively,which emphasizes the importance of having submillimeter images to early detect CLS in field conditions.Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV-and UAV-derived scores.While UGV shows the best estimation performance,UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired.Advantages and limitations of UGV,UAV,and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.

A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images

Multispectral observations from unmanned aerial vehicles(UAVs)are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status.However,the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas.Increasing the throughput of data acquisition while not degrading the ground sample distance(GSD)is,therefore,a critical issue to be solved.We propose here a new image acquisition configuration based on the combination of two focal length(f)optics:an optics with f=4:2 mm is added to the standard f=8 mm(SS:single swath)of the multispectral camera(DS:double swath,double of the standard one).Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude.The DS flight plan was designed to get 80%overlap with the 4.2 mm optics,while the SS one was designed to get 80%overlap with the 8 mm optics.As a result,the time required to cover the same area is halved for the DS as compared to the SS.The georeferencing accuracy was improved for the DS configuration,particularly for the Z dimension due to the larger view angles available with the small focal length optics.Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one.However,for both the DS and SS configurations,degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

The number of leaves at a given time is important to characterize plant growth and development.In this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images.The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning models.The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation techniques.Furthermore,the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips.The method is claimed to be self-supervised since no manual labeling is required for model training.The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems.The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.

Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images

The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy.Leaves orientation is an important architectural trait determining maize canopies light interception.Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition.The goal of the present study is 2-fold:firstly,to propose and validate an automatic algorithm(Automatic Leaf Azimuth Estimation from Midrib detection[ALAEM])based on leaves midrib detection in vertical red green blue(RGB)images to describe leaves orientation at the canopy level;and secondly,to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities(6 and 12 plants.m^(−2))and 2 row spacing(0.4 and 0.8 m)over 2 different sites in southern France.The ALAEM algorithm was validated against in situ annotations of leaves orientation,showing a satisfactory agreement(root mean square[RMSE]error=0.1,R^(2)=0.35)in the proportion of leaves oriented perpendicular to rows direction across sowing patterns,genotypes,and sites.The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition.In both experiments,a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1(6 plants.m^(−2),0.4 m row spacing)towards 8(12 plants.m^(−2),0.8 m row spacing).Significant differences among the 5 cultivars were found,with 2 hybrids exhibiting,systematically,a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity.Differences in leaves orientation were also found between experiments in a squared sowing pattern(6 plants.m^(−2),0.4 m row spacing),indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.

LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci

The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard.High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period.We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross,between IJ119,a local genitor,and Divona,both in summer and in winter,using several methods:fresh pruning wood weight,exposed leaf area calculated from digital images,leaf chlorophyll concentration,and LiDAR-derived apparent volumes.Using high-density genetic information obtained by the genotyping by sequencing technology(GBS),we detected 6 regions of the grapevine genome[quantitative trait loci(QTL)]associated with the variations of the traits in the progeny.The detection of statistically significant QTLs,as well as correlations(R^(2))with traditional methods above 0.46,shows that LiDAR technology is effective in characterizing the growth features of the grapevine.Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high,above 0.66,and stable between growing seasons.These variables provided genetic models explaining up to 47%of the phenotypic variance,which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements.Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.

A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020

The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involved,and research hotspots of these products.Its aim is to intrigue researchers and stimulate new research direction.Based on literature data from the Web of Science(WOS)and associated funding information,we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis(SNA)methods.We drew the following conclusions:(1)research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9%in the number of publications.(2)Researchers from China and the USA are the backbone of this research area,among which the Chinese Academy of Sciences(CAS)is the core research institution.(3)Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology.(4)Ecology,crop production estimation,algorithm improvement,and validation are the hotspots of these studies.(5)Broadening the research field,improving the algorithms,and overcoming existing difficulties in heterogeneous surface,scale effects,and complex terrains will be the trend of future research.Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.

Global Wheat Head Detection(GWHD)Dataset:A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

Global Wheat Head Detection Challenges:Winning Models and Application for Head Counting

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial applications.We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.We analyze the winning challenge solutions in terms of their robustness when applied to new datasets.We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels

The bidirectional reflectance distribution function(BRDF)of the land surface contains information relating to its physical structure and composition.Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies.However,the original kerneldriven models,which many operational BRDF/Albedo algorithms have adopted,do not explicitly consider the heterogeneity within heterogeneous pixels,which may result in large fitting residuals.In this paper,we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares(DWLS)inversion approach.The performance of DWLS and the traditional ordinary least squares(OLS)inversion approach were compared using simulated data.We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations.The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels.The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands.The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations.These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.

Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods

The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.