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 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.