Loci discovery, network-guided approach, and genomic prediction for drought tolerance index in a multi-parent advanced generation intercross (MAGIC) cowpea population

Cowpea is a nutrient-dense legume that significantly contributes to the population’s diet in sub-Saharan Africa and other regions of the world.Improving cowpea cultivars to be more resilient to abiotic stress such as drought would be of great importance.The use of a multi-parent advanced generation intercross(MAGIC)population has been shown to be efficient in increasing the frequency of rare alleles that could be associated with important agricultural traits.In addition,drought tolerance index has been reported to be a reliable parameter for assessing crop tolerance to water-deficit conditions.Therefore,the objectives of this study were to evaluate the drought tolerance index for plant growth habit,plant maturity,flowering time,100-seed weight,and grain yield in a MAGIC cowpea population,to conduct genome-wide association study(GWAS)and identify single nucleotide polymorphism(SNP)markers associated with the drought tolerance indices,to investigate the potential relationship existing between the significant loci associated with the drought tolerance indices,and to conduct genomic selection(GS).These analyses were performed using the existing phenotypic and genotypic data published for the MAGIC population which consisted of 305 F8 recombinant inbred lines(RILs)developed at University of California,Riverside.The results indicated that:(1)large variation in drought tolerance indices existed among the cowpea genotypes,(2)a total of 14,18,5,5,and 35 SNPs were associated with plant growth habit change due to drought stress,and drought tolerance indices for maturity,flowering time,100-seed weight,and grain yield,respectively,(3)the network-guided approach revealed clear interactions between the loci associated with the drought tolerance traits,and(4)the GS accuracy varied from low to moderate.These results could be applied to improve drought tolerance in cowpea through marker-assisted selection(MAS)and genomic selection(GS).To the best of our knowledge,this is the first report on marker loci associated with drought tolerance indices in cowpea.

Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages

The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected.In this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing imagery.All the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and F1-score.It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 pixels.The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.