Analysis of the Effects of the Vrn-1 and Ppd-1 Alleles on Adaptive and Agronomic Traits in Common Wheat (Triticum aestivum L.).

Wheat heading time is primarily governed by two loci: VRN-1 (response to vernalization) and PPD-1 (response to photoperiod). Five sets of near-isogenic lines (NILs) were studied with the aim of investigating the effect of the aforementioned genes on wheat vegetative period duration and 14 yield-related traits. Every NIL was sown in the hydroponic greenhouse of the Institute of Cytology and Genetics, SB RAS. To assess their allelic composition at the VRN-1 and PPD-1 loci, molecular markers were used. It was shown that HT in plants with the Vrn-A1vrn-B1vrn-D1 genotype was reduced by 29 and 21 days (p < 0.001) in comparison to HT in plants with the vrn-A1Vrn-B1vrn-D1 and the vrn-A1vrn-B1Vrn-D1 genotypes, respectively. In our study, we noticed a decrease in spike length as well as spikelet number per spike parameter for some NIL carriers of the Vrn-A1a allele in comparison to carriers of the Vrn-B1 allele. PCA revealed three first principal components (PC), together explaining more than 70% of the data variance. Among the studied genetic traits, the Vrn-A1a and Ppd-D1a alleles showed significant correlations with PCs. Regarding genetic components, significant correlations were calculated between PC3 and Ppd-B1a (-0.26, p < 0.05) and Vrn-B1 (0.57, p < 0.05) alleles. Thus, the presence of the Vrn-A1a allele affects heading time, while Ppd-D1a is associated with plant height reduction.

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.

Introduction: Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks. Methods: Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images. Results: For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.