The study applied Spatial Analysis of Field Trials with Splines (SpATS) and Neighbor Genome-Wide Association Study (Neighbor GWAS) to barley field data, revealing that neighboring genotypes contribute to spatial variation in disease damage. Neighbor GWAS identified variants on chromosome 7H that modestly affect net form net blotch and scald resistance, suggesting that genotype mixtures could mitigate pest damage.

The study presents GenoRetriever, an interpretable deep learning framework trained on STRIPE-seq data from soybean and other crops, that predicts transcription start site locations and usage by identifying 27 core promoter motifs. Validation using in silico motif insertions, saturation mutagenesis, and CRISPR‑Cas9 promoter editing demonstrates high predictive accuracy and reveals domestication‑driven motif usage shifts and lineage‑specific effects. The tool is provided via a web server for promoter analysis and design, offering a new resource for plant functional genomics and crop improvement.

The study evaluated natural genetic variation in non-photochemical quenching and photoprotection across 861 sorghum accessions grown in the field over two years, revealing moderate to high broad-sense heritability for chlorophyll fluorescence traits. By integrating genome-wide association studies (GWAS) with transcriptome-wide association studies (TWAS) and covariance analyses, the authors identified 110 high-confidence candidate genes underlying photoprotection, highlighting a complex, polygenic architecture for these traits.

The study examined salt-induced alterations in root system architecture across a diverse panel of wild and cultivated tomato accessions, identifying tolerant varieties with distinct lateral root strategies. By combining Bulk Segregant Analysis of an F2 population with GWAS, the authors pinpointed 22 candidate genes, further narrowing to two key regulators through RNA‑Seq and functional assays involving ethylene and ROS profiling. These findings reveal genetic targets for improving salt resilience in tomato root development.

The Global Wheat Dataset Consortium released a comprehensive semantic segmentation dataset (GWFSS) of wheat organs across developmental stages, comprising 1,096 fully annotated images and 52,078 unannotated images from 11 institutions. Models based on DeepLabV3Plus and Segformer were trained, with Segformer achieving ≈90% mIoU for leaves and spikes but lower precision (54%) for stems, while also enabling weed exclusion and discrimination of necrotic, senescent, and residue tissues.

The study examined how genetic variation among 181 wheat (Triticum aestivum) lines influences root endophytic fungal communities using ITS2 metabarcoding. Heritability estimates and GWAS identified 11 QTLs linked to fungal clade composition, highlighting genetic control of mycobiota, especially for biotrophic AMF. These findings suggest breeding can be used to modulate beneficial root-fungal associations.

The study surveyed vegetative water use and life‑history traits across Arabidopsis thaliana ecotypes in both controlled and outdoor environments to assess how climatic history shapes water‑use strategies. Trait‑climate correlations and genome‑wide association analyses uncovered that ecotypes from warmer regions exhibit higher water use, and identified MYB59 as a key gene whose temperature‑linked alleles affect water consumption, a finding validated using myb59 mutants. These results indicate that temperature‑driven adaptive differentiation partly explains intraspecific water‑use variation.

The study introduces an in-soil fiber Bragg grating (FBG) sensing system that continuously records three-dimensional strain from growing pseudo-roots, enabling non‑destructive monitoring of root architecture. Using two ResNet models, the system predicts root width and depth with over 90% accuracy, and performance improves to 96‑98% after retraining on data from actual corn (Zea mays) roots over a 30‑day period. This prototype demonstrates potential for scalable, real‑time root phenotyping and broader soil environment sensing.

The study performed ionomic profiling and genome-wide association studies on a diverse panel of pearl millet infield across two seasons to uncover genetic factors controlling nutrient acquisition. Soil analyses revealed stable depth-dependent patterns for phosphorus and zinc, while leaf ion concentrations showed high heritability and associations with root and agronomic traits. Integrating GWAS with gene expression data identified candidate ion transport/homeostasis genes for breeding nutrient-efficient, climate-resilient millet.