The study introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.

The study presents a deep‑learning pipeline that uses state‑of‑the‑art convolutional neural networks to automatically estimate the establishment of perennial groundcovers in agricultural research plots from smartphone images. By employing region‑of‑interest markers and deploying the models on AWS SageMaker with a lightweight Django web interface, the approach provides fast, objective, and reproducible assessments that can be adopted by researchers and growers across the Midwest.

The study used an interpretable convolutional neural network to predict ABA responsiveness from proximal promoter sequences in Arabidopsis thaliana, revealing both known ABF-binding motifs and novel regulatory elements. Model performance was boosted by advanced data augmentation, and predicted regulatory regions were experimentally validated using reporter lines, confirming the inferred cis‑regulatory code.

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.

A forward genetic screen in light-grown Arabidopsis seedlings identified the Evening Complex component ELF3 as a key inhibitor of phototropic hypocotyl bending under high red:far-red and blue light, acting upstream of PIF4/PIF5. ELF3 and its partner LUX also mediate circadian regulation of phototropism, and the orthologous ELF3 in Brachypodium distachyon influences phototropism in the opposite direction.

The study investigates the altered timing of the core circadian oscillator gene ELF3 in wheat compared to Arabidopsis, revealing that dawn-specific expression in wheat arises from repression by TOC1. An optimized computational model integrating experimental expression data and promoter architecture predicts that wheat’s circadian oscillator remains robust despite this shift, indicating flexibility in plant circadian network design.

The study tests whether the circadian clock component ELF3 shapes developmental trait heterogeneity, proposing that faster‑developing populations are more heterogeneous early but less so at maturity, whereas slower growers show the opposite pattern. Experiments with Arabidopsis elf3 and barley Hvelf3 mutants confirmed these predictions, showing ELF3 influences hypocotyl and bolting variability via maturation rate, and that smaller barley plants exhibit increased osmotic stress resilience, suggesting ELF3‑driven heterogeneity serves as a bet‑hedging strategy.

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 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.

A biparental Vicia faba mapping population was screened under glasshouse conditions for resistance to a mixture of Fusarium avenaceum and Fusarium oxysporum, revealing several families with moderate to high resistance. Using the Vfaba_v2 Axiom SNP array, a high-density linkage map of 6,755 SNPs was constructed, enabling the identification of a major QTL on linkage group 4 associated with partial resistance to foot and root rot.