The study demonstrates that limonene, a natural essential‑oil component, strongly inhibits Fusarium oxysporum, the causal agent of potato dry rot, by impairing colony growth, hyphal morphology, spore viability, membrane integrity, and transcription/translation processes, as well as disrupting ion homeostasis. Combined treatments reveal additive effects with mancozeb and synergistic effects with hymexazol, highlighting limonene's potential as an eco‑friendly bio‑fungicide for potato disease management.

The study introduces the Botanical Spectrum Analyzer (BSA), a GUI that incorporates a modified U‑Net deep neural network for accurate segmentation of plant images from RGB and hyperspectral (VNIR and SWIR) data. BSA was tested on wheat, barley, and Arabidopsis datasets, achieving >99% accuracy and F1‑scores above 98%, and markedly outperformed commercial tools on root segmentation tasks.

The study presents a high-quality genome assembly for the nickel hyperaccumulator Noccaea caerulescens and uses it as a reference for comparative transcriptomic analyses across different N. caerulescens accessions and the non‑accumulating relative Microthlaspi perfoliatum. It identifies a limited set of metal transporters (NcHMA3, NcHMA4, NcIREG2, and NcIRT1) whose elevated expression correlates with hyperaccumulation, and demonstrates that frameshift mutations in NcIRT1 can abolish the trait, indicating an ancient, transporter‑driven origin of nickel hyperaccumulation.

The study integrates genome, transcriptome, and chromatin accessibility data from 380 soybean accessions to dissect the genetic and regulatory basis of symbiotic nitrogen fixation (SNF). Using GWAS, TWAS, eQTL mapping, and ATAC-seq, the authors identify key loci, co‑expression modules, and regulatory elements, and validate the circadian clock gene GmLHY1b as a negative regulator of nodulation via CRISPR and CUT&Tag. These resources illuminate SNF networks and provide a foundation for soybean improvement.

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 identifies the transcription factor MdBRC1 as a key inhibitor of bud growth during the ecodormancy phase in apple (Malus domestica), directly regulating dormancy‑associated genes and interacting with the flowering promoter MdFT2 to modulate bud break. Comparative transcriptomic analysis and gain‑of‑function experiments in poplar demonstrate that MdFT2 physically binds MdBRC1, attenuating its repressive activity and acting as a molecular switch for the transition to active growth.

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 introduced full-length SOC1 genes from maize and soybean, and a partial SOC1 gene from blueberry, into tomato plants under constitutive promoters. While VcSOC1K and ZmSOC1 accelerated flowering, all three transgenes increased fruit number per plant mainly by promoting branching, and transcriptomic profiling revealed alterations in flowering, growth, and stress‑response pathways.

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.