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 authors present high‑throughput phenotyping workflows that convert UAV‑derived imagery from various sensors into precise, large‑scale plant trait data, covering canopy temperature, morphology, and spectral indices. They also provide open educational resources—including a website and YouTube channel—with step‑by‑step protocols and example scripts to streamline the entire drone data processing pipeline.

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 used comparative transcriptomics across Erysimum species to identify two 2‑oxoglutarate‑dependent dioxygenases, CARD5 and CARD6, responsible for the 14β‑ and 21‑hydroxylation steps in cardenolide biosynthesis in Erysimum cheiranthoides. Knockout mutants lacking these genes accumulated pathway intermediates, and transient expression in Nicotiana benthamiana confirmed their enzymatic functions, while structural modeling pinpointed residues linked to neofunctionalization.

The study used comparative and de‑novo transcriptomic analyses in poplar to uncover plant and fungal gene regulons that govern ectomycorrhizal (ECM) compatibility, distinguishing general fungal‑sensing responses from ECM‑specific pathways. Key findings include modulation of jasmonic acid‑related defenses, coordinated regulation of secretory and cell‑wall remodeling genes, and dynamic expression of the Common Symbiosis Pathway during early and mature symbiosis stages.

The study compared early protective, repair, and stress responses to chronic gamma irradiation in the radiosensitive conifer Norway spruce (Picea abies) and the radiotolerant Arabidopsis thaliana. Norway spruce exhibited growth inhibition, mitochondrial damage, and higher DNA damage at low dose rates, while Arabidopsis maintained growth, showed minimal organelle damage, and activated DNA repair and antioxidant genes even at the lowest dose rates. Transcriptomic analysis revealed that the tolerant species mounts a robust transcriptional response at low doses, whereas the sensitive species only responds at much higher doses.

The study used comparative transcriptomics to examine how Fusarium oxysporum isolates with different lifestyles on angiosperms regulate effector genes during infection of the non‑vascular liverwort Marchantia polymorpha. Core effector genes on fast core chromosomes are actively expressed in the bryophyte host, while lineage‑specific effectors linked to angiosperm pathogenicity are silent, and disruption of a compatibility‑associated core effector alters the expression of other core effectors, highlighting conserved fungal gene networks across plant lineages.

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.