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 examined how prior light‑acclimation influences the fitness and rapid photoprotective reprogramming of Chlamydomonas during transitions between low and high diurnal light intensities. While high‑light‑acclimated cells struggled to grow and complete the cell cycle after shifting to low light, low‑light‑acclimated cells quickly remodeled thylakoid ultrastructure, enhanced photoprotective quenching, and altered photosystem protein levels, recovering chloroplast function within a single day. Transcriptomic and proteomic profiling revealed swift induction of stress‑response genes, indicating high flexibility in diurnal light acclimation.

The study demonstrates that the chromatin remodeler DDM1 is essential for biomass heterosis in Arabidopsis thaliana hybrids, as loss of DDM1 function leads to reduced rosette growth and extensive genotype‑specific transcriptomic and DNA methylation changes. Whole‑genome bisulfite sequencing revealed widespread hypomethylation in ddm1 mutants, while salicylic acid levels were found unrelated to heterosis, indicating that epigenetic divergence, rather than SA signaling, underpins hybrid vigor.

The study applied a progressive, sublethal drought treatment to Arabidopsis thaliana, collecting time‑resolved phenotypic and transcriptomic data. Machine‑learning analysis revealed distinct drought stages driven by multiple overlapping transcriptional programs that intersect with plant aging, and identified high‑explanatory‑power transcripts as biomarkers rather than causal agents.

Salt stress strongly suppresses root growth in Festuca rubra while sparing shoot development. Transcriptome profiling identified over 68,000 differentially expressed genes, with up‑regulated genes enriched in methionine, melatonin, and suberin biosynthesis and down‑regulated genes involved in gibberellin, ABA, and sugar signaling, indicating extensive hormonal and metabolic reprogramming. Paradoxical regulation of gibberellin and ethylene pathways suggests a finely tuned balance between growth and stress responses.

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 examined how single and repeated mechanical disturbances (whole‑pot drops) affect leaf folding in Mimosa pudica, using chlorophyll fluorescence to track photosystem II efficiency and transcriptome profiling to identify responsive genes. A single drop mainly up‑regulated flavonoid biosynthesis genes, whereas multiple drops triggered broader biotic and abiotic stress pathways, indicating a shift in the plant’s gene regulatory network under repeated stress.

The study shows that inoculating Arabidopsis thaliana with the plant‑growth‑promoting bacterium Enterobacter sp. SA187 markedly boosts root and shoot biomass under elevated CO₂, accompanied by altered nitrogen and carbon content and reshaped phytohormone signaling. Transcriptomic and metabolomic analyses reveal activation of salicylic acid, jasmonic acid, and ethylene pathways and enhanced primary metabolism, while the ethylene‑insensitive ein2‑1 mutant demonstrates that the growth benefits are ethylene‑dependent.

The study examined soybean (Glycine max) responses to simultaneous drought and Asian soybean rust infection using combined transcriptomic and metabolomic analyses. Weighted Gene Co-expression Network Analysis identified stress-specific gene modules linked to metabolites, while Copula Graphical Models uncovered sparse, condition‑specific networks, revealing distinct molecular signatures for each stress without overlapping genes or metabolites. The integrative approach underscores a hierarchical, modular defense architecture and suggests targets for breeding multi‑stress resilient soybeans.

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.