The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

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 used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

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 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 used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.

The study introduces ENTRAP-seq, a high‑throughput in‑planta assay that couples protein‑coding libraries with a nuclear magnetic sorting‑based reporter to multiplexively assess transcriptional regulatory activity of thousands of protein variants. Using this platform and machine‑learning analysis, the authors screened 1,495 plant viral proteins, uncovering numerous novel regulatory domains, and applied machine‑guided, semi‑rational design to modify the activity of a plant transcription factor.

The study used phylogeny‑based analyses of 36 legume genomes and a newly created multiparent advanced generation intercross (MAGIC) population of common bean to predict and characterize genome‑wide deleterious mutations. Machine‑learning integration of conservation and protein features identified thousands of potentially deleterious sites, whose variation correlated negatively with flowering time, maturity, and yield, highlighting the impact of genetic load on breeding performance.

The authors introduce S²-PepAnalyst, a web-based tool that leverages plant-specific datasets and advanced machine learning to predict small signaling peptides (SSPs) with 99.5% accuracy and minimal false negatives. By integrating protein language models, geometric‑topological analysis, and reinforcement learning, the tool surpasses existing predictors such as SignalP 6.0 in classifying peptide families like CLE and RALF.

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.