The study generated a phenotypic dataset for 550 Lactuca accessions, including 20 wild relatives, and applied an iterative two‑step GWAS using a jointly processed SNP set for cultivated lettuce (L. sativa) and its wild progenitor (L. serriola) to dissect trait loci. Known and novel QTLs for anthocyanin accumulation, leaf morphology, and pathogen resistance were identified, with several L. serriola‑specific QTLs revealing unique genetic architectures, underscoring the breeding value of wild lettuce species.

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 used chlorophyll fluorescence imaging to map non-photochemical quenching (NPQ) gradients along barley leaf axes and found heat stress attenuates NPQ induction, revealing spatial heterogeneity in stress responses. Genome‑wide association and transcriptomic analyses identified candidate genes, notably HORVU.MOREX.r3.3HG0262630, that mediate region‑specific heat responses, highlighting pathways for improving cereal heat resilience.

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 applied Spatial Analysis of Field Trials with Splines (SpATS) and Neighbor Genome-Wide Association Study (Neighbor GWAS) to barley field data, revealing that neighboring genotypes contribute to spatial variation in disease damage. Neighbor GWAS identified variants on chromosome 7H that modestly affect net form net blotch and scald resistance, suggesting that genotype mixtures could mitigate pest damage.