The study investigates hormetic responses of tomato (Solanum lycopersicum) seedlings to low‑dose cadmium, demonstrating enhanced growth through morphological, biochemical, and histochemical analyses. Transcriptomic profiling revealed differential expression of oxidoreductase genes, signaling components, and several long non‑coding RNAs (lncRNAs) that generate miRNAs (sly‑MIR396a and sly‑MIR1063g), which modulate target genes to promote growth. In‑silico analyses of lncRNA targets and miRNA precursors provide mechanistic insight into cadmium‑induced hormesis and its potential for crop improvement.

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.

Foliar application of Trichoderma harzianum cell‑free culture filtrates (CF) increased fruit yield, root growth, and photosynthesis in a commercial tomato cultivar under prolonged water deficit in a Mediterranean greenhouse. Integrated physiological, metabolite, and transcriptomic analyses revealed that CF mitigated drought‑induced changes, suppressing about half of water‑stress responsive genes, thereby reducing the plant’s transcriptional sensitivity to water scarcity.

The study examined how individual hormone treatments (auxin, ethylene, gibberellin) influence root architecture and ion accumulation under salt stress in three tomato accessions, revealing species-specific hormonal effects on lateral root development and Na/K ratios. Genetic analyses using Arabidopsis mutants and a tomato ethylene‑perception mutant (nr) identified novel hormonal signaling components that modulate salt stress responses, highlighting potential strategies to improve crop performance.

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 performs a bibliometric analysis of 1,702 Scopus-indexed tomato omics publications over two decades, revealing a rapid surge in output after 2017 and highlighting dominant fields such as biochemistry, genetics, and molecular biology. Citation and co‑authorship network analyses identify key contributions in microRNA research and genome sequencing, major research hubs, and collaborative clusters, while keyword mapping underscores stress response, fruit quality, and immunity as priority topics.

The study characterizes the tomato class B heat shock factor SlHSFB3a, revealing its age‑dependent expression in roots and its role in enhancing lateral root density by modulating auxin homeostasis. Overexpression of SlHSFB3a increases lateral root emergence, while CRISPR‑mediated knockouts produce the opposite phenotype, indicating that SlHSFB3a regulates auxin signaling through repression of auxin repressors and activation of the ARF7/LOB20 pathway.