The study reveals that each individual plant possesses a statistically unique leaf appearance that can be discriminated using convolutional neural network (CNN) based deep learning, enabling a "plant face" recognition concept. Applications demonstrated include distinguishing leaves from the same species/cultivar, analyzing leaflet positional patterns on compound leaves, assessing bilateral symmetry, and detecting morphological differences linked to stem chirality, highlighting the encoding of genetic, environmental, and developmental information in leaf morphology.

The study examined how polyploidy, hybridization, and incomplete lineage sorting affect phylogenetic reconstructions in the genus Packera, evaluating several published paralog‑processing pipelines. Results showed that the choice of orthology and paralog handling methods markedly altered tree topology, time‑calibrated phylogenies, biogeographic histories, and detection of ancient reticulation, underscoring the need for careful methodological selection alongside comprehensive taxon sampling.

The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

The study performed comparative gene expression profiling across four rice accessions—from shoot apical meristem to primordia stage P5—to delineate developmental and photosynthetic transitions in leaf development. By integrating differential expression and gene regulatory network analyses, the authors identified stage-specific regulatory events and key transcription factors, such as RDD1, ARID2, and ERF3, especially in the wild rice Oryza australiensis, offering a comprehensive framework for optimizing leaf function.

The study functionally characterizes three tomato CNR/FWL proteins (SlFWL2, SlFWL4, SlFWL5) and demonstrates that SlFWL5 localizes to plasmodesmata, where it regulates leaf size and morphology by promoting cell expansion likely through cell‑to‑cell communication. Gain‑ and loss‑of‑function transgenic tomato lines reveal that SlFWL5 is a key regulator of organ growth via modulation of plasmodesmatal signaling.

The study constructs a ~1‑million‑cell single‑nuclei transcriptome atlas of Arabidopsis leaves to reveal that drought stress accelerates transcriptional programs associated with maturation and aging, thereby limiting leaf growth in proportion to stress intensity. Targeted upregulation of FERRIC REDUCTION OXIDASE 6 in mesophyll cells partially rescues leaf growth under drought, demonstrating the functional relevance of these transcriptional changes.