The authors applied semi‑supervised deep‑learning to super‑resolution images of modern and fossil grass pollen, training convolutional neural networks to extract abstract morphological features. These features were used to quantify past grass community diversity and C3:C4 ratios in a 25,000‑year lake‑sediment record, revealing a marked diversity loss during the last glacial and a gradual decline of C4 grasses in the Holocene.

The authors introduce AdaPoinTr, a geometry-aware transformer that predicts the alpha‑shape of coniferous tree crowns from incomplete terrestrial or mobile laser‑scanning point clouds, focusing on crown reconstruction rather than full tree completion. Trained on synthetically generated partial crowns, the model consistently improves crown shape similarity and reduces height estimation bias across three diverse forest datasets, providing a cost‑effective solution for enhanced 3D forest structural monitoring.

The review examines the genetic networks governing spikelet number per spike (SNS) in wheat, highlighting how the balance between inflorescence meristem activity and the timing of terminal spikelet transition determines yield potential. It discusses how mutations affecting meristem identity can create supernumerary spikelets, the trade-offs of such traits, and recent advances using spatial transcriptomics, single‑cell analyses, and multi‑omics to identify new SNS genes for breeding.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study assessed how well common deep learning models (ResNet, EfficientNet, Inception, MobileNet) generalize across different tomato pest and disease image datasets. While models performed well on the dataset they were trained on, they suffered substantial accuracy drops when applied to other datasets, indicating that architectural changes alone cannot overcome dataset variability. The results highlight the necessity for more diverse, representative training data to improve real-world deployment of PPD diagnostic tools.

The study applied dual-species spatial transcriptomics at single-cell resolution to map plant and fungal gene activity in rice roots colonized by Rhizophagus irregularis, revealing transcriptional heterogeneity among morphologically similar arbuscules. By pioneering an AM-inducible TRAP-seq using stage‑specific promoters, the authors uncovered stage‑specific reprogramming of nutrient transporters and defence genes, indicating dynamic regulation of nutrient exchange and arbuscule lifecycle.

The study demonstrates that hyperspectral imaging can non‑destructively differentiate active nitrogen‑fixing root nodules from non‑fixing nodules and root tissue based on distinct spectral signatures. By integrating deep‑learning models, the authors created an automated nodule counting pipeline that works across multiple legume species and growth conditions, eliminating labor‑intensive manual counting and reliably detecting nodules within dense root systems.

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 applied spatial transcriptomics to map the transcriptional landscape of wheat (Triticum aestivum) inflorescences during spikelet development, revealing two distinct regions—a RAMOSA2‑active primordium and an ALOG1‑expressing boundary. Developmental assays showed that spikelets arise from meristematic zones accompanied by vascular rachis formation, identifying key regulators that could be targeted to improve spikelet number and yield.

An optimized workflow was developed to apply the Xenium in situ sequencing platform to formalin‑fixed paraffin‑embedded (FFPE) sections of Medicago truncatula roots and nodules, incorporating customized tissue preparation, probe design, and imaging to overcome plant‑specific challenges such as cell wall autofluorescence. The protocol was validated across nodule developmental stages using both a 50‑gene panel for mature cell identity and an expanded 480‑gene panel covering multiple cell types, providing a scalable high‑resolution spatial transcriptomics method adaptable to other plant systems.