The study demonstrates that short‑term low phosphate treatment delays leaf senescence in rice by increasing photosynthetic pigments, enhancing antioxidant enzyme activities, and reducing oxidative damage, whereas high phosphate accelerates senescence. CRISPR/Cas9 editing of MIR827 to lower Pi levels also postpones senescence, while overexpression of MIR827 or MIR399, which raises Pi, speeds it up. Transcriptomic profiling reveals coordinated changes in senescence‑associated and metabolic pathways underlying the low‑phosphate response.

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 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.

Integrating physiological, transcriptomic, and cellular analyses, the study shows that olive fruit abscission zones undergo lignification, alkalization, and extensive cell‑wall remodeling during natural maturation and after ethephon treatment. A set of 733 FAZ‑specific genes, including β‑1,3‑glucanases, pectate lyases, and pH‑regulating transporters, were identified, and increased glucanase activity together with reduced plasmodesmata callose suggest enhanced intercellular communication facilitates organ detachment in this non‑climacteric fruit.

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 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 demonstrates that limonene, a natural essential‑oil component, strongly inhibits Fusarium oxysporum, the causal agent of potato dry rot, by impairing colony growth, hyphal morphology, spore viability, membrane integrity, and transcription/translation processes, as well as disrupting ion homeostasis. Combined treatments reveal additive effects with mancozeb and synergistic effects with hymexazol, highlighting limonene's potential as an eco‑friendly bio‑fungicide for potato disease management.

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 examined how soil phosphorus and nitrogen availability influence wheat root-associated arbuscular mycorrhizal fungal (AMF) communities and the expression of mycorrhizal nutrient transporters. Field sampling across two years combined with controlled pot experiments showed that P and N jointly affect AMF colonisation, community composition (with Funneliformis dominance under high P), and regulation of phosphate, ammonium, and nitrate transporters. Integrating metabarcoding and RT‑qPCR provides a framework to assess AMF contributions to crop nutrition.