Polyploidization is a key mechanism driving plant evolution, environmental adaptation, and trait improvement. This study investigated the effects of artificial chromosome doubling on growth and freezing tolerance in a Liriodendron hybrid (T × T genotype). Tetraploids were efficiently induced during the liquid suspension stage of somatic embryogenesis using oryzalin, with a maximum induction rate of 33.33%. Compared with diploids, 3-month-old tetraploids exhibited compact growth, characterized by increased stem diameter and markedly enlarged leaf area. Tetraploids also showed larger but less dense stomata, enlarged leaf cells, and denser chloroplast organization. Under freezing stress, tetraploids displayed enhanced tolerance accompanied by coordinated transcriptional reprogramming. Transcriptome analysis revealed significant enrichment of defense-related pathways, including plant hormone signaling (JA, SA, and auxin) and MAPK signaling, together with ploidy-specific calmodulin expression, suggesting Ca2+-mediated regulation of hormone responses. Tetraploids preferentially upregulated genes involved in phenylpropanoid metabolism and lignin biosynthesis, promoting structural defense and redox homeostasis, whereas diploids showed stronger induction of flavonoid biosynthesis genes associated with rapid antioxidant protection. These transcriptional patterns were supported by physiological measurements, with tetraploids accumulating higher lignin content and enzyme activities, while diploids accumulated more flavonoids. Overall, tetraploids and diploids adopt distinct freezing adaptation strategies. Polyploidization drives coordinated regulation of hormone signaling and secondary metabolism, enabling improved biomass allocation and freezing tolerance. This study establishes an efficient tetraploid induction system for Liriodendron hybrid and provides mechanistic insights into polyploid-enhanced stress adaptation in woody plants.
Mingyue Xu, Jiajie Feng, Han Wu et al.· Physiologia Plantarum : An I...· 0 citations
Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.
This review synthesizes the significant advancements made over the past decade in understanding JA's role in regulating plant development and mediating responses to environmental stresses, areas that lacked systematic review in previous years.
Rui Wang, Teja Manda, A. Movahedi et al.· Functional Plant Biology· 0 citations