Search Results (693)

Vegetatively propagated crops, including cassava, sweet potato, banana, and potato, are susceptible to mixed-pathogen infections resulting from the continuous use of clonal planting material and infrequent seed replacement. A diverse array of viruses, bacteria, and fungi can accumulate within these materials over successive cultivation cycles, precipitating seed degeneration and complex disease syndromes that complicate diagnosis and management. Mixed infections frequently trigger synergistic interactions that exacerbate disease severity and yield losses. This review synthesizes data on mixed-pathogen complexes in vegetatively propagated crops, with particular focus on vascular and systemically colonizing pathogens and analyzing starch crops to highlight the epidemiological, biological, and ecological drivers of synergism and antagonism. Furthermore, the review examines host defense responses during coinfection, including the modulation of plant immune pathways, and evaluates how interpathogen dynamics influence pathological outcomes. Although advancements in molecular diagnostics—notably next-generation sequencing and metagenomics—have revolutionized the detection of mixed infections, they have also introduced challenges in differentiating causal agents from commensal microorganisms. Finally, we discuss the implications for integrated disease management, emphasizing clean seed systems, resistance breeding, and phenotyping strategies tailored to multipathogen environments. The dynamics of mixed infections is critical for resilient and sustainable management strategies amidst increasingly complex agricultural and climatic shifts. Full article

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based on the fine-tuned Prithvi foundation model for direct LAI retrieval from cloud-contaminated 30 m Harmonized Landsat and Sentinel-2 imagery. By mapping inputs directly to Hi-GLASS reference labels, the proposed architecture processes cloud contamination and vegetation signals simultaneously and circumvents the error propagation inherent in cascaded retrieval pipelines. Results demonstrate that the end-to-end LAI retrieval model significantly outperforms cascaded variants, achieving a superior R² (0.78) and lower RMSE (0.57). Furthermore, predictive accuracy exhibits a distinct U-shaped trajectory relative to the temporal mean cloud fraction, reaching an inflection point at 50–60% occlusion, which highlights the model’s implicit regularization capacity under severe atmospheric interference. This work establishes that direct feature learning with foundation models offers a more robust and streamlined pathway for generating continuous biophysical products from imperfect optical observations, prioritizing quantitative fidelity over artificial perceptual sharpness. Full article

Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e.g., drone-embedded chips, satellite on-board processors) has become an urgent challenge—existing deep learning methods, despite high accuracy, are hindered by massive parameters and computational costs that preclude deployment on resource-constrained embedded hardware. To address this, we focus on lightweight (i.e., low parameter count and low computational cost) RSCD network design, targeting three critical bottlenecks: blurred boundaries of changed regions, missed detection of small objects, and insufficient computational efficiency. We propose ATCFNet (Adjacent-Temporal Cross Fusion Network), featuring a three-step progressive feature optimization strategy: (1) the Adjacent Feature Aggregation Module (AFAM) enhances shallow geometric details via lateral three-stage fusion to compensate for lightweight backbones; (2) the Temporal Attention Cross Module (TACM) integrates cross-level feature propagation and Convolutional Block Attention Module (CBAM) for collaborative optimization of high-level semantics and low-level details; and (3) the Efficient Guidance Module (EGM) establishes long-range dependencies using shared change priors and lightweight self-attention to suppress internal voids in changed regions. Experiments on three public datasets (LEVIR-CD, HRCUS, SYSU-ChangeDet) demonstrate that ATCFNet achieves state-of-the-art accuracy with merely 3.71 million (M) parameters and 3.0 billion (G) floating-point operations (FLOPs)—F1-scores of 91.46%, 77.05%, and 83.53%, significantly outperforming 18 existing methods in most indicators. Notably, it excels in edge integrity (avoiding jagged blurring at change boundaries) and small-target detection in high-resolution urban scenes. This study provides an efficient and reliable lightweight solution for edge computing scenarios such as real-time drone inspection and satellite on-board intelligent processing. Full article

The genus Passiflora includes species important for fruit production, ornamental value, and breeding programs. Conventional methods, such as seed propagation and vegetative cuttings, face challenges like genetic heterogeneity, pathogen transmission, and long juvenile phases, limiting large-scale cultivation and breeding efficiency. In vitro culture technologies are essential for clonal propagation, germplasm conservation, and improving Passiflora species using biotechnology. This review critically evaluates current progress in micropropagation and regeneration systems in Passiflora spp. and examines the prospects of haploid and doubled haploid technologies as future breeding tools. Unlike previous reviews, which primarily focus on summarizing tissue culture protocols, this study integrates regeneration biology, developmental constraints, and emerging biotechnological approaches to provide a broader framework for research. Additionally, this review offers a comparative analysis of various regeneration systems across Passiflora species and highlights the challenges of genotype-dependent methods. By synthesizing recent advancements in haploid technology, it provides new insights into the potential for accelerating breeding programs in Passiflora, a field where robust protocols are still lacking. Full article

Garlic (Allium sativum L.) is an important vegetable with high nutritional and medicinal value. Its reliance on asexual reproduction causes variety degradation and low propagation efficiency, severely limiting the garlic industry. This study established an efficient shoot organogenesis system for the garlic cultivar Gailiangsuan through optimizing tissue culture protocols. Various explants, media, and hormone combinations were tested to determine the optimal conditions for improving in vitro propagation efficiency. The results demonstrated that for garlic inflorescence explants, immature inflorescences protruding 0–5 cm from the leaf sheath or not protruding were the optimal explants, exhibiting the highest shoot number. The Gamborg B5 (B₅) medium supplemented with a hormone combination of zeatin (ZT) 2 mg/L + indole-3-acetic acid (IAA) 0.05–0.2 mg/L at the first stage and ZT 0.2 mg/L + IAA 0.05 mg/L at the second stage was the most effective for improving in vitro propagation efficiency. For in vitro stem disc culture, the B₅ medium containing 6-benzylaminopurine (6–BA) 2 mg/L + 1-naphthaleneacetic acid (NAA) 0.2 mg/L was optimal. Moreover, a sucrose concentration of 7% was identified as optimal for microbulb development, resulting in significantly larger microbulbs than those grown in a medium with 3% sucrose. These results provide a technical basis for large-scale production of high-quality garlic seedlings. Full article

The Landslide susceptibility assessment in complex mountainous terrain is frequently limited by static modelling frameworks that inadequately capture nonlinear deformation characteristics and temporally evolving hazard processes. To bridge this gap, a continuous-time dynamic assessment framework is proposed for Shazhou Town, Sichuan Province, integrating slowly moving scatterogram interferometric radar (S(BAS-InSAR))-derived deformation time series with Liquid Neural Networks (LNN). By incorporating a liquid time-constant architecture, the model accommodates irregular temporal sampling and captures non-stationary environmental responses through adaptive multimodal feature fusion. Analysis of long-term SBAS-InSAR observations (January 2021–May 2025) reveals distinctive deformation patterns, identifying eight active zones with maximum annual displacement rates of 107 mm yr⁻¹ and cumulative subsidence of 535.7 mm, which serve as critical dynamic inputs for the susceptibility model. Comparative experiments demonstrate that the LNN framework outperforms benchmark models (including LSTM, GRU, Random Forest, and SVM), achieving a coefficient of determination (R²) of 0.95 and an RMSE of 0.50. Furthermore, multi-temporal validation against 189 historical landslide records (2008–2025) confirms the model’s robustness, yielding a 91.5% capture rate within high-susceptibility zones. Interpretability analyses via SHAP and Layer-wise relevance propagation identify rainfall and vegetation cover as dominant dynamic controls, while characterising a distinct slope threshold effect at approximately 20°. These findings demonstrate that explicit continuous-time neural modelling enables physically consistent representation of irregular satellite acquisition intervals and delayed hydro-mechanical responses, thereby advancing landslide susceptibility assessment from static spatial classification toward dynamic state evolution inference under asynchronous Earth observation data streams. Full article

Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an improved water cloud model (IWCM) with machine learning algorithms. Multi-modal unmanned aerial vehicle (UAV) experiments were conducted during the heading stage of winter wheat over two consecutive years (2024–2025) using a synchronized system equipped with a miniature synthetic aperture radar (MiniSAR) and a multi-spectral sensor. The core innovation of the proposed framework lies in the IWCM, which explicitly decouples vegetation and soil scattering contributions by incorporating fractional vegetation cover, thereby deriving physically meaningful soil backscatter coefficients from complex microwave signals. Unlike traditional methods that treat remote sensing variables as black box inputs, our approach employed these physics-derived features to guide data-driven modeling. Four feature input schemes including spectral reflectance, vegetation indices, MiniSAR polarimetric parameters, and their multi-source fusion were systematically evaluated using back propagation neural network (BPNN) and random forest (RF) regressors. The results demonstrated that the proposed framework significantly enhances retrieval performance. Notably, the RF model driven by spectral band reflectance within this physically constrained architecture achieved optimal accuracy, with a coefficient of determination (R²) of 0.865, a mean absolute error (MAE) of 0.0152, and a root mean square error (RMSE) of 0.0197. Compared to purely empirical approaches, the IWCM significantly improved the physical interpretability of microwave polarimetric characteristics, enabling the multi-source data fusion to better represent the interactions among vegetation, soil, and microwave scattering. This study demonstrated that integrating mechanistic models with multi-source UAV remote sensing data not only improves soil water content retrieval accuracy in winter wheat fields but also provides a valuable reference for developing operationally applicable and physically interpretable farmland soil water content monitoring systems. Full article

Urban wetlands are acoustic hotspots where vegetation structure, hydrological dynamics, and anthropogenic noise interact, yet multi-season assessments of how vegetation influences avian soundscapes are limited. This study explored bird soundscape dynamics across forest, open forest grassland, and meadow habitats in Nanjing Xinjizhou National Wetland Park, eastern China, using passive acoustic monitoring during spring and autumn 2023. Twelve sampling points (four per vegetation type) were established, and six acoustic indices were calculated, including the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bioacoustic Index (BIO), Normalized Difference Soundscape Index (NDSI), and Acoustic Entropy Index (H). were calculated from 48-h recordings each season. Random forest models and redundancy analysis assessed the relationships between acoustic indices, fine-scale vegetation parameters (e.g., crown width, tree height, species richness), and anthropogenic factors (e.g., distance to roads/trails, surface hardness). Vegetation structure, particularly crown width, was the primary driver of avian acoustic diversity, with broad-crowned forests consistently exhibiting the highest acoustic complexity. In spring, anthropogenic factors such as trail and road proximity dominated soundscape variation, suppressing biological sounds. In autumn, with reduced human presence, vegetation structure emerged as the dominant factor, while bioacoustic activity remained elevated despite reduced peaks in acoustic complexity. Proximity to roads increased low-frequency (1–2 kHz) noise and suppressed mid-frequency (4–8 kHz) bird vocalizations, but trees with crown widths ≥4 m maintained higher acoustic diversity even near disturbance sources. This study demonstrates that vegetation structure mediates both resource availability and sound propagation, buffering the effects of anthropogenic disturbance in frequency-specific ways. Multi-season sampling is crucial for understanding the dynamic interplay between vegetation phenology and human activity that shapes urban wetland soundscapes. Full article

Clonal propagation of rust-resistant sugar pine (Pinus lambertiana Dougl.) is currently limited by extreme rooting recalcitrance and highly variable donor responses to nursery management. This study identified seed zone-specific nutritional sensitivities and evaluated rooting success; we hypothesized that northern seed sources would exhibit greater sensitivity to high nutrient loads and that stable microclimates would outperform high-intensity rooting systems. In Study 1, seedlings from five United States Department of Agriculture seed zones were grown for 27 weeks in five nutrient solutions (tap-water control, modified Hoagland, Foliage-Pro^®, Andrejow, and FloraNova^®) spanning 0.72–3.00 dS m⁻¹. The nutrient-rich Foliage-Pro^® and FloraNova^® solutions defined the upper end of the nutrient-intensity range and revealed strong seed zone contrasts: northern zones (526, 550) showed marked sensitivity, with survival declining from 70 to 100% in the control to 15–40% under the highest-EC formulations, whereas southern zones (992, 993) maintained high survival (≥75%) across all treatments and exhibited increased branching (up to 3.7 branches plant⁻¹) under higher-nutrient solutions. In Study 2, stem cuttings were rooted in three environments (non-mist, hydroponic, and aeroponic) and four hormone treatments (control, Clonex^®, Dip’n Grow^®, and IBA + Ethrel). Rooting occurred exclusively in the non-mist propagator; untreated controls achieved 65% success and outperformed all hormone treatments (0–10%). These results demonstrate that P. lambertiana propagation depends on seed zone-specific donor nutrition and stable, hormone-independent rooting environments. Full article

Ulluco (Ullucus tuberosus Caldas) is an Andean tuber crop of high nutritional and genetic importance. However, its vegetative propagation promotes the accumulation of pathogens and limits the availability of uniform, high-quality planting material. In this study, an efficient and reproducible in vitro micropropagation protocol was established for an ulluco genotype from the Amazonas region of Peru. Nodal segments were cultured on MS (Murashige and Skoog) medium supplemented with 6-benzylaminopurine (BAP) or kinetin (KIN) at increasing concentrations (0.0–2.0 mg L⁻¹). For rooting, in vitro-derived shoots were transferred to MS medium supplemented with indole-3-butyric acid (IBA) or 1-naphthaleneacetic acid (NAA) at the same concentration range (0.0–2.0 mg L⁻¹). The explants exhibited a high basal morphogenetic capacity; however, the addition of cytokinins significantly enhanced the response. KIN at 2.0 mg L⁻¹ achieved 100% regeneration, whereas BAP at 0.2 mg L⁻¹ maximized shoot proliferation, producing 2.07 shoots per explant. Shoot elongation was greater with KIN at 1.0 mg L⁻¹, reaching 39.15 mm. In the rooting phase, the response varied depending on the type and concentration of auxin. NAA at 0.1 mg L⁻¹ resulted in 100% rooting and produced the greatest root length (41.93 mm), whereas IBA at 0.1 mg L⁻¹ maximized the number of roots (4.67), although roots were shorter. Rooted plantlets exhibited 100% survival after eight weeks of acclimatization. This protocol provides an effective system for the rapid production of vigorous and uniform clonal plants and represents a useful tool for the propagation, conservation, and future biotechnological improvement of ulluco. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Intraspecific phenotypic diversity in clonally propagated crops is frequently constrained by narrow domestication histories and the widespread use of a limited number of elite cultivars. In Stevia rebaudiana, commercial production has largely centred on cv. ‘Morita II’, raising concerns about reduced diversity and adaptive potential. This study characterised and structured phenotypic diversity within a segregating population derived from ‘Morita II’ under tropical field conditions. Eighty-six progeny-derived genotypes (clonally propagated) plus the commercial control (87 genotypes total) were evaluated using 25 agromorphological descriptors (qualitative and quantitative). Quantitative traits showed broad variation, including plant height (28.26–119.50 cm) and dry yield rate (0.94–28.55 g plant⁻¹). Multivariate analyses of mixed descriptors (PCA and hierarchical clustering based on Gower distance) identified plant architecture, vegetative growth, and phenology as the main sources of differentiation. The first two principal components explained 19.65% and 12.58% of total phenotypic variance, respectively (32.23% cumulative). Hierarchical clustering (UPGMA; dissimilarity cut-off = 0.25) resolved four phenotypic groups (GI–GIV) with sizes n = 3, 1, 66, and 17, respectively, enabling the definition of contrasting ideotype candidates based on recurrent trait combinations. These results provide a quantitative baseline for phenotypic structuring, prioritization of materials for further evaluation, and management of clonal stevia collections in tropical production systems. These ideotype candidates should be considered preliminary until validated across environments and linked to chemical quality traits. Full article

Mulberry (Morus alba) is an economically important forest tree species, yet cutting propagation is constrained by low adventitious rooting efficiency. Although ABT, a composite rooting promoter, can improve cutting survival, its molecular basis remains unclear. Here, cuttings of the cultivar Qiangsang 1 were treated with ABT, NAA, or IAA (200–1000 mg/L) and subjected to transcriptome profiling to elucidate how ABT enhances rooting. Hormone-related analyses showed that ABT upregulated GH3 (auxin-amido synthetase) at days 0 and 20, implicating auxin homeostasis. ERF1/2 (ethylene response factors) exhibited a temporal oscillation, with induction at day 10 followed by repression from days 20 to 30, consistent with a shift from developmental programs to defense-related processes. In parallel, JAZ (jasmonate ZIM-domain) genes were downregulated at day 0 and subsequently upregulated; together with CYP94C1, these changes may attenuate jasmonate-associated defense signaling. For cell remodeling and defense coordination, ABT reduced the expression of genes associated with cell-wall rigidity while inducing EXPA11 (expansin) at day 20, potentially facilitating root primordium emergence. Meanwhile, PR-1 (pathogenesis-related protein 1) was transiently upregulated at days 0, 20, and 30, and the concomitant modulation of WRKY transcription factors and RPM1 suggests enhanced defense readiness. Integrative network analysis further indicated that a GH3–ERF1/2–PR-1 module links hormonal and defense cues and may activate BAT1 (energy metabolism) and RBOHB (ROS production) to support adventitious root elongation. Collectively, these results suggest that ABT improves rooting efficiency by reshaping hormonal homeostasis and coordinating cell-wall reconstruction with a pre-activated defense state, thereby providing a conceptual framework for balancing root induction and defense responses during vegetative propagation in forest trees. Full article

Background: LATERAL ORGAN BOUNDARIES DOMAIN (LBD/AS2) transcription factors integrate developmental and hormonal signals during organogenesis. As a high-value fruit tree crop, blueberries’ rooting ability underpins their vegetative propagation and field performance, yet a genome-wide view of the LBD repertoire and its roles in blueberry has been lacking. Results: We cataloged 153 non-redundant LBD genes (VcLBD) by homology search against the GDV RefTrans V1 genome and domain validation, substantially exceeding counts reported for other fruit crops. Phylogeny resolved the family into the canonical Class I/II and seven subclades, with extensive lineage-specific expansion supported by synteny: 72.31% of loci arose from whole-genome/segmental and tandem duplication. Gene structures were highly heterogeneous (2–24 exons) but conserved within clades; motif profiling (MEME/InterPro) recovered the signature LOB cysteine block, GAS module and a leucine-zipper-like motif with clade-specific combinations. Promoter scanning identified 38 cis-element types, including hormone- (auxin, cytokinin, GA, JA/MeJA, ABA, SA), stress- and meristem-associated motifs, indicating broad regulatory inputs. Public transcriptomes revealed pronounced tissue–stage specificity with a root-centered bias; qRT-PCR across eight organs/stages validated four archetypal expression programs (higher expression in roots, flowers, fruits in stage 1, or mature fruit, respectively), including floral/early-fruit enrichment (e.g., VcLBD39/40) and ripening-associated induction. Hormone assays demonstrated differential responsiveness: IAA up-regulated VcLBD6/16b/33c/40e/41, whereas 6-BA suppressed VcLBD16b/33c/39a/39c/40e and induced VcLBD41/46h; ACC and MeJA produced gene-specific induction or repression. During adventitious rooting (0/4/7/10 DAC), 30 VcLBDs were differentially expressed, forming three temporal patterns. VcLBD16b reaches its peak expression during the early stages of adventitious root development and exhibits a strong response to auxin. VcLBD11 shows dynamic changes synchronized with cytokinin activity, while VcLBD33/40 is associated with primordia growth and vascular-related processes. Conclusions: We identified and characterized 153 VcLBD genes, profiled their transcripts across multiple blueberry tissues, defined stages of adventitious root development, and evaluated hormone responsiveness for representative members. Together, these results establish a foundation for dissecting VcLBD regulatory mechanisms and functions, particularly in organ growth and adventitious rooting. Full article

Vegetation physiological processes are critical regulators of terrestrial carbon–water cycles and local microclimate dynamics, with photosynthetically active radiation (PAR, 400–700 nm) serving as a primary driving force. However, most vegetation–climate process models simplify the fraction of PAR in global solar radiation as a constant 50%, potentially introducing diurnal simulation biases that propagate into cumulative annual errors in vegetation carbon–water coupling estimates. To address this limitation, we first evaluated the performance of three empirical models for simulating the dynamic PAR fraction and integrated the most accurate model into the Vegetation Microclimate Process (VMcP) model, and further used typical meteorological year (TMY) data of Beijing, Shanghai and Shenzhen as input to compare the differences in vegetation carbon–water processes before and after the improvement. The results show that the diurnal variation range of PAR fraction in global solar radiation is between 39% and 58%. The existing models that neglect the dynamic changes in PAR may overestimate vegetation transpiration cooling and photosynthetic carbon sequestration by 2.3% and 3.5%, respectively. Meanwhile, Shenzhen (64.3 W/m²; 1.59 g/m²·d), characterized by favorable light and thermal conditions, is more prone to large errors compared with Shanghai (47.6 W/m²; 1.21 g/m²·d) and Beijing (39.5 W/m²; 0.93 g/m²·d). This study provides a novel tool for the accurate assessment of vegetation-mediated microclimate improvement, and offers a new perspective for nature-based climate solutions. Full article