Search Results (1,440)

Wheat (Triticum aestivum L.) is a crucial global food crop. The intensive crop farming, monoculture cultivation, and impact of climate change affect the susceptibility of wheat cultivars to biotic stresses, mainly caused by soil fungal pathogens, especially those belonging to the genus Fusarium. This situation threatens yield and grain quality through root and crown rot. While conventional chemical fungicides face resistance issues and environmental concerns, biological alternatives like seed priming with natural metabolites are gaining attention. Polyamines, including putrescine, spermidine, and spermine, are attractive priming agents influencing plant development and abiotic stress responses. Spermine in particular shows potential for in vitro antifungal activity against Fusarium. Optimising spermine concentration for seed priming is crucial to maximising protection against Fusarium infection while ensuring robust plant growth. In this research, we explored the potential of the polyamine spermine as a seed treatment to enhance wheat resilience, aiming to identify a sustainable alternative to synthetic fungicides. Our findings revealed that a six-hour seed soak in spermine solutions ranging from 0.5 to 5 mM did not delay germination or seedling growth. In fact, the 5 mM concentration significantly stimulated root weight and length. In complementary in vitro assays, we evaluated the antifungal activity of spermine (0.5–5 mM) against three Fusarium species. The results demonstrated complete inhibition of Fusarium culmorum growth at 5 mM spermine. A less significant effect on Fusarium graminearum and little to no impact on Fusarium oxysporum were found. The performed analysis revealed that the spermine had a fungistatic effect against the pathogen, retarding the mycelium growth of F. culmorum inoculated on the seed surface. A pot experiment with Bulgarian soft wheat cv. Sadovo-1 was carried out to estimate the effect of seed priming with spermine against infection with isolates of pathogenic fungus F. culmorum on plant growth and disease severity. Our results demonstrated that spermine resulted in a reduced distribution of F. culmorum and improved plant performance, as evidenced by the higher fresh weight and height of plants pre-treated with spermine. This research describes the efficacy of spermine seed priming as a novel strategy for managing Fusarium root and crown rot in wheat. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

The improvement in yield in cultivar mixtures has been well established. Despite increasing knowledge of the improvement involving within-species diversification and resource use efficiency, little is known about the benefits arising from relatedness-mediated intraspecific interactions in cultivar mixtures. This study used a relatedness gradient of rice cultivars to test whether neighbor relatedness contributes to improvements in grain yields in cultivar mixtures. We experimentally demonstrated the grain yield of rice cultivar mixtures with varying genetic relatedness under both field and controlled conditions. As a result, a closely related cultivar mixture had increased grain yield compared to monoculture and distantly related mixtures by optimizing the root-to-shoot ratio and accelerating flowering. The benefits over monoculture were most pronounced when compared to the significant yield reductions observed in distantly related mixtures. The relatedness-mediated improvement in yields depended on soil volume and nitrogen use level, with effects attenuating under larger soil volumes or nitrogen deficiency. Furthermore, neighbor relatedness enhanced the richness and diversity of both bacterial and fungal communities in the rhizosphere soil, leading to a significant restructuring of the microbial community composition. These findings suggest that neighbor relatedness may improve the grain yield of rice cultivar mixtures. Beneficial plant–plant interactions may be generated by manipulating cultivar kinship within a crop species. A thorough understanding of kinship strategies in cultivar mixtures offers promising prospects for increasing crop production. Full article

Aquatic macrophytes are a vital component of lake ecosystems, profoundly influencing ecosystem structure and function. Under future scenarios of more frequent extreme floods and intensified lake eutrophication, aquatic macrophytes will face increasing challenges. Therefore, understanding aquatic macrophyte responses to flood disturbances and nutrient enrichment is crucial for predicting future vegetation dynamics in lake ecosystems. This study focuses on Huangmaotan Lake, a Yangtze River floodplain lake, where we reconstructed 200-year successional trajectories of macrophyte communities and their driving mechanisms. With a multiproxy approach we analyzed a well-dated sediment core incorporating plant macrofossils, grain size, nutrient elements, heavy metals, and historical flood records from the watershed. The results demonstrate a significant shift in the macrophyte community, from species that existed before 1914 to species that existed by 2020. Unlike the widespread macrophyte degradation seen in most regional lakes, this lake has maintained clear-water plant dominance and experienced continuous vegetation expansion over the past 50 years. We attribute this to the interrelated effects of floods and the enrichment of ecosystems with nutrients. Specifically, our findings suggest that nutrient enrichment can mitigate the stress effects of floods on aquatic macrophytes, while flood disturbances help reduce excess nutrient concentrations in the water column. These findings offer applicable insights for aquatic vegetation restoration in the Yangtze River floodplain and other comparable lake systems worldwide. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

Disparities in the functional classification of secretory genes among aphid taxa may be attributed to variations in coding sequences and gene expression profiles. However, the driving factors that regulate sequence evolution remain unclear. This study aimed to investigate the differences in coding sequences and expression patterns of secretory genes between the rose grain aphid (Metopolophium dirhodum) and the pea aphid (Acrythosiphon pisum), with a particular focus on their roles in evolutionary adaptations and functional diversity. The study involved the rearing of aphids, RNA extraction, de novo transcriptome assembly, functional annotation, secretory protein prediction, and comparative analysis of coding sequences and expression patterns across various functional categories using bioinformatics tools. The results revealed that metabolic genes exhibited greater coding sequence divergence, indicating the influence of positive selection. Moreover, significant expression divergence was noted among functional categories, particularly in metabolic and genetic information processing genes, which exhibited higher variability. This study enhances our understanding of the molecular mechanisms that contribute to phenotypic and genetic diversity among aphid species. This study elucidates the relationship between variations in coding sequences and differences in gene expression among functional categories, thereby establishing a foundation for future studies on gene evolution in response to environmental pressures. Full article

This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro–temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands. On a curated dataset of 17,653 three-second recordings spanning 18 species, DuSAFNet achieves 96.88% accuracy and a 96.83% F1 score using only 6.77 M parameters and 2.275 GFLOPs. Cross-dataset evaluation on Birdsdata yields 93.74% accuracy, demonstrating robust generalization to new recording conditions. Its lightweight design and high performance make DuSAFNet well-suited for edge-device deployment and real-time alerts for rare or threatened species. This work lays the foundation for scalable, automated acoustic monitoring to inform biodiversity assessments and conservation planning. Full article

Plastic debris is a pervasive and persistent threat to marine ecosystems. Microplastics (plastics < 5 mm) are increasing in a variety of marine habitats, including open water systems, shorelines, and benthic sediments. It remains unclear how microplastics distribute and accumulate in marine systems and the extent to which this pollutant is accessible to marine taxa. We examined subtidal benthic sediments and beach sediments in critical nearshore habitats for forage fish species—Pacific sand lance (Ammodytes personatus), Pacific herring (Clupea pallasi), and surf smelt (Hypomesus pretiosus)—to quantify microplastic concentrations in the spawning and deep-water habitats of these fish and better understand how microplastics accumulate and distribute in nearshore systems. In the San Juan Islands, we examined an offshore subtidal bedform in a high-flow channel and beach sites of protected and exposed shorelines. We also examined 12 beach sites proximate to urban areas in Puget Sound. Microplastics were found in all samples and at all sample sites. Microfibers were the most abundant, and flakes were present proximate to major shipyards and marinas. Microplastics were significantly elevated in Puget Sound compared to the San Juan Archipelago. Protected beaches had elevated concentrations relative to exposed beaches and subtidal sediments. Microplastics were in higher concentrations in sand and fine-grain sediments, poorly sorted sediments, and artificial sediments. Microplastics were also elevated at sites confirmed as spawning habitats for forage fish. The model results indicate that both current speed and proximate urban populations influence nearshore microplastic concentrations. Our research provides new insights into how microplastics are distributed, deposited, and retained in marine sediments and shorelines, as well as insight into potential exposure in benthic, demersal, and shoreline habitats. Further analyses are required to examine the relative influence of urban populations and shipping lanes and the effects of physical processes such as wave exposure, tidal currents, and shoreline geometry. Full article

This study aims to analyze the differences in soil bacterial community structure under different vegetation restoration types, and to explore the role of microorganisms in the process of vegetation restoration on the soil ecosystem of the Grain for Green area in the Loess Plateau. High-throughput sequencing technology was used to analyze the alpha diversity of soil bacteria, community structure characteristics, and the correlation between soil environmental factors and bacterial communities in different artificial Hippophae rhamnoides forests. Soil microbial C and N show a decreasing trend with an increase in the 0–100 cm soil layers. The results indicated that the bacterial communities comprised 24 phyla, 55 classes, 110 orders, 206 families, 348 genera, 680 species, and 1989 OTUs. Additionally, the richness indices and diversity indices of the bacterial community in arbor shrub mixed forest are higher than those in shrub pure forest, and the indices of shrub forest on sunny slope are higher than those on shady slope. Across all samples, the dominant groups were Actinobacteria (37.27% on average), followed by Proteobacteria (23.91%), Acidobacteria (12.75%), and Chloroflexi (12.27%). Soil nutrient supply, such as TOC, TN, AN, AP, and AK, had crucial roles in shaping the composition and diversity of the bacterial communities. The findings reveal that vegetation restoration significantly affected soil bacterial community richness and diversity. Furthermore, based on the results, our data provide a starting point for establishing soil bacterial databases in the Loess Plateau, as well as for the plants associated with the vegetation restoration. Full article

Fusarium head blight in barley (Hordeum vulgare) reduces grain yield and can lead to the accumulation of deoxynivalenol (DON) and nivalenol (NIV) in grains. We surveyed Fusarium species and evaluated DON and NIV concentrations in barley grains in four regions of Rio Grande do Sul, the southernmost state in subtropical Brazil. Seven Fusarium species were identified: F. asiaticum, F. avenaceum, F. cortaderiae, F. graminearum, F. gerlachii, F. meridionale and F. poae. DON (0 to 10,200 µg/kg) and NIV (0 to 1630 µg/kg) were detected in 74% and 70% of the samples, respectively, with higher concentrations found in experimental fields. However, in commercial barley fields, most samples fell below 2000 µg/kg of DON, which is the maximum limit allowed by Brazilian legislation for grains intended for processing. The seasonality of temperature and precipitation influenced mycotoxin concentrations. Therefore, the variability of Fusarium species in Rio Grande do Sul and a high incidence of DON and NIV in barley grains highlight the complexity of this pathosystem. This variability of Fusarium species may also influence the effectiveness of measures to control the disease, particularly in relation to genetic resistance and fungicide application. Full article

Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to support conservation and breeding efforts. This study characterized genome-wide diversity, population structure (STRUCTURE, principal coordinate analysis (PCoA), neighbor-joining trees, analysis of molecular variance (AMOVA)), and selection signatures (F_ST, Hardy–Weinberg deviations) in Ethiopian durum wheat by analyzing 376 genotypes (148 accessions) using an Illumina Infinium 25K single nucleotide polymorphism (SNP) array. A set of 7842 high-quality SNPs enabled the assessments, comparing landraces with cultivars and breeding populations. Results revealed moderate genetic diversity (mean polymorphism information content (PIC) = 0.17; gene diversity = 0.20) and identified 26 loci under selection, associated with key traits like grain yield, stress tolerance, and disease resistance. AMOVA revealed 80.1% variation among accessions, with no significant differentiation by altitude, region, or spike density. Landraces formed distinct clusters, harboring unique alleles, while admixture suggested gene flow via informal seed exchange. The findings highlight Ethiopia’s rich durum wheat diversity, emphasizing landraces as reservoirs of adaptive alleles for breeding. This study provides genomic insights to guide conservation and the development of climate-resilient cultivars, supporting sustainable wheat production globally. Full article

The sheaths of bamboo shoots, characterized by distinct colors and spotting patterns, are key phenotypic markers influencing species classification, market value, and genetic studies. This study introduces YOLOv8-BS, a deep learning model optimized for detecting these traits in Chimonobambusa utilis using a dataset from Jinfo Mountain, China. Enhanced by data augmentation techniques, including translation, flipping, and contrast adjustment, YOLOv8-BS outperformed benchmark models (YOLOv7, YOLOv5, YOLOX, and Faster R-CNN) in color and spot detection. For color detection, it achieved a precision of 85.9%, a recall of 83.4%, an F1-score of 84.6%, and an average precision (AP) of 86.8%. For spot detection, it recorded a precision of 90.1%, a recall of 92.5%, an F1-score of 91.1%, and an AP of 96.1%. These results demonstrate superior accuracy and robustness, enabling precise phenotypic analysis for bamboo germplasm evaluation and genetic diversity studies. YOLOv8-BS supports precision agriculture by providing a scalable tool for sustainable bamboo-based industries. Future improvements could enhance model adaptability for fine-grained varietal differences and real-time applications. Full article

Soil contamination with toxic inorganic elements poses a major challenge to rice cultivation, affecting plant physiology, yield, and grain safety. While natural variation in tolerance exists among rice genotypes and related species, recent advances in genomics, breeding, and biotechnology offer new opportunities to enhance adaptation. This review synthesizes the current knowledge on the physiological effects of toxic elements and explores strategies to improve tolerance, from harnessing genetic diversity to genome editing and transgenic approaches. Attention is also paid to the role of microbiota in mitigating toxicity and reducing translocation to seeds, highlighting emerging solutions for sustainable rice production in contaminated environments. Full article

The review summarizes the historical and current research on perennial grain breeding in Russia within the context of growing global interest in perennial crops. N.V. Tsitsin’s pioneering work in the 1930s produced the first wheat–wheatgrass amphiploids, which demonstrated the capacity to regrow after harvest and survive for 2–3 years. Subsequent research at the Main Botanical Garden in Moscow focused on characterizing Tsitsin’s material, selecting superior germplasm, and expanding genetic diversity through new cycles of hybridization and selection. This work led to the development of a new crop species, Trititrigia, and the release of cultivar ‘Pamyati Lyubimovoy’ in 2020, designed for dual-purpose production of high-quality grain and green biomass. Intermediate wheatgrass (Thinopyrum intermedium) is native to Russia, where several forage cultivars have been released and cultivated. Two large-grain cultivars (Sova and Filin) were developed from populations provided by the Land Institute and are now grown by farmers. Perennial rye was developed through interspecific crosses between Secale cereale and S. montanum, demonstrating persistence for 2–3 years with high biomass production and grain yields of 1.5–2.0 t/ha. Hybridization between Sorghum bicolor and S. halepense resulted in two released cultivars of perennial sorghum used primarily for forage production under arid conditions. Russia’s agroclimatic diversity in agricultural production systems provides significant opportunities for perennial crop development. The broader scientific and practical implications of perennial crops in Russia extend to climate-resilient, sustainable agriculture and international cooperation in this emerging field. Full article

Nectar robbing typically reduces nectar availability to pollinators, damages flower structure, and/or induces secondary robbing. Consequently, it may reduce pollen deposition and seed set, increase pollination efficiency and outcrossing, and/or not affect reproduction in some species. However, spatiotemporal variations in nectar robbing and their effects on plant reproduction have received little attention. In this study, we assessed the effects of nectar robbing on floral visits, seed set, nectar volume and concentration, and flower longevity in two populations of Salvia castanea Diels (Lamiaceae) in the Himalayan region of Southwestern China in 2014–2020. We also examined whether one or a few visits by pollinators can result in the stigma receiving sufficient pollen to fertilize all ovules of S. castanea. We found that significant differences in the nectar robbing rate did not affect seed set in any of the years for either population of S. castanea. In the robbed and unrobbed flowers, nectar was consistently replenished every night at higher concentrations. Bagging, nectar robbing, and sufficient pollination did not affect flower longevity. Salvia castanea required only 5–10 pollen grains to achieve the maximum seed set. However, pollinators depositing more than 10 pollen grains after a single visit ensured a high seed set of >80%. Our results suggest that nectar availability, floral longevity maintenance, and sufficient pollen deposition mitigate the effects of nectar robbing on the reproductive success of S. castanea. These results are expected to further our understanding of plant–animal interactions and the ecological consequences of nectar robbing. Full article