Search Results (1,382)

Effective weed control is essential for sustainable and safe rice production, particularly under the long-term and widespread use of chemical herbicides. Oilseed rape (Brassica napus L.) is one of the most important oil crops worldwide, and the oilseed rape–rice rotation system is widely practiced in China. It has been reported to exhibit strong allelopathy on various plants, but the feasibility of using its straw incorporation for weed control in transplanted rice fields remains unclear. In this study, a natural weed management strategy based on shallow tillage of oilseed rape straw (ORS) was evaluated through laboratory bioassays, greenhouse experiments, and field trials. The results indicated that soil decomposition liquids (SDLs) of ORS exhibited strong dose- and decomposition time-dependent allelopathic effects on seven paddy weed species, while rice showed markedly lower sensitivity. ORS incorporated at 700–1100 g/m² generally exhibited high integrated allelopathic inhibition (in lab) and population control effects (in greenhouse) on paddy weeds, especially Leptochloa chinensis (L.) Nees, Cyperus iria L., and Cyperus difformis L. Among the growth parameters of ORS allelopathic stress, root growth was the most sensitive indicator, followed by shoot growth and seed germination. Greenhouse experiments displayed variety-dependent impact on the transplanted rice seedlings, with Xiushui134 and Yongyou1540 showing relatively high tolerance. Field trials revealed that ORS incorporation at 1100 g/m² for 10 d achieved a satisfactory control of population (77.7–84.9%) and fresh weight (80.7–95.6%) across Gramineae, Cyperaceae and Broadleaf weeds, without adverse impact on the growth of transplanted rice seedlings (Yongyou1540). This treatment also significantly promoted theoretical grain yield by 13.4–19.4%. Overall, shallow tillage of oilseed rape straw provides a feasible and environmentally friendly weed control strategy for transplanted rice systems. Full article

The global demand for Salvia miltiorrhiza Bunge in the botanical medicine market is steadily increasing. However, its production has long relied on asexual root propagation, making it highly susceptible to germplasm degradation. Transitioning to seed reproduction offers the advantage of genetic renewal, yet it is constrained by unstable seed vigor and slow seedling growth. In the present study, comprehensive physiological and microbiome analyses of S. miltiorrhiza seeds from 14 regions across 7 provinces in China were conducted to elucidate the association between the seed microbiome and vigor, and to identify plant growth-promoting (PGP) strains. The results demonstrated: (1) Seed physical traits and germination characteristics varied significantly across geographic origins. Seed vigor, exhibiting the highest coefficient of variation, served as a key parameter reflecting germination quality. (2) High-vigor seeds harbored distinct microbial communities characterized by higher diversity indices, greater network complexity, and the significant enrichment of potentially beneficial bacteria (e.g., Pseudomonas). (3) Through correlation-directed screening of isolated pure cultures, Pseudomonas mendocina P-6 and Enterobacter ludwigii BM-12 were identified as exhibiting robust, multi-trait PGP capacity. In planta validation showed that these two strains significantly promoted the growth of 1-month-old S. miltiorrhiza seedlings, increasing total fresh weight by 33.9–71.3%. This study reveals the microecological drivers of seed vigor and provides candidate strains for inoculant development, thereby supporting the sustainable, seed-based propagation of S. miltiorrhiza. Full article

To solve the problems of low seeding precision and the poor operational adaptability of traditional no-till seeders under dense planting mode, and meet the agronomic requirements for high maize yield, this study carried out optimization and experimental research on the no-till precision fertilizer-seed co-sowing system for maize with wide-narrow row dense planting, relying on the experimental base of the Science and Technology Courtyard for Super High-Yield Cropping Systems in Qihe, China Agricultural University. Through modular integration and the optimization of key components, precise row spacing adjustment and improved sowing depth consistency in complex plots were achieved. A tractor-implement integrated a kinematic model and a dynamic model of the seed metering tube, which were constructed to quantify the correlation between operational parameters and motion states, providing theoretical support for structural parameter optimization. Field tests showed that all operational quality indicators of the system met the local high-yield requirements for no-till dense planting; the comprehensive performance was optimal at a density of 75,000 plants·ha⁻¹, with the best seeding uniformity (coefficient of variation: 5.65%), seedling emergence and seedling uniformity, which is well adapted to the agronomic characteristics of the wheat–maize rotation areas in the Huang-Huai-Hai Plain. Subsequent optimization by reducing the operating speed and increasing the spring stiffness can further improve the operational quality, realize the deep integration of agronomy and agricultural machinery, provide agricultural machinery support for high-yield and high-quality maize cultivation, and is of great significance for improving agricultural production efficiency and resource utilization. Full article

Accurate identification of plant numbers is crucial for evaluating the effectiveness of mechanical rice seedling transplanting, which directly affects yield estimation and replanting decisions in precision agriculture. Conventional manual counting methods are time-consuming and labor-intensive, which hinders their application in modern agriculture, where efficiency and precision are paramount. Therefore, this study constructed a dataset based on images collected by consumer-grade Unmanned Aerial Vehicles (UAVs) and proposed an improved lightweight detection model named CLR-YOLO (Complex-scene Lightweight Rice-detection YOLO) based on the YOLOv11n. The model replaces the original C3k2 module with C3k2-PConv to improve computational efficiency while maintaining feature extraction capability. Additionally, it reconstructs the neck network using the Heterogeneous Selective Feature Pyramid Network (HSFPN) to optimize the handling of features from both large and small targets. Finally, the PConvHead detection head is designed to enhance feature utilization efficiency and reduce both false positives and missed detections in dense rice seedling scenarios. Experimental results demonstrated that CLR-YOLO achieved an average precision (AP@0.5) of 93.9%. While maintaining comparable accuracy, the model reduced parameters to 1.4 M, computational cost to 3.7 GFLOPs, and model size to 2.9 MB—reductions of 46.2%, 41.3%, and 44.2%, respectively, compared to the baseline model. This model provides significant support for rice seedling detection and offers valuable insights to assist agricultural producers in making subsequent decisions. Full article

This study investigated the effects of aqueous extracts of orange peel–derived biochar on seed germination and early seedling growth in durum wheat (Triticum durum Desf.) and common buckwheat (Fagopyrum esculentum Moench.). Biochar was produced by pyrolysis of orange peel at temperatures ranging from 250 to 550 °C. Germination assays were conducted under controlled laboratory conditions, and seedling growth parameters were evaluated after six days of cultivation. Untreated orange peel completely inhibited seed germination (0 %) in both species, while biochar produced at 250 °C significantly reduced germination (e.g., the germination index decreased from 54.21 % in the control to 47.2 % in T. durum). In contrast, biochar produced at 350 °C increased germination to >96 % in T. durum and 100 % in F. esculentum, accompanied by enhanced seedling vigor and biomass production. Chemical analyses revealed a pronounced decrease in total phenolic content (from 53.84 to 0.57 mg GAE g⁻¹ DW) and flavonoids (from 90.05 to 1.34 mg QE g⁻¹ DW) with increasing pyrolysis temperature, along with a reduction in antioxidant activity. Common buckwheat exhibited consistently higher tolerance to biochar extracts than durum wheat across all treatments. Overall, the results demonstrate that pyrolysis temperature is a key factor governing the transition from phytotoxic to biostimulatory effects, with optimal performance observed at approximately 350 °C. Full article

Light and drought antagonistically regulate stomatal movement, yet the mechanisms for integrating these conflicting signals remain unclear. In this study, the stomatal aperture and photosynthetic parameters under red light (RL), blue light (BL), and white light in different water regimes were evaluated. Transcriptome analysis was conducted during a 0–6 h period of BL exposure, with or without drought, to explore the molecular mechanisms underlying BL and drought-mediated stomatal movement. Under non-drought conditions, BL significantly enhanced stomatal conductance, transpiration rate, and stomatal aperture. After drought stress, BL-treated seedlings exhibited the greatest reductions in these indicators. Transcriptomic analysis revealed that both BL-responsive genes and drought-responsive genes were significantly enriched in overlapping pathways related to plant hormone signal transduction, and biological processes of water/fluid transport. Among these, the aquaporin gene CsPIP2;3 was identified as a core node in the crosstalk between BL and drought signals, and a potential key regulator of stomatal movement. Tissue-specific expression analysis showed its highest expression in mature leaves; GUS staining further confirmed its expression in guard cells and vascular bundles, while subcellular localization verified the plasma membrane localization of its encoded protein. The transcriptomic data provide novel insights into the mechanisms underlying stomatal movement regulated by BL and drought. Full article

Gold nanoparticles (AuNPs) have numerous applications in science and industry. Therefore, their potential phytotoxicity should be investigated. Garden cress (Lepidium sativum L.) is a useful model plant for assessing the effects of chemicals released into the environment. The aim of this study was to prepare alginate gels containing AuNPs for plant exposure experiments, evaluate their physicochemical properties, and determine their effects on selected biochemical parameters of garden cress seedlings. Gold nanoparticles were synthesized in sodium alginate at an initial concentration of 50 mg/L, using xylose and maltose as reducing agents. The gels were diluted with distilled water to obtain AuNP concentrations of 5 and 25 mg/L. Garden cress seeds were placed on filter paper soaked with the tested formulations, while distilled water and sodium alginate solutions without AuNPs served as controls. After 5 days of incubation at 20 °C under light conditions, the plant material was collected and selected bioactive compounds were determined. AuNP-containing gels significantly affected the biochemical status of the seedlings. In particular, AuNPs synthesized with xylose at 25 mg/L significantly increased the contents of photosynthetic pigments and total polyphenolic compounds. All tested AuNP formulations increased the antioxidant activity of seedlings, suggesting the activation of abiotic stress-related defense responses, however, direct markers of oxidative damage were not assessed in the present study. Overall, the results indicate that alginate-based AuNPs can modify selected biochemical parameters in garden cress seedlings, and these effects depend on nanoparticle concentration and reducing sugar used during synthesis, which may be relevant for the future development of plant-targeted nanomaterials for agricultural applications. Full article

Chitosan nanoparticles (Ch NPs) are versatile nanomaterials with expanding agricultural and biomedical applications, highlighting the need for reproducible, low-cost, and scalable synthesis methods to ensure their safe and widespread use in biological systems. This study presents a simple ionic-gelation protocol using a serological pipette–needle dropwise system that minimizes reagent waste and requires no sophisticated equipment. The synthesized Ch NPs were characterized by UV–Vis spectroscopy, ESEM, TEM, EDS, DLS, XRD, and FTIR, confirming nanoscale size, strong positive surface charge, and characteristic chitosan–TPP interactions. To establish a standardized biological safety assessment framework, three representative bioassays were implemented across microbial, plant, and mammalian systems. Antibacterial testing against Xanthomonas oryzae pv. oryzae (Xoo) using a resazurin-based microdilution assay revealed a minimum inhibitory concentration (MIC) of 128 µg/mL, whereas bulk chitosan showed no inhibition up to 512 µg/mL. Phytotoxicity and seed germination assays on rice (Oryza ‘KDML105’) demonstrated no inhibitory effects on germination, with over 90% germination by day 3 and significantly enhanced seedling growth parameters (p < 0.05) at 64–128 µg/mL, indicating non-phytotoxicity. MTT assays confirmed that Ch NPs were non-toxic to both human skin cell lines (HDF and HaCaT) across 2.5–160 µg/mL, showing enhanced cell viability in HDF cells at specific concentrations and stable viability in HaCaT cells, indicating overall biocompatibility. Importantly, all bioassays were conducted under aligned concentration ranges to enable cross-system comparison and reproducibility. This integrated workflow links nanoparticle synthesis with a standardized, multi-system evaluation strategy, supporting the safe application of Ch NPs in biological systems. Full article

Floodplain forests in central Amazonia are structured along a marked flooding gradient that influences species distribution, performance, and survival. This study evaluated the demographic structure, survival, and growth responses of two co-occurring tree species across contrasting várzea environments differing in inundation regimes. Field surveys quantified seedlings, juveniles, and adults in low- and high-floodplain forests, while a field experiment assessed survival and growth under conditions with and without interspecific interaction. Repeated-measures ANOVA revealed that temporal variation and forest type significantly affected growth parameters, with species-specific responses to flooding intensity. In the field experiment, mortality of Crateva tapia L. differed significantly among treatments (χ² = 24.96, p < 0.001), with the highest mortality observed in high-várzea (up to 75% under interspecific interaction), while Hura crepitans L. showed 100% survival across all treatments. Non-parametric analyses detected no significant treatment effects on selected morphological traits. The results support the stress-gradient hypothesis, suggesting that plant–plant interactions may shift along the flooding gradient, with facilitative processes becoming more relevant under higher stress conditions. Overall, differential flood tolerance appears to be a key driver of habitat preference and population structure in these Amazonian wetlands. Full article

Henan Province, the foremost wheat-producing region in China, frequently experiences drought stress during the wheat seedling stage. Innovating the evaluation methods for drought resistance at this stage and identifying drought-resistant varieties are crucial for the effective utilization of germplasm. This study utilized 55 wheat varieties that have been bred and promoted in Henan Province in recent years as experimental materials. A 15% PEG-6000 solution was employed to simulate drought stress, and the primary morphological indicators of seedlings, relative chlorophyll content (SPAD), and various chlorophyll fluorescence parameters were assessed. The comprehensive drought resistance scores (D values) for each variety were determined by calculating the drought resistance coefficients for each index, employing principal component analysis, and conducting membership function analysis. Based on the cluster analysis of D values, 55 varieties were categorized into four groups: high drought resistance (10), medium drought resistance (28), low drought resistance (16), and drought-sensitive (1). Seven indicators—SPAD, fresh root weight, seedling height, fresh weight of the aboveground part, Fv/Fm, PItotal, and Mo—were selected as evaluation metrics for the drought resistance of wheat through stepwise regression analysis. The comprehensive evaluation system developed in this study, which is based on morphological and photosynthetic fluorescence characteristics, can swiftly and accurately assess the drought resistance of wheat seedlings. The key indicators identified for highly drought-resistant varieties may serve as valuable references for drought-resistant wheat breeding in Henan Province. Full article

Agricultural robots and unmanned farmland management require real-time and precise parsing of crop leaves at the edge to support variable application of pesticides, seedling condition monitoring, and phenotypic analysis. However, the field environment features drastic changes in light, leaf occlusion, and interference from background weeds, which can cause semantic fragmentation and boundary artifacts in lightweight models. This paper presents ES2-LeafSeg, a lightweight framework for leaf semantic segmentation tailored for edge deployment. The method employs EfficientNetV2 as the backbone encoder and introduces the State Space Semantic Enhancement Module (S2FEM) on skip connection features, modeling long-range dependencies and suppressing local texture noise through SSM pooling in row and column directions. Meanwhile, a cross-scale decoder (CSD) and a global context transformation (GCT) are designed to achieve multi-scale semantic fusion and boundary refinement. On the three-class segmentation task of the SoyCotton dataset, ES2-LeafSeg achieved mIoU of 0.817, mDice of 0.869, Fβw of 0.925, and MAE of 0.011, outperforming multiple classic and recent baselines while maintaining 23.67 M parameters and 49.62 FPS. Ablation experiments further verified the complementary contributions of S2FEM and GCT to regional consistency and boundary quality. Full article

Liquidambar formosana Hance, a tree species in subtropical broad-leaved forests, exhibits a striking autumn leaf coloration. However, how drought stress during this period influences leaf color change remains poorly understood. In this study, two-year-old seedlings were subjected to four drought gradients. Leaf color parameters, pigment contents, cellular structure, photosynthetic physiology, and hydraulic properties were systematically measured throughout the leaf color transition period. The results show that, with increasing drought severity, leaf red-green coordinate a* increased significantly during early-to-middle stress (S1–S3), while lightness L* and yellow-blue coordinate b* increased at late stress (S4). Chlorophyll (Chl) content continuously decreased, anthocyanins (Ant) peaked at mid-stress, and carotenoids (Car) became enriched at late stress. Leaf cellular structure and hydraulic parameters declined, photosynthetic function was inhibited, and antioxidant enzyme activities showed an initial increase followed by a decrease. Correlation analysis and Random Forest models revealed that L* was strongly associated with superoxide dismutase (SOD) activity, carotenoid-to-chlorophyll (Car/Chl) ratio, and net photosynthetic rate (P_n); a* was closely linked to osmotic potential at full saturation (Ψ_sat), relative water content at the turgor loss point (RWCtlp), SOD activity, Car/Chl ratio, anthocyanin-to-chlorophyll (Ant/Chl) ratio, Ant content, transpiration rate (T_r), P_n, and main vein thickness (Mvt), while b* was primarily correlated with Ψ_sat, Car/Chl ratio, SOD activity, Ant/Chl ratio, and P_n. These statistical associations suggest multiple physiological processes are involved in leaf color change. Based on these findings, we propose a hypothetical sequence: drought initially disrupts leaf water status, leading to structural atrophy and hydraulic decline, followed by photosynthetic inhibition, activated antioxidant defense, and altered pigment accumulation, which are correlated with the sequential leaf color transition from green to red to yellow-orange in this species. Full article

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

The use of nanoparticles (NPs) synthesized from plant extracts is an alternative to conventional pesticides for the control of agricultural pests. This study aimed to optimize the conditions of synthesis of silver–zinc nanoparticles (Ag-ZnNPs) using extracts of Ocimum basilicum L. and Crotalaria longirostrata Hook. & Arn. and to evaluate their phytotoxic impact on maize seedlings. The Ag-ZnNPs (Ag-Zn nanoparticles) were synthesized by redox reaction between metal ions and reducing metabolites present in the extracts. A response surface methodology (RSM) with three factors (extract concentration, heating time and pressure) was applied to determine the optimal synthesis conditions. The phytotoxicity of nanoparticles (NPs) on maize seedlings was subsequently evaluated on root growth, oxidative stress enzymes (CAT, POD, and APX), and physiology of seedlings. Nanoparticles synthesized from C. longirostrata extract demonstrated superior properties, with an optimization of synthesis (R² = 95.3%) where the extract concentration (1:4 v/v; p < 0.01) was the critical factor influencing the reduction of metallic ions to nanoparticles. These NPs exhibited superior stability, smaller size (<100 nm), and zeta potential greater than 30 mV compared with O. basilicum extracts. Their NPs exhibited poorer optimization of synthesis (R² = 43.8%) without the effect of any of the variables evaluated. Essentially, C. longirostrata NPs showed no phytotoxic effects on maize seedlings’ physiological parameters and enhanced root growth (117.2 mm) without negatively affecting photosynthesis (PSII 70-81 FvFm). Ag-ZnNPs synthesized with C. longirostrata exhibited optimal stability and size, along with no observed possible phytotoxicity effects, unlike O. basilicum NPs, which cause stress on maize seedlings. Therefore, Crotalaria longirostrata NPs could represent a promising material for agricultural pest control, with no apparent adverse effect on maize crops. Full article

This study investigates how soil calcium (Ca) and magnesium (Mg) supplementation influence mitragynine accumulation in Mitragyna speciosa (kratom), addressing the lack of quantitative thresholds in previous research. Seedlings from a uniform seed stock were cultivated in a controlled environment using a standardized soil mix (soil:peat moss:earthworm castings, 6:1:1). Following an initial growth phase, Ca and Mg were applied at three concentrations and in fixed Ca:Mg ratios (5:1, 10:1, 20:1) using gypsum and Epsom salt. Over a 45-day treatment period, growth parameters and mitragynine levels were assessed one week after the final application. Seedlings under control had the highest total biomass (102.35 g), significantly exceeding both the Mg-only and Ca:Mg treatments (64–84 g), and values above the typical upper threshold of 20 did not suppress growth, as evidenced by unchanged root-to-shoot ratios across treatments. In contrast, mitragynine accumulation was the highest under moderate Ca:Mg ratios (8–10), exhibiting a 2–14% increase relative to the control, suggesting that production of this alkaloid is more sensitive to nutrient balance than overall growth. These findings underscore the importance of nutrient ratios, rather than individual nutrient concentrations, in regulating both vegetative development and alkaloid production in kratom. Maintaining an appropriate Ca:Mg ratio can support efficient seedling growth as well as maximizing mitragynine levels. Preliminary field trials over a span of one month indicate that field-grown seedlings exhibit a similar result with high growth and mitragynine content in soils having a Ca:Mg ratio of 1:10. Future studies should test these responses under field conditions and over longer growth periods. Full article