Search Results (69)

Asian countries, which account for more than 60% of global rice consumption, are expanding the adoption of precision agriculture technology using image sensors to increase the profitability of rice production. This requires the development of technology to process 2D images that can be obtained by individual farmers instead of expensive 3D scanners. This study aims to quantitatively extract grain-level shape information necessary for yield prediction using 2D rice panicle images. To achieve this, a framework for predicting rice panicle yield from 2D images that uses a convolutional neural network (CNN) to detect grains is developed. Unlike existing approaches that measure grain length, width, and thickness using vernier calipers or 3D scanners to reconstruct 3D volume and estimate yield factors through volume-weight relationships, this methodology utilizes panicle length and projected grain area, which are relatively stable shape indices derived from 2D panicle images, to accurately describe weight variation within the same variety (e.g., Huaidao, Sidao, Suxiu, Jingjing). Experiments are conducted using panicle image data of Chinese Japonica rice varieties collected in Jiangsu Province, China. The proposed methodology demonstrates high prediction accuracy, with coefficients of determination ranging from 0.89 to 0.96, by combining panicle length and projected grain area information. Full article

Rainfed wheat (Triticum aestivum L.) production in semi-arid regions is strongly influenced by precipitation variability, soil water availability, and crop management practices. This study evaluated the effects of nutrient management under uniform weed control on soil water dynamics, weed density, and grain yield of winter wheat grown under rainfed no-till conditions in southern Kazakhstan. Field experiments were conducted during the 2018–2021 growing seasons on gray soils characterized by low organic matter and limited nitrogen and phosphorus availability. Eight fertilization treatments, including phosphorus and nitrogen combinations and a micronutrient treatment, were arranged in a randomized complete block design. Soil moisture reserves, weed density, and grain yield were analyzed in relation to precipitation variability. Productive soil moisture reserves in the 0–100 cm layer at tillering (BBCH 21–25) ranged from 155 to 178.8 mm and were closely associated with overwinter precipitation. Balanced nitrogen–phosphorus fertilization reduced weed density from 38 plants m⁻² in the control to 16 plants m⁻² under the P₄₅N₇₀ treatment. Yield stability varied across dry, normal, and wet years, reflecting the influence of precipitation conditions on crop performance. Overall, the results suggest balanced fertilization in no-till systems contributes to improved resource use and more stable wheat production under variable precipitation. Full article

Designing materials with superior corrosion resistance is critical for seawater electrolysis systems to achieve efficient and long-term stable hydrogen production. In the current study, CrAlN coatings were deposited on TA1 titanium substrates by reactive magnetron sputtering with nitrogen flow ratios ranging from 40%–70% to investigate the effect of nitrogen stoichiometry on corrosion behavior in simulated alkaline seawater (pH ≈ 14, chloride-containing). Microstructural characterization (Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), Grazing Incidence X-Ray Diffraction (GIXRD), Transmission Electron Microscopy (TEM), X-Ray Photoelectron Spectroscopy (XPS), Atomic Force Microscopy (AFM)) reveals that a 60% nitrogen ratio promotes grain refinement, improved CrN/AlN phase stoichiometry, and reduced oxygen-related defects, resulting in a dense columnar structure with minimized diffusion pathways. Electrochemical measurements show that this condition yields the lowest corrosion current density (0.297 μA·cm⁻²) and the highest polarization resistance (123.9 kΩ·cm²). Electrochemical impedance spectroscopy confirms enhanced charge transfer resistance and suppressed ionic transport at the coating/electrolyte interface. The results establish a clear correlation between nitrogen-controlled phase evolution, defect density, and passivation kinetics in highly alkaline chloride environments relevant to seawater electrolysis. This study targets the fabrication of protective coatings for alkaline seawater electrolysis via nitrogen flow ratio optimization. The optimized CrAlN coating achieves remarkably improved corrosion resistance compared with existing coatings, showing promising practical value for long-term stable seawater electrolysis. Full article

Climate variability amplifies temporal heterogeneity in crop production, challenging uniform varietal recommendations and highlighting the need to integrate genotype × environment interactions. This study evaluated the yield performance and stability of sixteen triticale (×Triticosecale Wittmack) genotypes over three consecutive growing seasons (2022/2023, 2023/2024, 2024/2025) at a single location with pronounced interannual climatic variability. Grain yield ranged from 3.49 to 6.68 t/ha in the least productive season (2022/2023) and from 7.71 to 9.92 t/ha in the most favorable season (2024/2025), with overall genotype means varying between 6.67 and 8.12 t/ha. Stability was assessed using regression-based parameters (regression coefficient and variance of deviations from regression), Shukla’s stability variance, and derived indices describing responsiveness (RI), predictability (PI), genetic risk (GRI), stress robustness (SRI), and yield opportunity (YOI). Results revealed substantial genotype × year interaction, with yield strongly dependent on seasonal conditions. Four genotypes combined high mean yield with stable performance and low interaction-related risk, indicating broad adaptability across years. Another four exhibited strong responsiveness to favorable seasons or elevated instability, increasing production risk despite high yield potential. The derived indices enabled risk-oriented genotype profiling, identifying contrasting adaptation strategies. Multivariate AMMI and GGE biplot analyses confirmed these patterns, providing a comprehensive view of interaction structure and stability. This integrated framework translates stability metrics into practical, decision-oriented descriptors, supporting risk-aware genotype selection under variable climates. Full article

Heike 60, a cold-tolerant soybean cultivar developed at the Heihe Branch of the Heilongjiang Academy of Agricultural Sciences, was evaluated across seven locations in Heilongjiang Province, northeastern China, over four growing seasons (2015–2018), generating 28 site–year environments. The objectives were to characterize yield performance and stability, partition sources of agronomic variation, and identify the yield component pathways through which the cultivar adapts to contrasting cold–temperate environments. Grain yield across the trial network ranged from 1591 to 3219 kg ha⁻¹ with a grand mean of 2688 kg ha⁻¹, and Heike 60 consistently outperformed the regional check variety Heihe 43 across all evaluated locations and seasons, with a mean yield advantage of 11.5%. Two-way ANOVA revealed highly significant (p < 0.001) Year, Location, and Year × Location interaction effects for all eight agronomic traits examined, with the interaction term accounting for the largest proportion of yield variance, indicating that relative site performance was not consistent across seasons. Five of the seven locations were classified as stable by the coefficient of variation criterion (CV < 15%), with Eberhart–Russell regression coefficients of 1.000 across all sites confirming average and proportional responsiveness to environmental quality. Hierarchical cluster analysis partitioned the 24-core site–year environments into three agronomically distinct groups reflecting differences in accumulated thermal resources: a pod number-compensating profile under lower temperature accumulation, a seed weight-dominated profile under higher post-anthesis thermal supply, and a balanced yield component expression representing the predominant growing conditions of the region. Random forest modeling identified hundred-seed weight, pods per plant, and growth period as the primary predictors of grain yield across environments. Collectively, the results demonstrate that Heike 60 possesses broad adaptability and phenotypic plasticity across the cold–temperate soybean production zone of Heilongjiang Province, combining competitive mean yield with stable performance across diverse environmental conditions. Full article

Dynamic characterization of calcareous (carbonate) sands is essential for performance-based design of offshore foundations, coastal reclamation, and marine infrastructure in tropical and subtropical regions. In contrast to silica sands, carbonate sediments are biogenic and typically comprise angular, irregular grains with intra-particle voids and fragile skeletal microstructure. These traits promote grain crushing and fabric evolution at relatively low-to-moderate confinement, leading to pronounced stress dependency, strong nonlinearity with strain amplitude, and substantial scatter in laboratory stiffness and damping measurements. Consequently, empirical correlations calibrated primarily on quartz sands may yield biased estimates when transferred to carbonate environments. This study presents an ML-driven, leakage-aware benchmarking framework for predicting two key dynamic parameters of biogenic calcareous sands, damping ratio $D$ and shear modulus $G$, using standard tabular descriptors commonly available in geotechnical practice. Two consolidated experimental databases were curated from resonant column and cyclic triaxial measurements ($D$: $n=890$; $G$: $n=966$), spanning mean effective confining stress $25 \le {\sigma }_m^{\prime }\le 1600 \mathrm{k}$Pa and a wide range of density and gradation conditions. To emphasize transferability, explicit deposit/site labels were excluded, and missingness arising from heterogeneous reporting was handled through a consistent preprocessing pipeline (training-only imputation, categorical encoding, and scaling). Eleven regression algorithms were evaluated, covering linear baselines, regularized regression, neighborhood learning, single trees, bagging and boosting ensembles, kernel regression, and a feedforward neural network. Performance was assessed using $R^2$, RMSE, and MAE on training/validation/test splits, and engineering credibility was supported through explainability-based diagnostics to verify mechanically plausible sensitivities. Results show that ensemble-tree models (Extra Trees and Random Forest) provide the most reliable accuracy–robustness balance across both targets, consistently outperforming linear models and the tested SVR configuration and exhibiting stable validation-to-test behavior. The explainability audit confirms physically meaningful separation of governing controls: stiffness is primarily stress-controlled (${\sigma }_m^{\prime }$ dominant for $G$), whereas damping is primarily strain-controlled ($\gamma$ dominant for $D$). The proposed framework supports practical deployment as a fast surrogate for generating $G\hspace{0.17em}-\hspace{0.17em}\gamma$ and $D\hspace{0.17em}-\hspace{0.17em}\gamma$ curves within the training domain and for guiding targeted laboratory test planning in carbonate settings. Full article

Identifying superior genotypes in multi-environment trials is crucial for accelerating cultivar improvement and breeding innovation. This study evaluated the yield potential of 29 maize hybrids (including the control) across 10 trial locations in mid-to-high altitude regions of Yunnan Province from two growing seasons (2023–2024), aiming to recommend high-yielding, stable, and widely adapted maize varieties. Analysis of variance indicated that genotype, environment, and their interaction all had highly significant effects (p < 0.001) on maize yield, with environmental factors accounting for the primary source of variation; in 2023 and 2024, 63.79% and 64.15% of the total variation were explained, respectively. The grain yield of the maize hybrids ranged from 8873 kg/ha to 12,089 kg/ha, with the highest yield over the two consecutive years being 11,783 kg/ha (XR-399). Yield mean analysis identified the top-performing hybrids annually: in 2023, these were G28, G13, G22; in 2024, they included G5, G13, G4. In the GGE biplot analysis, E2 (Binchuan), E5 (Lijiang), E7 (Shilin), and E8 (Xuanwei) were the most distinguishable and representative test environments. The “mean vs. stability” GGE biplot indicated that G22 (LS-2305), G9 (LS-2303), and G13 (XR-399) exhibited consistent high yields and stability across years. Based on the “Which-Won-Where” GGE biplot, G27 (SS-2205) and G13 (XR-399) were identified as the optimal hybrids for each mega-environment, with G13 (XR-399) emerging as the most outstanding. Therefore, these findings confirm that the GGE biplot method is effective for screening high-yielding, stable hybrids and identifying representative test environments, thereby providing a scientific foundation for maize breeding work in the region. Full article

This study systematically investigates the thickness-dependent mechanical properties of electroplated copper foils with fine-grained (FG-Cu) and columnar nanotwinned (NT-Cu) microstructures. Tensile testing across a thickness range of 5–30 μm revealed that NT-Cu exhibits superior mechanical stability, with significantly lower reductions in both ultimate tensile strength (UTS) and yield strength (YS) compared to FG-Cu. The UTS of the 30 μm thick FG-Cu foil was measured at 651 MPa, increasing to 792 MPa at a thickness of 5 μm. In contrast, the UTS of NT-Cu foils only rose from 624 MPa at 30 μm to 663 MPa at 5 μm. A similar trend was observed for the YS. Microstructural analysis confirmed that NT-Cu maintains a stable columnar grain structure with minimal grain growth, contributing to its resistance to thickness-induced strength loss. These findings highlight NT-Cu as a promising candidate for applications requiring consistent mechanical performance across varying foil thicknesses. Full article

Hyperspectral images (HSIs) provide rich spectral–spatial information and support applications in remote sensing, agriculture, and medicine, yet their development is hindered by data scarcity and costly acquisition. Diffusion models have enabled synthetic HSI generation, but conventional integer-order solvers such as Denoising Diffusion Implicit Models (DDIM) and Pseudo Linear Multi-Step method (PLMS) require many steps and rely mainly on local information, causing error accumulation, spectral distortion, and inefficiency. To address these challenges, we propose Integer–Fractional Alternating Diffusion Sampling (IFADiff), a training-free inference-stage enhancement method based on an integer–fractional alternating time-stepping strategy. IFADiff combines integer-order prediction, which provides stable progression, with fractional-order correction that incorporates historical states through decaying weights to capture long-range dependencies and enhance spatial detail. This design suppresses noise accumulation, reduces spectral drift, and preserves texture fidelity. Experiments on hyperspectral synthesis datasets show that IFADiff consistently improves both reference-based and no-reference metrics across solvers without retraining. Ablation studies further demonstrate that the fractional order $\alpha$ acts as a controllable parameter: larger values enhance fine-grained textures, whereas smaller values yield smoother results. Overall, IFADiff provides an efficient, generalizable, and controllable framework for high-quality HSI generation, with strong potential for large-scale and real-time applications. Full article

We propose a local–global attention fusion network for benign–malignant discrimination of ovarian tumors in color Doppler ultrasound (CDFI). The framework integrates three complementary modules: a local enhancement module (LEM) to capture fine-grained texture and boundary cues, a Global Attention Module (GAM) to model long-range dependencies with flow-aware priors, and a Fusion Discriminator (FD) to align and adaptively reweight heterogeneous evidence for robust decision-making. The method was evaluated on a multi-center clinical dataset comprising 820 patient cases (482 benign and 338 malignant), ensuring a realistic and moderately imbalanced distribution. Compared with classical baselines including ResNet-50, DenseNet-121, ViT, Hybrid CNN–Transformer, U-Net, and SegNet, our approach achieved an accuracy of 0.923, sensitivity of 0.911, specificity of 0.934, AUC of 0.962, and F1-score of 0.918, yielding improvements of about three percentage points in the AUC and F1-score over the strongest baseline. Ablation experiments confirmed the necessity of each module, with the performance degrading notably when the GAM or the LEM was removed, while the complete design provided the best results, highlighting the benefit of local–global synergy. Five-fold cross-validation further demonstrated stable generalization (accuracy: 0.922; AUC: 0.961). These findings indicate that the proposed system offers accurate and robust assistance for preoperative triage, surgical decision support, and follow-up management of ovarian tumors. Full article

Weeds are a significant factor affecting crop yield and quality. Herbicides have made crucial contributions to ensuring stable and high grain production, but the low effective utilization rate and short duration of traditional formulations have led to excessive application and a range of ecological and environmental issues. Nanoherbicides, particularly carrier-coated systems, can simultaneously leverage the small size, large specific surface area, and high permeability of nanoparticles, as well as the multifunctionality of carriers, to synergistically enhance the efficacy and safety of the formulations. This provides a scientific and promising strategy for overcoming the functional deficiencies of traditional formulations. Nevertheless, there are currently relatively few articles that systematically review the research progress and performance advantages of nanoherbicides. This review provides a concise overview of the preparation methods and structural characteristics of nanoherbicides. It primarily highlights the classification of carrier-coated nanoherbicides, along with representative studies and their distinctive properties across various categories. Based on this foundation, the performance advantages of nanoherbicides are systematically summarized. Finally, the major challenges and future prospects in this research field are proposed. This review offers valuable insights and methodological guidance for the design and rational application of efficient, environmentally friendly nanoherbicides. Full article

Beer is the third most popular beverage in the world, and its production is distributed uniformly between the biggest continents. Considering the environmental aspects, the utilization of brewing by-products, mainly brewers’ spent grain (BSG), is essential on a global scale. The beer revolution, lasting over a few decades, significantly diversified the beer market in terms of styles, and therefore, also its by-products, which should be characterized appropriately prior to further application. Herein, the presented study investigated the unprecedented number of 22 different variants of brewers’ spent grain, yielded from the production of various beer styles, enabling their proper comparison. A comprehensive by-product characterization revealed an almost linear relationship (Pearson correlation coefficients exceeding 0.9) between the color parameters (L*, a*, browning index) of beer and generated spent grain, enabling a prediction of BSG appearance based on beer color. Applying wheat or rye malts increased the content of extractives by over 40%, reducing cellulose content by as much as 45%. Thermal treatments of malts (kilning or smoking) also reduced extractive content and limited antioxidant activity, often by over 30%. A lack of husk for wheat or rye reduced the crystallinity index of spent grain by 21–41%, while the roasting of barley efficiently decomposed the less stable compounds and maintained the cellulose crystalline structure. All the analyzed BSG samples were characterized by low volatile emissions and very limited antimicrobial activity. Therefore, their harmfulness to human health and the environment is limited, broadening their potential application range. Full article

Soybean (Glycine max L.) is a vital grain and oil crop, serving as a primary source of edible oil, plant-based protein, and livestock feed. Its production is crucial for ensuring global food security. However, soybean yields are severely impacted by various diseases, and the development of disease-resistant cultivars remains the most sustainable strategy for mitigating these losses. While stable genetic transformation is a common approach for studying gene function, virus-induced gene silencing (VIGS) offers a rapid and powerful alternative for functional genomics, enabling efficient screening of candidate genes. Nevertheless, the application of VIGS in soybean has been relatively limited. In this study, we established a tobacco rattle virus (TRV)-based VIGS system for soybean, utilizing Agrobacterium tumefaciens-mediated infection. The TRV vector was delivered through cotyledon nodes, facilitating systemic spread and effective silencing of endogenous genes. Our results demonstrate that this TRV–VIGS system efficiently silences target genes in soybean, inducing significant phenotypic changes with a silencing efficiency ranging from 65% to 95%. Key genes, including phytoene desaturase (GmPDS), the rust resistance gene GmRpp6907, and the defense-related gene GmRPT4, were successfully silenced, confirming the system’s robustness. This work establishes a highly efficient TRV–VIGS platform for rapid gene function validation in soybean, providing a valuable tool for future genetic and disease resistance research. Full article

In this study, a combined localization experiment was performed on different nitrogen application rates in rice–wheat rotation. Rice cultivar Nanjing 5718 and wheat variety Yangmai 25 were employed in this two-season study, with six and five distinct nitrogen rates designed during the rice and wheat growing seasons, respectively. Thus, a total of 30 N rate combinations were formed across the two seasons. Our findings indicate that when current-season N inputs ranged from 0 to 240 kg ha⁻¹, residual N from the preceding season contributed significantly to yield improvement (5.58–18.96% increase) for subsequent crops, primarily through enhanced panicle formation and the number of grains per spike. Conversely, high current-season N rates (360–420 kg ha⁻¹) lead to reduced yields (4.61–5.81%) in the following cropping cycle under identical N management practices. Maximizing annual crop production was achieved with a combined N regimen of 264.63 kg ha⁻¹ (rice) and 254.89 kg ha⁻¹ (wheat), yielding 14.21 t ha⁻¹. Notably, current-season N levels exhibited significant correlations with starch and protein content in both rice and wheat, whereas previous-season N application showed no comparable relationships. Furthermore, soil N storage remained stable, and the highest N use efficiency was observed under the total annual N input of 547.7 kg ha⁻¹ (rice + wheat). Full article

Triticale (× Triticosecale Wittmack) is a valuable dual-purpose crop due to its adaptability to marginal environments and its potential for both high-quality grain and forage production. However, a comprehensive evaluation of its forage quality characteristics and agronomic performances is still needed. This study evaluated the grain and forage yield potentials and nutritional compositions of 11 triticale genotypes over two consecutive years in a semi-arid region located in Shanxi province, China. Forage quality was assessed using several key parameters, including nutrient composition, fiber digestibility, mineral content, and energy density, while grain quality parameters, including nutrient composition as well as carbohydrate and fiber characteristics, were also analyzed. Significant genetic variation was observed in these traits, indicating the influence of genotype–environment interactions on these traits. The tested genotypes exhibited grain yields ranging from 4.83 to 6.92 t ha⁻¹ and fresh forage biomass yields between 20.06 and 29.78 t ha⁻¹, demonstrating their potential for sustainable forage and grain production under semi-arid conditions. Genotypes from our breeding programs, including Shengnongsicao 1 and Jinsicao 1, demonstrated superior adaptability, maintaining stable forage and grain yield potentials under adverse conditions. Their favorable nutritional characteristics further enhance their suitability for semi-arid livestock systems. High levels of essential minerals, particularly calcium and potassium, further enhanced the nutritional value of these genotypes. These results provide valuable insights for triticale breeding programs and suggest triticale’s potential as a reliable crop in semi-arid regions, where maximizing land productivity is essential. Full article