Search Results (3,721)

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

Lodging is a major constraint limiting grain yield in dry direct seeding rice (DDSR), yet the key traits and phenotypic relationships governing lodging resistance in japonica varieties adapted to this system remain poorly understood. This study evaluated 79 japonica accessions over two years in Shenyang, Northeast China, to dissect phenotypic variation in lodging index and identify ideotypes for breeding. Based on hierarchical clustering, varieties were classified into strong lodging resistance (SLR), medium lodging resistance (MLR), and weak lodging resistance (WLR) types, with SLR varieties achieving lodging indices 27.4–31.8% lower than those of MLR and 63.2–83.8% lower than those of WLR varieties. SLR varieties reduced lodging risk by coordinately balancing whole-plant bending moment and stem breaking resistance: plant height and center-of-gravity height were 5.2–10.7% lower, while basal internode bending stress was 27.9–81.9% higher than in other types. Structural equation modeling identified culm dry weight, inner diameter, and culm phenotype index as primary determinants of lodging variation. Notably, despite 11.0–13.7% fewer spikelets per panicle, SLR varieties maintained grain yields comparable to those of WLR varieties through compensatory increases in grain-filling rate (6.7–7.3%) and 1000-grain weight (8.1–8.7%). These findings demonstrate that optimizing basal internode structure and enhancing culm tissue density can simultaneously improve lodging resistance and preserve yield potential, providing a practical framework for breeding lodging-resistant, high-yielding japonica varieties for DDSR systems in Northeast China. Full article

The development of rice flowers and panicles critically affects grain yield and quality. LEAFY HULL STERILE1/OsMADS1, a grass-specific SEPALLATA-like MADS-box transcription factor, is essential for rice floral development and floral meristem activity maintenance. However, the mechanism through which OsMADS1 interacts with other genes to regulate floral organ identity and meristem determinacy remains unclear. In this study, we first generated OsMADS1 knockout mutants using CRISPR/Cas9. The mutant florets exhibited obvious morphological defects, which were categorized into five phenotypic classes. Yeast two-hybrid screening identified two OsMADS1-interacting proteins: OsMADS22, an STMADS11-like protein, and OsYABBY5, a YABBY transcription factor. Their physical interactions were validated both in vitro and in vivo, and were important for floral organ specification and meristem maintenance. Transcriptomic analysis revealed that OsMADS1 regulates numerous genes involved in hormone signaling and panicle/flower development. Furthermore, OsMADS1 acts together with OsMADS22 and OsYABBY5 to modulate the expression of the downstream target OsMADS55, thereby controlling rice spikelet development. Together, our results reveal that OsMADS1 executes diverse regulatory functions in floral organ specification and meristem identity by interacting with multiple developmental regulators, providing new insights into the molecular mechanisms of plant flower development. Full article

The uptake and accumulation of mercury (Hg) in rice poses a serious threat to food safety. Pot experiments are widely used to screen for low-Hg-accumulating cultivars, yet their reliability in predicting field performance remains uncertain. This study evaluated pot-based screening by (1) comparing Hg uptake in rice grown in freshly processed versus aged soil; (2) contrasting Hg accumulation in the same cultivars grown in pots versus at two field sites; and (3) isolating micro-environmental effects by burying pots in situ. A total of 22 rice cultivars were used during 2021–2023 in this study. Pot systems, regardless of soil treatment, failed to replicate field accumulation patterns, yielding significantly greater Hg concentrations in brown rice (up to 59.24 ng g⁻¹) than field conditions (maximum 32.33 ng g⁻¹). Cultivar rankings derived from pot experiments showed little or no correlation with field rankings, indicating that performance is not transferable across environments. Random forest analysis identified elevated soil temperature and reduced light intensity as key artificial factors driving overestimation in pots, explaining 15.68% (total Hg) and 21.65% (methylmercury) of the variation. We conclude that pot experiments—due to soil disturbance and altered microclimates—overestimate Hg accumulation potential and show limited predictive capacity under the tested conditions. Therefore, field validation across multiple sites and seasons is essential for accurate mercury risk assessment and region-specific cultivar recommendation. Full article

Accurate large-scale crop mapping is critical for yield prediction, agricultural disaster monitoring, and global food security. Synthetic aperture radar (SAR), with its all-weather imaging capability, plays a vital role in remote sensing based on crop mapping studies. However, although feature-level fusion has been widely explored in remote sensing, existing VV and VH fusion approaches for rice mapping are still predominantly conducted at the data level and fail to adequately integrate their complementary information across the rice growth cycle, so the simplistic fusion methods yield features that are redundant or conflicting at field boundaries and in heterogeneous areas, thereby increasing classification errors. To address these challenges, this study proposes a novel spatiotemporal attention model (SimTA) for feature fusion to improve rice mapping. (1) A VV-VH feature-level fusion scheme is designed, integrated with a Content-Guided Attention (CGA) fusion method which effectively exploits the complementary information of the dual-polarized SAR data for achieving deep spatiotemporal dynamics fusion. (2) A Central Difference Convolution Spatial Extraction Conv (CDCSE Conv) Block is designed, enhancing sensitivity to edge variations in rice fields by combining standard and central difference convolutions. (3) To achieve efficient spatiotemporal feature integration across SAR time series, a Temporal–Spatial Attention (TSA) Block is developed, utilizing large-kernel convolutions for spatial feature extraction and a squeeze-and-excitation mechanism for capturing long-range temporal dependencies of rice time series. Extensive experiments were conducted by comparing SimTA with different models under five fusion schemes. Results demonstrate that feature-level fusion consistently outperforms other schemes, with SimTA achieving the best performance: OA = 91.1%, F1 score = 90.9%, and mIoU = 86.2%. Compared to the baseline Simple Video Prediction (SimVP), SimTA improves F1 score and mIoU by 0.8% and 2.1%, respectively. The CGA enhanced feature-level fusion further boosts SimTA’s performance to OA = 91.5% and F1 = 91.4%. SimTA bridges the gap between existing VV-VH deep fusion schemes and modern spatiotemporal modeling demands, offering a more accurate and generalizable approach for large-scale rice field mapping. Full article

Background/Objectives: With the acceleration of global industrialization and continuous population growth, the world is increasingly confronted with the dual challenges of food insecurity and cultivated land contamination. The screening and breeding of rice varieties with superior agronomic traits and low heavy metal accumulation have therefore become important strategies for ensuring food safety and sustainable agricultural production. Methods: In this study, rice varieties carrying the Gn1a-i gene and exhibiting specific cadmium (Cd) accumulation characteristics were screened using a combination of molecular marker detection and cadmium accumulation evaluation. Specific loop-mediated isothermal amplification (LAMP) primers targeting the Gn1a-i gene were designed and combined with a lateral flow dipstick (LFD) assay to enable rapid genetic screening of rice varieties. A six-day hydroponic experiment under cadmium stress was conducted across three temperature ranges (15–20 °C, 22–27 °C, and 30–35 °C), and cadmium accumulation in different plant organs (roots, stem sheath, and leaves) was analyzed. Results: Seven varieties carrying the Gn1a-i gene, including Xiangwanxian 12, were identified among ten tested rice varieties. Xiangwanxian 12 was subsequently selected for further evaluation, with the high-cadmium-accumulating variety Yuzhenxiang used as a control. At 144 h, the total Cd content in the measured organs of Xiangwanxian 12 was 9.6%, 4.0%, and 23.2% lower than that of Yuzhenxiang under low, medium, and high temperatures, respectively (one-tailed t-test, p < 0.01 for all three temperatures). Conclusions: The integration of LAMP-based genotyping and physiological evaluation provides a novel and reliable strategy for identifying low-Cd rice germplasm. Xiangwanxian 12, which carries the Gn1a-i allele and exhibits consistently lower Cd accumulation than Yuzhenxiang, suggests potential as a candidate for breeding high-yield, low-Cd rice cultivars. Full article

The synergistic effects of soil acidification and heavy metal pollution present major challenges for global agroecosystems. To systematically trace the evolution of research and identify key topics in this field, this study employed CiteSpace to visualize and analyze 691 records from the China National Knowledge Infrastructure (CNKI) and 6747 highly relevant articles or reviews from the Web of Science (WOS) Core Collection database from 2005 to 2025. The results indicate a steady to rapid rise in global publications, with China contributing the largest share, at 2468 publications. This has produced a research cluster centered around the Chinese Academy of Sciences (CAS); however, the centrality of its international cooperation remains limited. Studies in the CNKI database are driven by agricultural needs, focusing on national food security, rice yield stability, improvement of arable land, and heavy metal passivation and remediation, with a concentration on basic agricultural science. By contrast, research in the WOS database emphasizes fundamental mechanisms and interdisciplinary integration, addressing aluminum toxicity, microbial communities, the nitrogen cycle, and global climate change, intersecting fields such as environmental science, soil science, ecology, and microbiology. The evolution of research hotspots shows a clear trajectory: from acidity regulation and chemical speciation analysis of heavy metals (2005–2013), to heavy metal passivation, remediation, and phytoremediation (2014–2018), and then to biochar materials, microbiome analysis, and the synergistic role of carbon sequestration (2019–2025). This study argues that future research should move beyond single remediation measures and adopt integrated strategic management to jointly improve bioremediation efficiency, promote soil carbon sequestration and soil health, and enhance microbial adaptation to global climate change. Full article

Ratoon rice is a unique cropping system that utilizes the regenerative capacity of rice tillers to achieve one sowing with two harvests in a single growing season, thus exhibiting great yield potential. However, the ratooning ability is often constrained by impaired root function after the first harvest. In this study, we established an integrated nutrient management (INM) strategy to enhance root growth and function, thereby improving nutrient use efficiency and yield. Compared with farmers’ conventional management (FCM), INM increased annual total yield by 7.8% and 13.9% and enhanced ratooning ability by 20.7% and 19.0% in 2024 and 2025, respectively. INM consistently maintained higher root biomass in both main and ratoon crops: by 26.9% and 54.0% in 2024, and by 44.8% and 26.0% in 2025. Root biomass was significantly and positively correlated with brown rice weight across both seasons, and was positively associated with ratooning ability. INM also promoted early root establishment after transplanting, increasing the white-root number by 105.7%, 175.0%, and 484.8% at 3, 5, and 14 days after transplanting (DAT), respectively. Meanwhile, the xylem sap exudation rate and root triphenyl tetrazolium chloride (TTC) reduction activity were increased by 37.4% and 64.5% relative to FCM. In the 2024 ratoon season, INM improved nutrient use efficiency, with partial factor productivity (PFP) of nitrogen (PFP_N), phosphorus (PFP_P), and potassium (PFP_K) increased by 371.0%, 59.3%, and 91.1%, respectively. Gene Set Enrichment Analysis (GSEA) revealed significant enrichment of gene sets involved in root growth, development, nutrient acquisition, and assimilation under INM, providing molecular evidence for root-mediated nutrient synergy. In summary, INM enhances root growth and function, promotes nutrient uptake and utilization, and consequently improves yield. These results offer a practical management strategy supported by physiological and transcriptomic evidence for boosting ratoon rice production via root-mediated nutrient synergies. Full article

Agriculture faces escalating challenges from pests, diseases, and climatic stresses that threaten global food security. Green nanotechnology offers a sustainable approach to enhance crop protection and productivity by using plant-based methods to synthesize metallic nanoparticles (NPs), reducing chemical inputs and environmental impacts. This review presents the framework of green nanotechnology in agriculture, focusing on biogenic sources of nanoparticle synthesis (especially plant extracts), mechanisms of nanoparticle formation and stabilization by phytochemicals, and characterization techniques for green-synthesized NPs. We examine the application of plant-derived metallic nanoparticles as nanofertilizers to improve nutrient use efficiency and crop yields, as nanopesticides to manage plant pathogens and pests, and as nano-enabled agents to enhance tolerance to abiotic stresses such as salinity and drought. Recent studies demonstrate that green-synthesized NPs can increase wheat and rice yields by 13–55%, improve nutrient-use efficiency by up to 80–90% compared to conventional fertilizers, and provide effective pathogen control at reduced active ingredient doses, while reducing dependence on conventional agrochemicals. The review also discusses key challenges limiting large-scale adoption, including production scalability, biological variability in synthesis, potential phytotoxicity at high concentrations, regulatory uncertainties, and gaps in knowledge regarding nanoparticle fate and safety. Overall, green-synthesized metallic nanoparticles emerge as promising tools for improving crop productivity and protection in an eco-friendly manner, supporting the transition toward more sustainable agricultural systems. Full article

Rice is a globally important food crop, and its production is often affected by extreme climates such as drought and high temperatures. This study investigated how drought applied at different growth stages affects cadmium (Cd) uptake and accumulation in rice, as well as the underlying mechanisms. The results showed that drought treatments generally increased soil organic matter and alkali-hydrolyzed nitrogen content but decreased pH and available phosphorus content. The available Cd content in soil under the grain-filling stage drought treatment was lower than that under other treatments. Speciation analysis showed that under grain-filling stage drought, exchangeable Cd decreased by 3.04%, and residual Cd increased by 2.67%. Furthermore, drought treatments significantly enhanced soil urease and sucrase activities. Rice plant height and yield were significantly affected by the timing of drought, with the grain-filling stage drought treatment yielding the highest, while full growth stage and tillering stage drought treatments resulted in significantly lower yields. Cd content in various organs followed the order: root > stem > leaf > brown rice, with the brown rice Cd content being the lowest under grain-filling stage drought. In conclusion, drought treatment during the grain-filling stage had the least effect on Cd content in various rice tissues while maintaining a relatively high yield, providing a theoretical basis for water management in Cd-contaminated paddy fields. Full article

A comprehensive management framework integrating environmental and agronomic factors is critical for stable and resource-efficient rice production. The primary objective of this study was to develop an optimization framework for transplanted rice in Jiangsu Province, China, using a Generalized Additive Model (GAM). The framework was used to quantify the inter-annual stability of optimized management schemes and assess their sensitivity to future climate scenarios. The study evaluated the model’s generalization capability using two cross-validation strategies: Leave-One-Year-Out (LOYO) and Leave-One-Site-Out (LOSO). By predicting the yield of each candidate, the scheme maximizing yield was selected as the annual optimal management practice. Validation results demonstrated robust generalization capabilities across both spatial and temporal dimensions, with the model achieving an R² of 0.66 and an RMSE of 836 kg ha⁻¹ in LOSO validation, and an R² of 0.61 and an RMSE of 848 kg ha⁻¹ in LOYO validation. Analysis of the optimized schemes revealed that transplanting date and seedling age functioned as relatively stable planning benchmarks across years, whereas inter-annual adaptation was achieved primarily through adjustments in planting density and nitrogen inputs. Beyond yield prediction alone, this framework translates interpretable GAM response surfaces into spatially differentiated management prescriptions and highlights both soil-conditioned variable-rate strategies and the distinction between stable and adaptive management components under future climate scenarios. Full article

Nitrogen is an essential macronutrient for plant growth, photosynthesis, and grain yield. However, the nitrogen use efficiency (NUE) of crops remains relatively low, leading to nitrogen losses and environmental concerns. This is particularly important in upland rice because it is a high nitrogen user, but research of its NUE is limited. This literature review explored the contributions of leaf morphology, specifically leaf size and leaf angle, to nitrogen utilization efficiency in upland rice under varying rates of nitrogen fertilization. It also evaluated sustainable nitrogen management practices across diverse cropping systems. Findings reveal that nitrogen fertilization significantly influences leaf development, canopy structure, and nitrogen remobilization, all of which directly affect photosynthetic efficiency and yield. Breeding strategies focusing on moderate leaf size and erect leaf angles improve the nitrogen uptake and use by rice. In addition, sustainable farming practices, including precision nitrogen management, conservation agriculture, and intercropping with legumes, are effective in enhancing NUE and reducing nitrogen losses across various rice production systems. Future research should focus on identifying the thresholds of nitrogen rates that optimize leaf morphology across diverse upland rice genotypes and unravel the genetic and physiological mechanisms linking nitrogen application to leaf development. Full article

Sustainable rice production necessitates innovative strategies optimizing productivity while minimizing environmental impacts. This study developed and evaluated a Methodical Nitrogen–Water Distribution (MNWD) system, employing ¹⁵N isotopic tracing to quantify the fate of nitrogen under three management regimes: Farmer’s Practice (FP), Nitrogen–Water Coupling (NWC), and MNWD. Among them, NWC is conventional N–water coupling management, while MNWD is optimized management with reduced N, saved water and synchronous N–W uniform application. Two-year field experiments (2019–2020) demonstrated that MNWD achieved yield increases of 9.01–15.60% over FP and 2.51–5.73% over NWC, while reducing nitrogen application by 20%. Based on ¹⁵N tracing, the nitrogen recovery efficiency of MNWD reached 52.9–56.6%, and leaching losses were reduced by 65.4% compared to FP. The modular design of MNWD requires only moderate increases in labor input and basic fertigation infrastructure, ensuring its applicability to smallholder systems. The trade-off between emissions and efficiency confirmed the environmental benefits of MNWD: it resulted in 34.0% lower N₂O emissions than NWC while achieving a 5.45–5.49 percentage-point higher nitrogen recovery efficiency. Relative to FP, MNWD reduced total nitrogen losses by 48.5–61.4% with only a 3.4% increase in N₂O emissions. This indicates that nitrogen conservation was predominantly achieved through enhanced plant uptake rather than conversion to alternative loss pathways. The MNWD system demonstrates a viable pathway for sustainable rice intensification by successfully decoupling productivity gains from nitrogen input intensity. Full article

Climate variability poses major challenges to agriculture worldwide amid an increasing world population and growing food demand. This study evaluates the impact of climate variability on rice production in Liberia. Rice yields and production data (1990–2023) were attained from the Food and Agriculture Organization Statistics (FAOSTAT), while temperature and precipitation were sourced from ERA5 Agrometeorological Indicators and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). Trends and relationships were analyzed using Mann–Kendall, Sen’s slope tests, and Spearman’s rank correlation. Multiple linear regression estimates climate variables’ impact on rice productivity. The results show that mean, minimum, and maximum temperatures increased by 0.57 °C, 0.55 °C, and 0.55 °C, respectively, with precipitation variability at 180.31 mm. Climate variables showed diverse correlations with rice production. Regression results revealed a significant negative impact of minimum temperature (p-value = 0.015) on production and a positive effect of precipitation on yields (p-value = 0.036). Farmers in Liberia recognized climate impacts and adopted adaptation strategies, but resilience is hindered by limited credit access, low technology adoption, reliance on traditional practices, and inadequate extension services. Overall, the findings highlight the sensitivity of rice production in Liberia to climate variability and underscore the need for guided adaptation and institutional support to augment farmer resilience. Full article

Background: The optimal bowel preparation regimen for a small bowel capsule endoscopy (SBCE) remains uncertain. The PrepRICE clinical trial showed that the administration of purgatives after the capsule reached the duodenum improved the mucosal visualization and diagnostic yield. However, it was limited to patients with suspected mid-gastrointestinal bleeding who met strict inclusion criteria. This work aims to report real-life results after the implementation of the new protocol and to compare them with those of the PrepRICE trial. Methods: A prospective analysis was performed on all consecutive patients who underwent an SBCE between December of 2024 and December of 2025. The quality of the small bowel visualization (QSBV), gastric transit time (GTT), small bowel transit time (SBTT), adequate visualization rate, and complete examination rate were assessed. The QSBV was evaluated according to the Brotz quantitative scale. Results: A total of 188 patients were included (52.1% male; median age 56 years [IQR 30]). The median Brotz scale scores were 9 (IQR 1), 9 (IQR 1), 8 (IQR 2), and 8 (IQR 1) in the first, second, and third terciles and overall, respectively (compared to 9, 9, 9, 9 in PrepRICE, p < 0.001). No significant differences were found in the complete examination rate (96.8% vs. 99%, p = 0.43), adequate visualization rate (91.3% vs. 92.0%, p = 0.68), GTT and SBTT. Conclusions: The real-life results were good and similar to those of the original study, with a high rate of complete examination and adequate visualization, with slightly weaker QSBV compared to that reported in the periprocedural group in the PrepRICE study yet still superior to the preprocedural groups. Full article