Search Results (75)

Yield-related traits of rice (Oryza sativa L.) are pivotal to safeguarding global food security. As a powerful and efficient strategy, genome-wide association study (GWAS) has identified numerous genes for yield-related traits in rice over recent decades, providing critical resources for germplasm improvement. Most yield-related traits are complex quantitative traits controlled by multiple genes with diverse effect sizes, and traditional GWAS approaches have limited power to detect small-effect loci. In this study, we employed Fast3VmrMLM, a compressed mixed linear model integrating genome-wide scanning and machine learning, to perform GWAS for 10 key yield-related traits using a panel of 529 rice accessions and 4,945,006 single-nucleotide polymorphisms (SNPs). The traits included heading date, plant height, panicle number, effective panicle number, yield per plant, spikelet length, grain length, grain width, grain weight, and grain thickness. We detected 141 significant quantitative trait nucleotides (QTNs) associated with target traits and identified 92 previously validated genes located near these QTNs. As a key environmental regulator, photoperiod directly controls flowering and indirectly modulates yield-related traits, and we further identified 182 photoperiod-responsive candidate genes via differential expression and Gene Ontology (GO) enrichment analysis. Through tissue-specific expression analysis, homology analysis with Arabidopsis genes, and haplotype-phenotype differential analysis, six pleiotropic candidate genes were confirmed; notably, LOC_Os02g02210 appears to contribute substantially to grain width and yield-related traits. In conclusion, Fast3VmrMLM proved effective for dissecting the genetic basis of yield-related traits, especially in detecting small-effect loci. These results not only establish a potential genetic link between photoperiod regulation and rice yield formation but also provide high-confidence candidate genes and loci that will accelerate functional genomic studies and precision molecular breeding for high-yield rice. Full article

Rice yield is a complex quantitative trait. Although a lot of genes for yield have been cloned, their genetic basis remains unknown. In the present study, a set of chromosome segment substitution line population (CSSL) was developed, derived from the indica variety Huanghuazhan as the recipient parent and the Aus variety N22 as the donor parent, and a high-density bin map containing 609 bins was constructed by resequencing. The CSSL population comprised 155 families with an average background recovery rate of 93.02%. Nine yield-related traits, including plant height, panicle number, panicle length, primary branch number, spikelet number per panicle, grain number per panicle, seed setting rate, 1000-grain weight, and grain yield per plant, were evaluated across four environments. The results showed significant differences in yield-related traits between the two parents across four environments. All nine traits showed continuous distribution with transgressive segregation. Spikelet number per panicle, grain number per panicle and 1000-grain weight showed strong correlations with each other, whereas panicle number had weak correlations with them. A total of 80 main-effect quantitative trait loci (QTLs) affecting yield-related traits were identified, among which 13 QTLs were repeatedly detected in multiple environments, 45 QTLs were located in 8 pleiotropic QTL regions, and 47 QTLs showed significant interactions with environments. In addition, 260 pairs of epistatic QTLs underlying yield-related traits were identified, of which 2 pairs stably expressed across different environments, and 11 pairs controlled more than two traits. These findings provide a theoretical basis for clarifying the genetic differentiation between indica and Aus and cloning yield-related genes, and offer valuable gene resources for molecular breeding of high-yield rice varieties. Full article

In the field of precision agriculture, accurately detecting rice panicles is crucial for monitoring rice growth and managing rice production. To address the challenges posed by complex field backgrounds, including variety differences, variations across growth stages, background interference, and occlusion due to dense distribution, this study develops an improved YOLO11-based rice panicle detection model, termed DRPU-YOLO11. The model incorporates a task-oriented CSP-PGMA module in the backbone to enhance multi-scale feature extraction and provide richer representations for downstream detection. In the neck network, DySample and CGDown are adopted to strengthen global contextual feature aggregation and suppress background interference for small targets. Furthermore, fine-grained P2 level information is integrated with higher-level features through a cross-scale fusion module (CSP-ONMK) to improve detection robustness in dense and occluded scenes. In addition, the PowerTAL strategy adapts quality-aware label assignment to emphasize high-quality predictions during training. The experimental results based on a self-constructed dataset demonstrate that DRPU-YOLO11 significantly outperforms baseline models in rice panicle detection under complex field environments, achieving an accuracy of 82.5%. Compared with the baseline model YOLO11 and RT-DETR, the mAP₅₀ increases by 2.4% and 5.0%, respectively. These results indicate that the proposed task-driven design provides a practical and high-precision solution for rice panicle detection, with potential applications in rice growth monitoring and yield estimation. Full article

Background: The coordinated improvement of yield, quality and resistance is a primary goal in rice breeding. Gene editing technology is a novel method for precise multiplex gene improvement. Methods: In this study, we constructed a multiplex CRISPR/Cas9 vector targeting yield-related genes (GS3, OsPIL15, Gn1a), fragrance gene (OsBADH2) and rice blast resistance gene (Pi21) to pyramid traits for enhanced yield, quality, and disease resistance in rice. A tRNA-assisted CRISPR/Cas9 multiplex gene editing vector, M601-OsPIL15/GS3/Gn1a/OsBADH2/Pi21-gRNA, was constructed. Genetic transformation was performed using the Agrobacterium-mediated method with the japonica rice variety Xin Dao 53 as the recipient. Mutation editing efficiency was detected in T₀ transgenic plants. Grain length, grain number per panicle, thousand-grain weight, 2-acetyl-1-pyrroline (2-AP) content, and rice blast resistance of homozygous lines were measured in the T₃ generations. Results: Effectively edited plants were obtained in the T₀ generation. The simultaneous editing efficiency for all five genes reached 9.38%. The individual gene editing efficiencies for Pi21, GS3, OsBADH2, Gn1a, and OsPIL15 were 78%, 63%, 56%, 54%, and 13%, respectively. Five five-gene homozygous edited lines with two genotypes were selected in the T₂ generation. In the T₃ generation, compared with the wild-type (WT), the edited homozygous lines showed increased grain number per panicle (14.60–25.61%), increased grain length (7.39–11.16%), increased grain length–width ratio (8.37–13.02%), increased thousand-grain weight (3.79–9.15%), a 42–64 folds increase in the fragrant substance 2-AP content, and significantly enhanced rice blast resistance. Meanwhile, there were no significant changes in other agronomic traits. Conclusions: CRISPR/Cas9-mediated multiplex gene editing technology enabled the simultaneous editing of genes related to rice yield, quality, and disease resistance. This provides an effective approach for obtaining new japonica rice germplasm with blast resistance, long grains, and fragrance. Full article

Shendao3^# (SD3^#) exhibits perennial characteristics. Identifying the QTLs underlying the yield-related traits in SD3^# provides a theoretical basis for future perennial rice breeding. In this study, SD3^# and an F₂ population derived from a cross between SD3^# and XieqingzaoB (XQZB) and its bi-parents were selected as experimental materials. A total of fifteen yield-related traits including plant height, effective panicle per plant and thousand-grain weight in the SD3^#-population and its bi-parents were investigated for both phenotypic analysis and QTL mapping. Results indicated that the fifteen yield-related traits in the SD3^#-population exhibited quantitative genetic characteristics suitable for QTL analysis. Altogether, 25 QTLs underlying the yield-related traits and 26 pairs of epistatic QTLs were identified; these explained phenotypic variances ranging from 4.21% to 27.30% and 1.24% to 19.30%. Of these, nine novel QTLs underlying unfilled grain per panicle (UGP), spikelet per panicle (SP), seed setting density (SSD), grain yield per plant (GYP) and thousand-grain weight (TGW) with additive effects derived from SD3^# were detected on the first, second, fourth, eighth, ninth, and tenth chromosomes. Six pleiotropic QTLs underlying two or more traits were detected on the first, fourth, seventh, eighth, and eleventh chromosomes. This work lays a good foundation for both the yield-related gene mined from SD3^# and future perennial Chinese rice breeding. Full article

Ratoon rice plays a crucial role in sustainable rice production due to its potential for additional harvests; however, the genetic basis of its yield remains to be explored. In this study, we aimed to precisely dissect the genetic basis of yield in ratoon rice by selecting 302 eight-way MAGIC lines that achieved synchronized heading within a 10-day period through staggered sowing. The eight parental lines exhibited distinct yield performances across both main and ratoon crops. Significant correlations were observed between the main and ratoon crops concerning panicle length (R = 0.67) and spikelets per panicle (R = 0.36). Genome-wide association studies (GWAS) revealed a total of 17 quantitative trait loci (QTLs) associated with five yield-related traits in both main and ratoon crops. Specifically, seven QTLs were detected for yield components in the main crop, while six QTLs were identified in the ratoon crop, in addition to five QTLs associated with ratooning ability. Notably, only one QTL, qPL1, was commonly detected in both crops, exhibiting opposite effects on tiller number across crop types. Among the QTLs specifically identified in the ratoon crop, qGY10 demonstrated the largest effect on ratoon grain yield without compromising the performance of the main crop. The known gene, Ghd7.1, exhibited pleiotropic effects on both ratooning ability and ratoon grain yield. Candidate gene analysis prioritized likely causal genes and defined key haplotypes within these QTL intervals by leveraging the genomic diversity of the eight founders. These findings underscore the distinct genetic determinants for yields in main and ratoon crops, providing a genetic basis for breeding high-yielding varieties in both crop types. Full article

High temperatures during grain filling degrade rice quality, yet the metabolite-level basis of varietal tolerance—particularly root contributions—remains unclear. We compared the heat-tolerant ‘Fusaotome’ and the widely grown ‘Akitakomachi’ under control and high-temperature conditions. Panicles and roots were sampled at heading and profiled by capillary electrophoresis–mass spectrometry (CE–MS), followed by PCA, univariate testing, and KEGG pathway analysis. PCA resolved treatment and cultivar differences in an organ-specific manner. In panicles, ‘Fusaotome’ showed 8 increased metabolites (≥1.5-fold) and 11 decreased (≤1/1.5), whereas ‘Akitakomachi’ showed 19 increases and 6 decreases (p < 0.05). In roots, 12 metabolites increased in ‘Fusaotome’ and 9 in ‘Akitakomachi’; no significant decreases were detected. Pathway analysis indicated activation in ‘Fusaotome’ panicles of tryptophan, nicotinate/nicotinamide, arginine/proline, glycolysis/TCA, pyruvate, and vitamin B6 pathways, while ‘Akitakomachi’ emphasized phenylpropanoid, isoquinoline alkaloid, caffeine, and ubiquinone/terpenoid–quinone biosynthesis. In roots, ‘Fusaotome’ prioritized phenylalanine/phenylpropanoid, aromatic amino acids, lysine degradation, branched-chain amino acids, glycerophospholipids, and alkaloids, whereas ‘Akitakomachi’ favored nitrogen- and antioxidant-related routes. Collectively, the tolerant cultivar maintained antioxidant capacity and energy supply while coordinating root–panicle metabolism, whereas the susceptible cultivar shifted toward secondary defenses. These signatures nominate candidate metabolic markers and targets for breeding and management to stabilize rice production under warming climates. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

In rice detection tasks, accurate identification of leaf streaks, pest and disease distribution, and spikelet hierarchies relies on high-quality images to distinguish between texture and hierarchy. However, existing images often suffer from texture blurring and contour shifting due to equipment and environment limitations, which affects the detection performance. In view of the fact that pests and diseases affect the whole situation and tiny details are mostly localized, we propose a rice image reconstruction method based on an adaptive two-branch heterogeneous structure. The method consists of a low-frequency branch (LFB) that recovers global features using orientation-aware extended receptive fields to capture streaky global features, such as pests and diseases, and a high-frequency branch (HFB) that enhances detail edges through an adaptive enhancement mechanism to boost the clarity of local detail regions. By introducing the dynamic weight fusion mechanism (CSDW) and lightweight gating network (LFFN), the problem of the unbalanced fusion of frequency information for rice images in traditional methods is solved. Experiments on the 4× downsampled rice test set demonstrate that the proposed method achieves a 62% reduction in parameters compared to EDSR, 41% lower computational cost (30 G) than MambaIR-light, and an average PSNR improvement of 0.68% over other methods in the study while balancing memory usage (227 M) and inference speed. In downstream task validation, rice panicle maturity detection achieves a 61.5% increase in mAP50 (0.480 → 0.775) compared to interpolation methods, and leaf pest detection shows a 2.7% improvement in average mAP50 (0.949 → 0.975). This research provides an effective solution for lightweight rice image enhancement, with its dual-branch collaborative mechanism and dynamic fusion strategy establishing a new paradigm in agricultural rice image processing. Full article

Nitrogen is a crucial element that impacts rice yield and its constituent factors. The effects of reduced nitrogen levels on yield constitute is a complex quantitative trait that is controlled by multiple genes, and its genetic basis requires further exploration. In this study, 562 MAGIC line population and 284 germplasm varieties were used for genome-wide association analysis (GWAS) and haplotype analysis, aiming to detect quantitative trait loci (QTL) and candidate genes associated with tolerance to low nitrogen levels. The ratio of effective panicle number per plant (REPN), total number of grains per panicle (RTGN), seed setting rate (RSSR), thousand grain weight (RTGW), biomass (RBM), harvest index (RHI), and grain yield per plant (RGY) of low to normal nitrogen conditions were measured in this study. The RBM and RHI were directly closely related to RGY, while the RSSR indirectly and positively affected RGY through RHI, and the REPN and RTGN mainly indirectly and positively affected RGY through RBM. LOC_Os06g06440 was the most likely gene affecting low-nitrogen-tolerance-related traits in rice within the region, ranging from 2.898 Mb to 3.046 Mb (148 kb) on chromosome 6, and the haplotype AA, with a significantly larger mean RGY of 0.95 and 1.53 in the MAGIC and germplasm varieties, respectively, was the advanced allele of LOC_Os06g06440. Nine xian (indica) varieties (IRIS_313-11624, IRIS_313-10932, CX382, B067, B249, IRIS_313-8215, IRIS_313-10544, B052, and B233) carrying the superior haplotype (AA) of LOC_Os06g06440 and having a higher RGY were selected for the molecular marker-assisted selection of low nitrogen tolerance in rice. These results will enhance our knowledge of the genetic basis of tolerance to low levels of nitrogen and provide valuable information for improving tolerance to low levels of nitrogen in rice-breeding programs. Full article

Rice panicle detection is a key technology for improving rice yield and agricultural management levels. Traditional manual counting methods are labor-intensive and inefficient, making them unsuitable for large-scale farmlands. This paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images. The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. The model was trained and evaluated on the open-access Dense Rice Panicle Detection (DRPD) dataset, which comprises UAV images captured at 7 m, 12 m, and 20 m altitudes. Experimental results demonstrate that our method significantly outperforms existing advanced models, achieving an AP50 of 0.8931 and an F2 score of 0.8377 on the test set. While ensuring model accuracy, the parameters of the proposed model decreased by 42.87% and the GFLOPs by 48.95% compared to Panicle-AI. Grad-CAM visualizations reveal that FRPNet exhibits superior background noise suppression in 20 m altitude images compared to mainstream models. This work establishes an accuracy-efficiency balanced solution for UAV-based field phenotyping. Full article

Bacterial leaf streak (BLS) is one of the internationally significant quarantine diseases in rice. Effectively utilizing BLS resistance genes from wild rice (Oryza rufipogon Griff.) to breed new varieties offers a fundamental solution for BLS control. This study focused on the fine mapping of the BLS resistance gene bls2 and the development of closely linked molecular markers for breeding BLS-resistant lines. Using a Guangxi common wild rice accession DY19 (carrying bls2) as the donor parent and the highly BLS-susceptible indica rice variety 9311 as the recipient parent, BLS-resistant rice lines were developed through multiple generations of backcrossing and selfing, incorporating molecular marker-assisted selection (MAS), single nucleotide polymorphism(SNP) chip genotyping, pathogen inoculation assays, and agronomic trait evaluation. The results showed that bls2 was delimited to a 113 kb interval between the molecular markers ID2 and ID5 on chromosome 2, with both markers exhibiting over 98% accuracy in detecting bls2. Four stable new lines carrying the bls2 segment were obtained in the BC₅F₄ generation. These four lines showed highly significant differences in BLS resistance compared with 9311, demonstrating moderate resistance or higher with average lesion lengths ranging from 0.69 to 1.26 cm. Importantly, no significant differences were observed between these resistant lines and 9311 in key agronomic traits, including plant height, number of effective panicles, panicle length, seed setting rate, grain length, grain width, length-to-width ratio, and 1000-grain weight. Collectively, two molecular markers closely linked to bls2 were developed, which can be effectively applied in MAS, and four new lines with significantly enhanced resistance to BLS and excellent agronomic traits were obtained. These findings provide technical support and core germplasm resources for BLS resistance breeding. Full article

Accurately detecting rice panicles in complex field environments remains challenging due to their small size, dense distribution, diverse growth directions, and easy confusion with the background. To accurately detect rice panicles, this study proposes OE-YOLO, an enhanced framework derived from YOLOv11, incorporating three synergistic innovations. First, oriented bounding boxes (OBB) replace horizontal bounding boxes (HBB) to precisely capture features of rice panicles across different heights and growth stages. Second, the backbone network is redesigned with EfficientNetV2, leveraging its compound scaling strategy to balance multi-scale feature extraction and computational efficiency. Third, a C3k2_DConv module improved by dynamic convolution is introduced, enabling input-adaptive kernel fusion to amplify discriminative features while suppressing background interference. Extensive experiments on rice Unmanned Aerial Vehicle (UAV) imagery demonstrate OE-YOLO’s superiority, achieving 86.9% mAP50 and surpassing YOLOv8-obb and YOLOv11 by 2.8% and 8.3%, respectively, with only 2.45 M parameters and 4.8 GFLOPs. The model has also been validated at flight heights of 3 m and 10 m and during the heading and filling stages, achieving mAP50 improvements of 8.3%, 6.9%, 6.7%, and 16.6% compared to YOLOv11, respectively, demonstrating the generalization capability of the model. These advancements demonstrated OE-YOLO as a computationally frugal yet highly accurate solution for real-time crop monitoring, addressing critical needs in precision agriculture for robust, oriented detection under resource constraints. Full article

The overuse of chemical fertilizers under adverse conditions endangers the sustainability of agriculture. A biological approach should be investigated to address this issue. Therefore, this study aimed to detect the potency of purple non-sulfur bacteria that can fix nitrogen (N) (PNSB-fN) Rhodobacter sphaeroides in soil N fertility, plant N uptake, growth, and rice yield. In brief, an experiment was conducted to check whether the biofertilizer containing PNSB-fN strains can improve rice yield and soil fertility under a highly saline acidic condition. A randomized complete block design was used with four replicates on saline soil in An Bien-Kien Giang, Vietnam. The first factor was the N fertilizer level, i.e., (i) 100%, (ii) 75%, (iii) 50%, and (iv) 0%; the second factor was the PNSB-fN (R. sphaeroides), i.e., (i) the control, (ii) S01, (iii) S06, and (iv) combined S01–S06. In the results, supplying PNSB-fN increased NH₄⁺ compared with the control, i.e., 104.7–112.0 mg NH₄⁺ kg⁻¹ compared with 94.0 mg NH₄⁺ kg⁻¹ in season 1 and 35.9–38.0 mg NH₄⁺ kg⁻¹ compared with 34.2 mg NH₄⁺ kg⁻¹ in season 2. Additionally, by supplying each PNSB-fN strain, the soil Na⁺ and plant Na in culm leaf and grain were decreased in comparison with those in treatments without PNSB-fN. The total N uptake was also enhanced by the PNSB-fN compared with the control. Moreover, supplying PNSB-fN improved the crop height, panicle length, panicle quantity pot⁻¹, grain quantity panicle⁻¹, filled spikelet rate, and grain yield compared with the control. Ultimately, in extremely saline soil, the mixture of PNSB-fN not only improved soil fertility and reduced soil salinity but also replaced 25% of chemical N fertilizer to ensure sustainable agriculture. This newly developed biofertilizer was potent in not only improving the rice and soil health in the locality but also performing the same under similar conditions around the globe. Full article

This study aimed to develop an aromatic thermosensitive genic male sterile (TGMS) line in indica rice using CRISPR/Cas9 technology. The TMS5 and FGR in the high-quality conventional rice variety Huahang 48 were targeted for editing using CRISPR/Cas9 technology. CRISPR/Cas9 vectors designed for TMS5 and FGR were constructed and introduced into rice calli through Agrobacterium-mediated transformation. Transgenic seedlings were subsequently regenerated, and the target sites of the edited plants were analyzed via sequencing. A total of fifteen T₀ double mutants were successfully obtained. Three mutants without T-DNA insertion were screened in the T₁ generation by the PCR detection of hygromycin gene fragments, and homozygous mutants without T-DNA insertion were screened in the T₂ generation by the sequencing analysis of the mutation sites, named Huahang 48s. Huahang 48s exhibited complete sterility at 24 °C and pollen transfer at 23 °C. The 2-acetyl-1-pyrroline (2-AP) content was detected in the young panicles, leaves, and stems of Huahang 48s. The leaves of Huahang 48s had the highest 2-AP content, contrasting with the absence of 2-AP in HuaHang 48. F₁ hybrids that crossed Huahang 48s with two high-quality restorer lines were superior to the two parents in terms of yield per plant and 1000-grain weight. Huahang 48s has a certain combining ability and application potential in two-line cross breeding. The successful application of CRISPR/Cas9 technology in Huahang 48 established a foundation for developing aromatic TGMS lines, providing both theoretical insights and practical materials for breeding efforts. Full article