Rice seed variety classification is crucial for seed quality control and breeding, yet practical deployment is often limited by the computational and memory demands of modern deep models. We propose SimpleEfficientCNN (SimpleEfficient: simple & efficient; CNN: convolutional neural network), an ultra-lightweight convolutional network built on depthwise separable convolutions for efficient fine-grained seed classification. Experiments were conducted on three datasets with distinct imaging characteristics: a self-constructed Guangdong dataset (7 varieties; 10,500 seeds imaged once and expanded to 112 K images via post-split augmentation), the public M600 rice subset (7 varieties; 9100 original images expanded to 112 K images using the same post-split augmentation pipeline for scale-matched comparison), and the International dataset (75 K images; official train/validation/test split provided by the original release and used as-is without any preprocessing or augmentation, 5 varieties). SimpleEfficientCNN achieved 98.52%, 88.07%, and 99.37% accuracy on the Guangdong, M600, and International test sets, respectively. With only 0.231 M parameters (≈92× fewer than ResNet34), it required 20.5 MB peak GPU memory and delivered 2.0 ms GPU latency (RTX 4090D, batch = 1, FP32) and 1.8 ms single-thread CPU median latency (Ryzen 9 7950X3D, batch = 1, FP32). These results indicate that competitive accuracy can be achieved with substantially reduced model size and inference cost, supporting deployment in resource-constrained agricultural settings.

The integration of visual and thermal infrared sensors is crucial for rice phenotyping, and image registration serves as a prerequisite for data fusion and phenotypic analysis. The mainstream image registration methods rely on feature descriptor derived from hand crafted or deep learning for keypoint matching. Such methods cannot address the challenges brought by abundant self-similarities of rice canopy and modal variation between sensors. To address this problem, this study employed the adaptive connection line filtering (ACLF) for homography estimation. Specifically, this framework utilized the geometric topology from global pairwise correspondences, and introduced a connection line filtering (CLF) algorithm to eliminate keypoint mismatches, followed by a fine-grained matching (FGM) approach to refine the keypoint location. Also, color contrast enhancement (CCE) was applied prior to representation learning to enhance the keypoint detection. Cross year and cross sites field experiments demonstrated the effectiveness of our method. Specifically, the proposed method outperformed the state of the art by 1.98 in RMSE at seedling stage, and 0.38 in RMSE at tillering stage. This study lays the foundation for phenotypic analysis using UAV-based visual and thermal infrared data, which can accelerate the process of large scale rice breeding and ensure food security. Our data and code will be available at: https://github.com/huanghsheng/ACLF.

Rapid Visco Analyzer (RVA) profile characteristics are important indicators of rice (Oryza sativa L.) eating quality. In this study, based on the high-density genetic linkage map constructed under the genetic background of Yuexianghzan (YXZ) and Shengbasimiao (SBSM), combined with the RVA profile characteristic data of recombinant inbred lines (RILs) grown in two environments, QTL scanning was performed using the ridge regression analysis method. A total of 59 QTLs associated with RVA profile characteristics were detected across 11 chromosomes in the two environments, with individual QTLs explaining 0.12% to 85.16% of the phenotypic variation. Moreover, 11 QTLs were repeatedly detected in two environments with large effects. The QTL located in the 1.44-1.85 Mb interval on chromosome 6 simultaneously controlled eight RVA profile characteristics and contained the cloned waxy (Wx) gene. Additionally, the intervals 20.58-20.70 Mb on chromosome 5 and 24.96-25.42 Mb on chromosome 8 were repeatedly mapped and influenced multiple RVA characteristics. Based on gene annotation information, a total of nine candidate genes (LOC_Os05g34730, LOC_Os05g34830, LOC_Os05g34854, LOC_Os06g03910, LOC_Os06g04200, LOC_Os06g42720, LOC_Os08g39830, LOC_Os08g39850, and LOC_Os08g39860) that directly or indirectly influence the starch synthesis pathway were identified. The results of this study lay a foundation for further map-based cloning of genes related to rice RVA profile characteristics and molecular design breeding.

'Heguang Simiao'is a new conventional rice variety with high harvest index and excellent comprehensive traits.It has a good plant type,high yield and good rice quality.When participating in the regional test of Guangdong Province and the national late indica rice area in the middle and lower reaches of the Yangtze River,the rice quality reached the first grade of high quality awarded by the Ministry and the first grade of high quality in the agricultural industry.The grains of rice are crystal clear and lustrous,the maximum yield per 667 m² of the regional trial reached 608.6 kg.It was approved by Guangdong Province in 2019,it was approved by the state twice successively in 2020 and 2023.Meanwhile,the variety is also an excellent hybrid rice restorer line.Seven hybrid rice combinations,including'Quanyou Heguang Simiao'and'Huiliangyou Guangsimiao',have been developed and approved based on it as the male parent.These combinations are characterized by high and stable yield,good quality and good adaptability,demonstrating the"core"role of'Heguang Simiao'seedlings as superior parent.'Heguang Simiao'seedlings can be promoted and cultivated in seven regions including Guangdong,Guangxi,Hainan,Fujian,Jiangxi,Hunan and Zhejiang.It was selected as a leading agricultural variety in Guangdong Province in 2024-2025,and is currently a key promoted variety in cities such as Huizhou,Zhongshan city and Haifeng County in Guangdong Province.It has a good prospect for promotion and application in the double-cropping rice areas of South China and the late indica rice areas in the middle and lower reaches of the Yangtze River.

BackgroundRapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of estimation models for rice yield-related trait indicators (leaf nitrogen concentration, LNC; leaf area index, LAI; aboveground biomass, AGB; and grain yield, GY) through a strategy of "spectral data + texture data + dimensionality reduction + machine learning" is highly important.MethodsBetween 2022 and 2023, hyperspectral canopy images, the LNC, LAI, AGB, and GY were collected synchronously. Then, dimensionality reduction was performed on the preprocessed spectral data using the Pearson correlation coefficient method, the successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) to select sensitive wavelengths. Estimation models were constructed using artificial neural networks (ANNs), support vector machine regression, one-dimensional convolutional neural networks, and long short-term memory networks. By extracting the texture features corresponding to sensitive wavelengths, high-precision estimation models were constructed using a "spectral data + texture data + dimensionality reduction + machine learning" method.ResultsSPA-ANN provided the best prediction for LNC (R2 = 0.82, RMSE = 3.68 g/kg) and LAI (R2 = 0.75, RMSE = 0.47), while CARS-ANN was optimal for AGB (R2 = 0.90, RMSE = 79.05 g/m2) and GY (R2 = 0.63, RMSE = 0.59 t/ha). Adding texture features increased R2 by up to 9.9% and reduced RMSE by up to 27.2%.ConclusionThe optimized method can significantly increase the accuracy of estimation models. The results provide a scientific basis and technical data for the precise diagnosis of rice yield-related traits.

Rice is an important food crop in China, and its quality and safety are closely related to national food security and public health. In recent years, the problem of cadmium exceeding the standard in rice has become increasingly prominent, which has become a key factor restricting the sustainable development of rice and threatening public health. Therefore, indepth exploration of the molecular mechanism of low cadmium absorption in rice and environmental regulation strategies, as well as the cultivation of low cadmium rice varieties, has become a major research direction in the agricultural field. This article systematically reviews the key environmental factors affecting cadmium absorption and accumulation in rice, clearly pointing out that temperature, water, and nutrient element are the core regulatory factors. At the genetic mechanism level, this article focuses on the quantitative trait loci (QTL) controlling low cadmium accumulation in rice and the related genes affecting low cadmium rice. Currently, the genes that are widely used include OsNRAMP5, OsHMA3, OsLCT1, OsIRT1, OsHMA2, etc. These genes are systematically classified into three core gene families: transport and compartmentalization, regulation and response, and detoxification and chelation. A schematic diagram of the role of these low cadmium genes in organelles such as the cytoplasm and vacuole is drawn, visually presenting the complete molecular pathway of "absorption - transport - chelation - compartmentalization", providing a reference for the mining of low cadmium genes. This article also summarizes the progress of low cadmium rice screening in various provinces, and takes Guangdong Province as an example to detail its "introduction + localization" strategy for low cadmium variety cultivation, that is successfully introducing the nationally approved low cadmium variety 'Xizi 3', while vigorously promoting and demonstrating local excellent varieties such as 'Yuehe Simiao', 'Guanghong 3', and 'Taifeng You 208', gradually establishing a scientific and reasonable low cadmium rice variety system. In addition, this article systematically reviews the main breeding techniques for existing low cadmium rice, covering traditional conventional breeding, molecular marker-assisted selection, and gene editing and other cutting-edge methods, which can provide a reference for the subsequent mining of low cadmium genes and the cultivation of new low cadmium accumulation germplasm and varieties.

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote research on high harvest indices and variety breeding; (2) Methods: In this study, we proposed a method to predict the harvest index during the rice growth period based on uncrewed aerial vehicle (UAV) remote sensing technology. UAV obtained visible light and multispectral images of different varieties, and the data such as digital surface elevation, visible light reflectance, and multispectral reflectance were extracted after processing for correlation analysis. Additionally, characteristic variables significantly correlated with the harvest index were screened out; (3) Results: The results showed that TCARI (correlation coefficient −0.82), GRVI (correlation coefficient −0.74), MTCI (correlation coefficient 0.83), and TO (correlation coefficient −0.72) had a strong correlation with the harvest index. Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. The results showed that the Stacking model performed best in predicting the harvest index (R2 reached 0.88) and had a high prediction accuracy. (4) Conclusions: Therefore, the harvest index can be accurately predicted during rice growth through UAV remote sensing images and machine learning technology. This study provides a new technical means for screening high harvest index in rice breeding, provides an important reference for crop management and variety improvement in precision agriculture, and has high application potential.

【Objective】This study aims to compare the effects of ratoon and direct seeding cultivation methods on rice yield, and identify high-performing ratoon rice and direct-seeded rice varieties. It will provide important references for variety selection of late-season ratoon rice and direct-seeded rice in South China, and offer theoretical support for regional variety optimization and the application of simplified cultivation techniques.【Method】Field experiments were conducted in Guangzhou using 15 conventional rice varieties for ratoon cultivation and 15 varieties (seven conventional and eight hybrid) for direct seeding. In the ratoon experiment, the main crop was manually harvested at maturity, leaving a 15 cm stubble, and the ratoon crop was allowed to regrow naturally. Yields of both the main and ratoon crops were measured based on 18 plants, and growth durations were recorded. In the direct-seeding experiment, both transplanting and direct-seeding methods were applied to assess seedling establishment rate, heading date, maturity period, plant height, and yield performance.【Result】All 15 ratoon varieties exhibited lower yields than their main crops, with an average reduction of 33.69%. 'Hefuzhan' achieved the highest ratoon yield (393.00 g) and annual total yield (877.58 g), followed by 'Yueyitang 1. Henongsimiao' 'Hefuzhan' and 'Yueyitang 1' also had the longest ratoon growth duration (80 days). In the direct-seeding trial, 'Yuenuo 2' recorded the highest seedling establishment rate (77.34%). 'Tailiangyou 1' 'Yuehesimiao', and 'Hefuzhan' exhibited the longest growth duration (111 days) under direct seeding, while 'Yuenuo 2' 'Yuefujinzhan' and 'Huanghuazhan' matured 6-7 days earlier than their transplanted counterparts. The highest yield under direct seeding was obtained by 'Wolianyouyueyasimiao' (646.17 g).【Conclusion】Based on ratoon yield and total productivity, 'Hefuzhan' and 'Yueyitang 1' are recommended as promising ratoon rice varieties for Guangdong and the broader South China region. Considering seedling establishment and yield performance, 'Yuenuo 2' 'Yuefujinzhan' and 'Huanghuazhan' are identified as representative varieties suitable for direct-seeding cultivation.

High harvest index (HI), defined as the grain yield-to-biomass ratio (>= 0.55), reflects a well-balanced source-sink relationship. It is a key trait in rice high-HI breeding programs, which has proven to be a successful strategy for developing super high-yield rice varieties. However, its genetic basis remains elusive. This study conducted QTL analysis for HI-related traits using a recombinant inbred line (RIL) population derived from a cross between a geng/japonica cultivar, Lijiangxintuanheigu (LTH), and a high HI xian/indica variety, Yuenongsimiao (YNSM). A high-density genetic map with 6674 bin markers identified 97 QTLs across 12 HI-related traits, forming 13 QTL clusters that affect the source-sink related traits in rice. These bin markers were converted from 1,009,324 high-quality SNPs sourced from the sequenced RIL population. Notably, qRSC1 (QTL cluster of rice source capacity 1), which included qFLL1, qSTW1, qBM1.2, and qHI1, was tightly linked to the semi-dwarf gene sd1 and collectively shaped the high HI plant architecture of YNSM. In contrast, qRSC3(qFLL3/qFLW3/qSTW3/qBM3.1/qHI3.1) exhibited an opposite effect and positively regulated source-related traits. Among nine QTLs associated with yield per plant (YPP), only qYPP2.2, part of qRSS2 (QTL cluster of rice sink size 2), was consistently detected over two consecutive years. qRSS2 governed sink size by integrating multiple yield-related QTLs, including qYPP2.2. Overall, qRSC1, qRSC3, and qRSS2 collectively optimized source-sink balance, enabling YNSM's high HI and high yields. These findings provide insights into the genetic basis of high HI in YNSM and may facilitate breeding high-yielding rice with superior HI.

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.

Objective High quality is one of the core objectives in rice breeding, encompassing multiple aspects such as processing quality, appearance quality, cooking and eating quality, as well as nutritional quality. ‘Yuehesimiao’ is a high-quality main rice variety developed by the Rice Research Institute of Guangdong Academy of Agricultural Sciences. It exhibits strong lodging resistance, high stress tolerance, and excellent grain quality, and has been widely adopted. This study was aimed to systematically analyze the grain quality and associated genotypes of ‘Yuehesimiao’, further explore its superior traits, and provide a theoretical support for rice breeding and variety improvement.MethodUsing high-quality varieties ‘Yuexiangzhan’ and ‘Yuzhenxiang’ as controls, the grain quality traits of ‘Yuehesimiao’ (including grain shape, amylose content, gel consistency) were measured, and its starch viscosity profile (including peak viscosity, cool paste viscosity and setback viscosity, etc.) were characterized. Simultaneously, PCR amplification and genotyping were performed for quality-related genes. Meanwhile, the application potential of ‘Yuehesimiao’ in quality breeding was explored. ResultThe grain shape of ‘Yuehesimiao’ was intermediate between those of the two control varieties, with its grain morphology primarily governed by gs3 and gw7 genes. The processing quality traits of ‘Yuehesimiao’ were excellent, featuring low amylose content and high gel consistency. Its superior cooking and eating quality was associated with Wxb gene. Additionally, ‘Yuehesimiao’ was demonstrated with high protein and fatty acid contents, with these characteristics being particularly pronounced under early-season cultivation conditions. The quality characteristics of hybrid and conventional rice varieties developed using ‘Yuehesimiao’ as a restorer line or a founder parent were excellent. Conclusion‘Yuehesimiao’ is a medium-to-long grain rice variety with excellent appearance quality and superior processing quality. These findings provide a theoretical basis for developing new rice varieties featuring synergistic improvement of yield, stress resistance, and grain quality.

Soil and plant analyzer development (SPAD) value and leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield and quality. However, there are few reports on the use of machine learning algorithms based on hyperspectral monitoring to synchronously predict SPAD value and LNC of indica rice. Meixiangzhan No. 2, a high-quality indica rice, was grown at different nitrogen rates. A hyperspectral device with an integrated handheld leaf clip-on leaf spectrometer and an internal quartz-halogen light source was conducted to monitor the spectral reflectance of leaves at different growth stages. Linear regression (LR), random forest (RF), support vector regression (SVR), and gradient boosting regression tree (GBRT) were employed to construct models. Results indicated that the sensitive bands for SPAD value and LNC were displayed to be at 350–730 nm and 486–727 nm, respectively. Normalized difference spectral indices NDSI (R497, R654) and NDSI (R729, R730) had the strongest correlation with leaf SPAD value (R = 0.97) and LNC (R = −0.90). Models constructed via RF and GBRT were markedly superior to those built via LR and SVR. For prediction of leaf SPAD value and LNC, the model constructed with the RF algorithm based on whole growth periods of spectral reflectance performed the best, with R2 values of 0.99 and 0.98 and NRMSE values of 2.99% and 4.61%. The R2 values of 0.98 and 0.83 and the NRMSE values of 4.88% and 12.16% for the validation of leaf SPAD value and LNC were obtained, respectively. Results indicate that there are significant spectral differences associated with SPAD value and LNC. The model built with RF had the highest accuracy and stability. Findings can provide a scientific basis for non-destructive real-time monitoring of leaf color and precise fertilization management of indica rice.

Aroma is a crucial determinant of rice taste quality, with volatile organic compounds (VOCs) playing a key role in defining this characteristic. However, limited research has explored the dynamic changes in these aromatic substances during the ripening stages of rice grains. In this study, we analyzed VOCs in rice grains across four ripening stages post-flowering using headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC-MS). A total of 417 VOCs were identified, among which 65 were determined to be key aroma-active compounds based on relative odor activity value (rOAV) analysis. Most of these aroma-active compounds exhibited an accumulation pattern as the grains matured. Notably, 5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone and 2-Methyloxolan-3-one had the largest rOAV values. Additionally, (Z)-6-nonenal, (Z,Z)-3,6-nonadienal, 2-thiophenemethanethiol, 5-methyl-2-furanmethanethiol, 2,2,6-trimethyl-cyclohexanone, and 3-octen-2-one were identified as potential key markers for distinguishing rice-grain maturity stages. Moreover, 2-acetyl-1-pyrroline (2-AP), heptanal, and 1-nonanol were identified as marker metabolites differentiating aromatic from non-aromatic brown rice. These findings contribute to a deeper understanding of the dynamic variation and retention of aroma compounds during rice-grain ripening, and they offer valuable insights into the improvement of fragrant rice varieties.

Grain shape in rice (Oryza sativa L.) is a complex trait governed by multiple quantitative trait loci (QTLs). To dissect the genetic basis of rice shape, QTL analysis was conducted for milled rice grain width (MGW), milled rice grain length (MGL), and milled rice length-to-width ratio (MLWR) using a recombinant inbred line (RIL) population of F10 and F11 generations derived from a cross between Yuexiangzhan and Shengbasimiao. A high-density genetic map consisting of 2412 bins was constructed by sequencing 184 RILs, spanning a total length of 2376.46 cM. A total of 19 QTLs related to MGL, MGW, and MLWR were detected under two environments. The range of phenotypic variation attributed to individual QTL ranged from 1.67% to 32.08%. Among those, a novel locus for MGL, MGW and MLWR, designated as qMLWR3.2, was pinpointed within a specific ~0.96-Mb region. Two novel loci for MGW and MLWR, qMLWR11.1 and qMLWR11.2, were verified within ~1.22-Mb and ~0.52-Mb regions using three RIL-developed populations, respectively. These findings lay the foundation for further map-based cloning and molecular design breeding in rice.

Rice globally faces yield reduction due to various diseases and pests, posing a significant threat to food security. Accurate identification of rice diseases and pests in real field conditions is challenging owing to the chaotic background and varying sizes of affected areas in the captured images. In this study, we propose a lightweight hybrid model named DM-ConvTNet, which integrates convolution and transformer structures to address this issue. Our model incorporates a dual-branch joint attention mechanism, aiding the convolutional layers in mitigating interference from complex backgrounds during feature extraction. Then, the linear vision transformer module focuses on the global disease information in the images. Finally, a lightweight version of the Inception module is employed for multi-scale feature extraction of high-level semantic information. Experimental results demonstrate that DM-ConvTNet excels in balancing model computational requirements and recognition performance. On two real-field rice disease datasets, DM-ConvTNet achieves impressive recognition accuracy, outperforming other state-of-the-art lightweight models. These results validate the effectiveness of our approach, highlighting its practical utility in addressing the challenges of rice disease and pest identification in complex field environments.

Rice is one of the most important crops in the world,and safe production of rice is related to food safety issues.Rice blast,caused by Magnaporthe oryzae,is a worldwide fungal disease that causes serious losses to rice production.Compared with chemical pesticides control,the breeding and application of disease-resistant cultivars is the most economical and effective way to control the disease.However,the complex and diverse population of blast fungus in the field,excessive use of chemical pesticides,change of temperature environment and other factors cause rapid evolution of M.oryzae isolates,and the resistance of cultivars can only last for 3-5 years.Magnaporthe oryzae generates new races through the mutation of avirulent genes,which can escape or suppress the immune system of rice,causing infection and disease.At present,26 avirulent genes in blast fungus have been identified,and 14 of them have been cloned,which plays an important role in the infection and colonization of pathogens and in interfering with the immune response of host plants.Further studies on the interaction mechanism of M.oryzae effector protein and rice resistance protein have also been conducted.Understanding the pathogenic mechanism of blast fungus and molecular mechanism of its interaction with rice will help to better understand the pathway of pathogens and the immune response of plant disease resistance genes,to formulate more efficient and green control measures.This review summarized the process of M.oryzae effector protein translocation and secretion in rice cells,the research advances in the interaction between effector proteins and disease-resistance proteins and the regional distribution of effector proteins.And the opportunities and challenges in current researches were discussed and prospected,with an aim to provide references for the molecular mechanism of the interaction between rice and blast fungus,disease-resistance breeding and disease prevention and control strategies.

Rice quality is one of the main targets of rice breeding and is a complex trait that involves grain appearance, milling, cooking, eating and nutritional quality. For many years, rice breeding has contended with imbalances in rice yield, quality, and disease and lodging resistance. Here, the milling and appearance quality, cooking quality, starch rapid viscosity analyzer (RVA) profile, and nutritional quality of grains of Yuenongsimiao (YNSM), an indica rice variety with high yield, high quality and disease resistance, were determined. YNSM had excellent appearance and quality, with low amylose contents and high gel consistency, and these characteristics exhibited significant correlations with the RVA profile such as hot paste viscosity, cool paste viscosity, setback viscosity, and consistency. Moreover, 5 genes related to length-to-width ratio (LWR) as well as the Wx gene were used to detect the main quality genotype of YNSM. The results showed that YNSM is a semilong-grain rice with a relatively high brown rice rate, milled rice rate and head rice yield and low chalkiness. The results indicated that the LWR and food quality of YNSM might be related to gs3, gw7 and Wxb. This study also reports the quality characteristics of hybrid rice developed using YNSM as a restorer line. The quality characteristics and the genotype for grain quality determined through gene analysis in YNSM may facilitate the breeding of new rice varieties that achieve a balance of grain yield, resistance and quality.

[Objective]By measuring and analyzing the physical characteristics of the basal stems of rice,the relationship between these traits and the stem breaking resistance was explored,and the core traits affecting the basal stem breaking resistance of rice were identified,providing a theoretical basis for the breeding of rice lodging-resistant varieties.[Method]The study analyzed the lodging resistance-related agronomic traits,including plant height,single plant bending resistance,basal internode breaking resistance,and stem structure,of the rice variety Yuehesimiao,the lodging-susceptible variety Xiangyaxiangzhan,and Yuehesimiao×Xiangyaxiangzhan F2 segregation populations.The influence of each trait on lodging resistance of rice was evaluated by correlation coefficient.[Result]There are significant differences in lodging resistance between the two varieties of Yuehesimiao and Xiangyaxiangzhan.The N2(second internode from the base)breaking resistance(16.58 N),N2 wall thickness(1.02 mm),N2 filling degree(0.19 g/cm),and N2 stem diameter(6.21 mm)of Yuehesimiao are significantly larger than those of Xiangyaxiangzhan(N2 breaking resistance:9.93 N,N2 wall thickness:0.66 mm,N2 filling degree:0.11 g/cm,N2 stem diameter:4.60 mm).The N2 breaking resistance,N2 length,N2 stem diameter,stem wall thickness,fresh weight and filling degree of the F2 population show continuous distribution and are controlled by polygenes.The correlation analysis results of the F2 population show that,N2 breaking resistance is positively correlated with plant height(r=0.18),N2 breaking resistance is significantly positively correlated with N2 wall thickness(r=0.75**).N2 breaking resistance is significantly positively correlated with N2 filling degree(r=0.67*).N2 breaking resistance is positively correlated with N2 minor axis(r=0.52)and N2 stem diameter is negatively correlated with N2 length(r=-0.20).[Conclusion]The genetic mechanism of lodging resistance in the F2 population of Yuehesimiao×Xiangyaxiangzhan shows that N2 wall thickness is significantly positively correlated with N2 breaking resistance and is the core trait of stem lodging resistance,which is a quantitative trait controlled by polygenes.Taking N2 wall thickness as the research focus will greatly reduce the complexity of previous research methods for lodging resistance,which clarifies the research goals and directions for future lodging resistance-related researches.

Small auxin-up-regulated RNAs (SAURs) are genes rapidly activated in response to auxin hormones, significantly affecting plant growth and development. However, there is limited information available about the specific functions of SAURs in rice due to the presence of extensive redundant genes. In this study, we found that OsSAUR10 contains a conserved downstream element in its 3′ untranslated region that causes its transcripts to be unstable, ultimately leading to the immediate degradation of the mRNA in rice. In our investigation, we discovered that OsSAUR10 is located in the plasma membrane, and its expression is regulated in a tissue-specific, developmental, and hormone-dependent manner. Additionally, we created ossaur10 mutants using the CRISPR/Cas9 method, which resulted in various developmental defects such as dwarfism, narrow internodes, reduced tillers, and lower yield. Moreover, histological observation comparing wild-type and two ossaur10 mutants revealed that OsSAUR10 was responsible for cell elongation. However, overexpression of OsSAUR10 resulted in similar phenotypes to the wild-type. Our research also indicated that OsSAUR10 plays a role in regulating the expression of two groups of genes involved in auxin biosynthesis (OsYUCCAs) and auxin polar transport (OsPINs) in rice. Thus, our findings suggest that OsSAUR10 acts as a positive plant growth regulator by contributing to auxin biosynthesis and polar transport.

Hybrid rice breeding is an important strategy for enhancing grain yield. Breeding high-performance parental lines and identifying combining abilities is a top priority for hybrid breeding. Yuenongsimiao (YNSM) and its derivative variety Yuehesimiao (YHSM) are elite restorer lines with a high ability of fertility restoration, from which 67 derived hybrid combinations have been authorized to different degrees in more than 110 instances in China. In this study, we found that YNSM and YHSM contained three candidate restorer-of-fertility (Rf) genes, Rf3, Rf4, and Rf5/Rf1a, that might confer their restoration ability. Subsequently, we investigated heterosis and combining ability of YNSM and YHSM using 50 F1 hybrids from a 5 × 10 incomplete diallelic mating design. Our results indicated that hybrid combinations exhibited significant genetic differences, and the additive effects of the parental genes played a preponderant role in the inheritance of observed traits. The metrics of plant height (PH), 1000-grain weight (TGW), panicle length (PL), and the number of spikelets per panicle (NSP) were mainly affected by genetic inheritance with higher heritability. Notably, the general combining ability (GCA) of YHSM exhibited the largest positive effect on the number of grains per panicle (NGP), NSP, PL, and TGW. Thus, YHSM had the largest GCA effect on yield per plant (YPP). In addition, the GCA of YNSM exhibited a positive impact on YPP, mainly due to the critical contribution of seed setting percentage (SSP). Moreover, YNSM and YHSM exhibited negative GCA effects on PH, implying that YNSM and YHSM could effectively enhance plant lodging resistance by reducing the plant height of the derived hybrids. Remarkably, among the hybrids, Yuanxiang A/YNSM (YXA/YNSM), Shen 08S/Yuemeizhan (S08S/YMZ), and Quan 9311A/YHSM (Q9311A/YHSM) represent promising new combinations with a higher specific combining ability (SCA) effect value on YPP with a value more than 3.50. Our research thus highlights the promising application for the rational utilization of YNSM and YHSM in hybrid rice breeding.