Deep rooting is an important factor affecting rice drought avoidance. However, few genes that control this trait have been identified in rice. In the present work, we cloned a gene, OsIAA8, associated with a rice quantitative trait locus for deep rooting. Overexpression of OsIAA8 increased deep rooting and yield under drought stress. OsIAA8, which encodes an Aux/IAA protein, interacts with auxin response factor OsARF12, and osarfl2 mutant plants also displayed increased deep rooting. The expression of several auxin transport genes (e.g., OsPIN2 and OsPIN8) was down-regulated in OsIAA8-overexpressing rice plants. OsARF12 promotes the transcription of these genes, whereas OsIAA8 inhibited this transcription activation. Furthermore, the auxin content in the roots of OsIAA8-overexpressing plants was reduced compared with wild type plants. Of five haplotypes at the OsIAA8 locus, accessions carrying Hap 2 had higher deep rooting. This study revealed that OsIAA8-OsARF12 module regulates deep rooting by inhibiting auxin transport, providing the insights into improvement of root architecture and drought resistance in rice breeding.

Lettuce is a globally important economic crop, and accurate prediction of its growth and yield is of great importance for its breeding and commercialization. In this study, we developed an image-based software for lettuce growth and yield prediction. Using a dataset of 15,187 background-free images of 237 lettuce varieties representing six major types, we trained a high-accuracy classification model (98.7%). Based on this classification model, using a generative adversarial network (GAN), we developed the first single image-based stepwise growth prediction model which could generate the images of lettuce in the coming 5 days during growth period (SSIM=0.948). Based on these developed models, we proposed three methods of image-based yield prediction, achieving a maximum prediction accuracy of 0.80. Finally, we integrated these models and methods into a user-friendly desktop application and named it the Advanced Lettuce for lettuce segmentation, classification, growth prediction, and yield prediction. Case study using an independently collected dataset further demonstrated the practical applicability of the software under realistic field conditions. Overall, this study developed an integrated lettuce growth and yield prediction software that may facilitate lettuce phenotyping, breeding, and production research.

Rice is the main food for half of the global population, but drought is one of the main abiotic stresses affecting rice yield. Therefore, breeding rice varieties with enhanced drought resistance is an essential strategy to ensure food security. Varieties with stronger drought resistance typically exhibit lower degrees of leaf withering and higher yields under drought. However, no automatic screening model for drought resistance in rice has yet been established that accounts for leaf wilting and post-drought yield. To improve the screening efficiency and accuracy, it is urgent to develop a standardized pipeline for high-throughput drought-resistance rice evaluation. This study established a set of standardized workflows for screening drought-resistance rice in complex population based on unmanned aerial vehicle (UAV) images. Firstly, we defined five levels (Level 1-5) of rice leaf withering and developed an efficient and automatic UAV RGB (red-green-blue) image processing software PLOT ASSISTANT. Secondly, based on manual measurement and UAV images, we constructed a leaf withering assessment model and a rice drought-resistance prediction model in terms of final yield under drought stress. Among leaf withering assessment models, the MobileNet model introducing spatial attention mechanism based on transfer learning exhibited the highest assessing accuracy (R2 =0.94, MSE=0.08). The rice drought-resistance prediction model introducing coordinate inverse projection successfully screened the top 5 % of droughtresistant rice varieties with the highest yield (F1 = 0.95, Precision=0.95, and Recall= 0.96). Using our method, the screening of the desirable drought-resistant rice varieties could be accomplished at the early growth stage, and will drastically reduce time and labor consumption for manual trait measurement during traditional screening. Furthermore, the screening model could work in complicated big population including not only Japonica rice but also Indica or median type between Japonica and Indica. Last, using the image-traits from the UAV, we also identified 1942 novel genetic loci associated with drought-resistance providing valuable resources for future research and breeding aimed at enhancing drought resistance of rice.

Dissecting the mechanism of drought resistance (DR) and designing drought-resistant rice varieties are promising strategies to address the challenge of climate change. Here, we selected a typical droughtavoidant (DA) variety, IRAT109, and a drought-tolerant (DT) variety, Hanhui15, as parents to develop a stable recombinant inbred line (RIL) population (F8, 1262 lines). The de novo assembled genomes of both parents were released. By resequencing of the RIL population, a set of 1 189 216 reliable SNPs were obtained and used to construct a dense genetic map. Using above- and belowground phenomic platforms and multi- modal cameras, we captured 139 040 image-based traits (i-traits) of whole-plant phenotypes in response to drought stress throughout the entire rice growth period and identified 32 586 drought-responsive quantitative trait loci (QTLs), including 2097 unique QTLs. QTLs associated with panicle i-traits occurred more than 600 times on the middle of chromosome 8, and QTLs associated with leaf i-traits occurred more than 800 times on the 50 end of chromosome 3, indicating the potential effects of these QTLs on plant phenotypes. We selected three candidate genes (OsMADS50, OsGhd8, OsSAUR11) related to leaf, panicle, and root traits, respectively, and verified their functions in DR. OsMADS50 was found to negatively regulate DR by modulating leaf dehydration, grain size, and downward root growth. A total of 18 and 21 composite QTLs significantly related to grain weight and plant biomass were also screened from 597 lines in the RIL population under drought conditions in field experiments, and the composite QTL regions showed substantial overlap (76.9%) with known DR gene regions. Based on three candidate DR genes, we proposed a haplotype design suitable for different environments and breeding objectives. This study provides a valuable reference for multimodal and time-series phenomic analyses, deciphers the genetic mechanisms of DA and DT rice varieties, and offers a molecular navigation map for breeding of DR varieties.

Agricultural production is severely affected by environmental stresses such as drought, and deep rooting is an important factor enhancing crop drought avoidance. H+-ATPases provide a transmembrane proton gradient and are thought to play a crucial role in plant growth and abiotic stress responses. However, their expression under abiotic stress and function on deep rooting is poorly understood in rice. In this study, the conserved domains, potential phosphorylation sites, and three-dimensional structures of ten Oryza sativa PM H+-ATPases (OSAs) were analyzed. Quantitative PCR analysis revealed different expression patterns of these OSA genes under hormone treatment conditions (e.g., abscisic acid) and abiotic stress conditions (e.g., drought and salt stress). Subcellular localization analysis revealed that most OSA proteins were localized to the cell membrane. Phenotype determination of OSA mutants indicated that the ratio of deep rooting (RDR) of both osa7 and osa8 mutants was significantly reduced compared to that of wild-type rice plants. Additionally, OSA haplotypes in 268 rice accessions were analyzed, and the haplotypes associated with RDR were identified. The present results provide valuable information on crucial domains, expression patterns, and functional identification of OSA paralogs to reveal their role in rice responses to abiotic stress.

The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high-throughput root phenotyping poses a great challenge. In this study, we established a cost-effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits. Based on this root phenotyping platform and RPT, we screened 18 candidate (quantitative trait loci) QTL regions from 219 rice recombinant inbred lines under drought stress and validated the drought-resistant functions of gene OsIAA8 identified from these QTL regions. This study confirmed that RPT exhibited a great application potential for processing images with various sources and for mining stress-resistance genes of rice cultivars. Our developed root phenotyping platform and RPT software significantly improved high-throughput root phenotyping efficiency, allowing for large-scale root trait analysis, which will promote the genetic architecture improvement of drought-resistant cultivars and crop breeding research in the future.

Mesocotyl elongation of rice seedlings is a key trait for deep sowing tolerance and well seedling establishment in dry direct sowing rice (DDSR) production. Subsets of the Rice Diversity Panel 1 (RDP1, 294 accessions) and Hanyou 73 (HY73) recombinant inbred line (RIL) population (312 lines) were screened for mesocotyl length (ML) via dark germination. Six RDP1 accessions (Phudugey, Kasalath, CA902B21, Surjamkuhi, Djimoron, and Goria) had an ML longer than 10 cm, with the other 19 accessions being over 4 cm. A GWAS in RDP1 detected 118 associated SNPs on all 12 chromosomes using a threshold of FDR-adjusted p < 0.05, including 11 SNPs on chromosomes 1, 4, 5, 7, 10, and 12 declared by -log10(P) > 5.868 as the Bonferroni-corrected threshold. Using phenotypic data of three successive trials and a high-density bin map from resequencing genotypic data, four to six QTLs were detected on chromosomes 1, 2, 5, 6, and 10, including three loci repeatedly mapped for ML from two or three replicated trials. Candidate genes were predicted from the chromosomal regions covered by the associated LD blocks and the confidence intervals (CIs) of QTLs and partially validated by the dynamic RNA-seq data in the mesocotyl along different periods of light exposure. Potential strategies of donor parent selection for seedling establishment in DDSR breeding were discussed.

Background Deep rooting is an important factor affecting rice drought resistance. However, few genes have been identified to control this trait in rice. Previously, we identified several candidate genes by QTL mapping of the ratio of deep rooting and gene expression analysis in rice. Results In the present work, we cloned one of these candidate genes, OsSAUR11, which encodes a small auxin-up RNA (SAUR) protein. Overexpression of OsSAUR11 significantly enhanced the ratio of deep rooting of transgenic rice, but knockout of this gene did not significantly affect deep rooting. The expression of OsSAUR11 in rice root was induced by auxin and drought, and OsSAUR11-GFP was localized both in the plasma membrane and cell nucleus. Through an electrophoretic mobility shift assay and gene expression analysis in transgenic rice, we found that the transcription factor OsbZIP62 can bind to the promoter of OsSAUR11 and promote its expression. A luciferase complementary test showed that OsSAUR11 interacts with the protein phosphatase OsPP36. Additionally, expression of several auxin synthesis and transport genes (e.g., OsYUC5 and OsPIN2) were down-regulated in OsSAUR11-overexpressing rice plants. Conclusions This study revealed a novel gene OsSAUR11 positively regulates deep rooting in rice, which provides an empirical basis for future improvement of rice root architecture and drought resistance.

Phosphate (Pi) is indispensable for the growth and development of plant, and low-Pi stress is a major limitation for crop growth and yield worldwide. The tolerance to low-Pi stress varied among rice germplasm resources. However, the mechanisms underlying the tolerance of rice to low-Pi stress, as a complex quantitative trait, are not clear. We performed a genome-wide association study (GWAS) through a diverse worldwide collection of 191 rice accessions in the field under normal-Pi and low-Pi supply in two years. Twenty and three significant association loci were identified for biomass and grain yield per plant under low-Pi supply respectively. The expression level of OsAAD as a candidate gene from a associated locus was significantly up-regulated after low-Pi stress treatment for five days and tended to return to normal levels after Pi re-supply in shoots. Suppression of OsAAD expression could improve the physiological phosphorus use efficiency (PPUE) and grain yields through affecting the expression of several genes associated with GA biosynthesis and metabolism. OsAAD would be a promising gene for increasing PPUE and grain yield in rice under normal- and low-Pi supply via genome editing.

BACKGROUND With the development of high-throughput genome sequencing and phenotype screening techniques, there is a possibility of leveraging multi-omics to speed up the breeding process. However, the heterogeneity of big data handicaps the progress and the lack of a comprehensive database supporting end-to-end association analysis impedes the efficient use of these data. METHODS In response to this problem, a scalable entity-relationship model and a database architecture are firstly proposed in this paper to manage the cross-platform data sets and explore the relationship among multi-omics, and finally accelerate our breeding efficiency. First, the targeted omics data of crops should be normalized before being stored in the database. A typical breeding data content and structure is demonstrated with the case study of rice (Oryza sativa L). Second, the structure, patterns and hierarchy of multi-omics data are described with the entity-relationship modeling technique. Third, some statistical tools used frequently in the agricultural analysis have been embedded into the database to help breeding. RESULTS As a result, a general-purpose scalable database, called GpemDB integrating genomics, phenomics, enviromics and management, is developed. It is the first database designed to manage all these four omics data together. The GpemDB involving Gpem metadata-level layer and informative-level layer provides a visualized scheme to display the content of the database and facilitates users to manage, analyze and share breeding data. CONCLUSIONS GpemDB has been successfully applied to a rice population, which demonstrates this database architecture and model are promising to serve as a powerful tool to utilize the big data for high precise and efficient research and breeding of crops.

Deep rooting is an important trait in rice drought resistance. Genetic resources of deep-rooting varieties are valuable in breeding of water-saving and drought-resistant rice. In the present study, 234 BC2F7 backcross introgression lines were derived from a cross of Dongye 80 (an accession of Dongxiang wild rice as the donor parent) and R974 (an indica restorer line as the recurrent parent). A genetic linkage map containing 1 977 bin markers was constructed by ddRADSeq for QTL analysis. Thirty-one QTLs for four root traits (the number of deep roots, the number of shallow roots, the total number of deep roots and the ratio of deep roots) were assessed on six rice chromosomes in two environments (2020 Shanghai and 2021 Hainan). Two of the QTLs, qDR5.1 and qTR5.2, were located on chromosome 5 in a 70-kb interval. They were detected in both environments. qDR5.1 explained 13.35% of the phenotypic variance in 2020 Shanghai and 12.01% of the phenotypic variance in 2021 Hainan. qTR5.2 accounted for 10.88% and 10.93% of the phenotypic variance, respectively. One QTL (qRDR2.2) for the ratio of deep roots was detected on chromosome 2 in a 210-kb interval and accounted for 6.72% of the phenotypic variance in 2020. The positive effects of these three QTLs were all from Dongxiang wild rice. Furthermore, nine and four putative candidate genes were identified in qRDR2.2 and qDR5.1/qTR5.2, respectively. These findings added to our knowledge of the genetic control of root traits in rice. In addition, this study will facilitate the future isolation of candidate genes of the deep-rooting trait and the utilization of Dongxiang wild rice in the improvement of rice drought resistance.

发展节水抗旱稻对于在“资源节约、环境友好”基础上保障粮食安全具有重大意义。受限于抗旱性状的复杂性和缺乏精准抗旱鉴定平台,基因组序列等大量生物信息的潜力无法得到有效挖掘和应用。发展水稻抗旱表型组学可为稻种资源抗旱性评价、基因发掘与利用提供新思路。通过简略介绍国内外作物表型平台的研发和应用,重点阐述节水抗旱稻表型组学平台的建设和应用。目前,该平台集成了可见光RGB成像、红外成像、高光谱成像和激光雷达成像共4种采集单元,可对水稻的抗旱性进行连续无损的图像采集和分析鉴定,是国内外进行水稻抗旱高通量表型研究和应用较为先进和全面的设施之一。

水稻冠层温度是指示水稻生理状态的重要指标,在水稻抗旱育种中具有重要意义.本试验利用红外热成像技术并结合多种田间环境传感器,对4个水稻材料的冠层温度进行了分析.结果表明:不同水稻材料的冠层温度存在显著差异,旱处理会造成水稻冠层温度显著升高,水旱处理的水稻冠层温度差出现的时间与水旱处理土壤水势差出现的时间一致.利用环境数据和距抽穗期天数对水旱处理下水稻冠层温度和冠层温度差进行多元线性回归建模,发现空气温度、土壤温度、光照强度对水稻冠层温度均有显著影响;而水旱土壤水势差、距抽穗期天数和空气湿度对水稻冠层温度差具有显著影响,决定系数均在0.80左右.本研究结果对运用高通量表型选择技术开展水稻抗旱育种具有一定的指导作用.

根系是植物吸收水分和养分的主要器官,培育发达的深根系统是水稻抗旱育种的重要目标。该文系统介绍了水稻根系生长角度、根系向重力性、根系穿透力、深层土壤根系分布特征等深根性相关性状的遗传研究进展,指出今后需针对目标环境开展水稻深根性QTL发掘与利用、加快主效深根性QTL的验证及其在水稻抗旱分子育种的应用。

栽培稻作为主要的粮食作物,消耗了大量的农业用水。解析栽培稻抵抗干旱胁迫的调控机制,培育节水抗旱稻,对于节约淡水资源、保障粮食安全具有重要意义。在长期的进化过程中,栽培稻形成了渗透调节、活性氧有毒物质的清除、气孔调节等多种生理生化机制来适应干旱胁迫,这其中涉及大量基因参与的分子调控途径。笔者结合其研究对近年来在栽培稻响应干旱胁迫的生理生化过程、抗旱基因挖掘及其参与的分子调控机制方面取得的进展进行综述,并对存在的问题和未来的发展趋势进行讨论。

为探索利用深根比作为鉴定指标评价水稻避旱性的标准方法,规范"篮子法"进行深根比鉴定的操作流程,引入两个不同深根比参比品种(CK1和CK2),根据同样生长条件下测试材料与参比材料深根比的比值(高值组H>3/4CK2、低值组L<3/4CK1、中值组3/4CK1

The increasing concentration of greenhousegases(GHGs)in Earth's atmosphere leads to global warming,which further causes a series of climate changes and does great harm to both human society and natural ecosystems.Agricultural GHG emissions,mainly in the form of methane(CH4)and nitrous oxide(N2O),are a significant source of GHGs,accounting for～14％ to-tal global GHGs(Zhang et al.,2022).

Since gravitropism is one of the primary determinants of root development, facilitating root penetration into soil and subsequent absorption of water and nutrients, we studied this response in rice. The gravitropism of 226 Chinese rice micro-core accessions and drought-resistant core accessions were assessed through the modified gravity-bending experiment and genome-wide association analysis (GWAS) was used to map the associated QTLs. The average value of gravitropic response speed of seminal roots was 41.05°/h, ranging from 16.77°/h to 62.83°/h. The gravity response speed of Indica (42.49°/h) was significantly (P < 0.002) higher than Japonica (39.71°/h) subspecies. The gravitational response speed of seminal roots was significantly positively correlated with the number of deep roots (r = 0.16), the growth speed of seminal roots (r = 0.21) and the drought resistance coefficient (r = 0.14). In total, 3 QTLs (quantitative traits) associated with gravitropic response speed were identified on chromosome 4, 11 and 12. There are some known QTLs relating to roots traits and drought resistance located nearby the QTLs identified here, which confirms the close relationship between radicle gravitropism and the drought resistance. From within these intervals, 5 candidate genes were screened and verified by qPCR in a few rice varieties with extreme phenotypic values, demonstrating that gene LOC_Os12g29350 may regulate gravitropism negatively. This may be a promising candidate to be confirmed in further studies.

The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At present, crop spike-shaped point cloud segmentation has problems such as fewer samples, uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement of point clouds, and lack of targeted network models. The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process. Then, the rice ear point cloud was collected, and the rice ear point cloud data set was made. In addition, data argumentation is used to improve sample utilization efficiency and training accuracy. Finally, a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy. Specifically, the design of Panicle-3D is aimed at the multiscale characteristics of plant organs, combined with the structure of PointConv and long and short jumps, which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling. After comparison experiments, the segmentation accuracy of Panicle-3D reaches 93.4%, which is higher than PointNet. Panicle-3D is suitable for other similar crop point cloud segmentation tasks.

16 The automated measurement of crop phenotypic parameters is of great significance to 17 the quantitative study of crop growth. The segmentation and classification of crop point 18 cloud help to realize the automation of crop phenotypic parameter measurement. At 19 present, crop spike-shaped point cloud segmentation has problems such as fewer samples, 20 uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement 21 of point clouds, and lack of targeted network models. The traditional clustering method 22 can realize the segmentation of the plant organ point cloud with relatively independent 23 spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level 24 point cloud scanning apparatus based on a structured-light projection module to facilitate 25 the point cloud acquisition process. Then, the rice ear point cloud was collected, and the 26 rice ear point cloud data set was made. In addition, data argumentation is used to improve 27