【Objective】To meet the increasing food demand driven by population growth and environmental changes, it is necessary to continuously cultivate varieties with high yield, good quality, and multiple resistances. Efficiently create new germplasm with rich genetic backgrounds and genetic diversity to provide a reference for breeding new varieties that balance multiple excellent traits. 【Method】The Sanming dominant genic male sterile material was used to simplify the hybridization procedure. It was hybridized with multiple parents with distant geographical relationships to aggregate multiple excellent traits. Aiming at problems such as a narrow genetic basis and the difficulty of applying molecular markers, S221 was successively and continuously hybridized with materials such as 09598, Ezhong 5, Yuanfengzhan, Yunxiangruan, etc. Fertile plants were selected from the offspring of the last hybridization. The new variety was cultivated by combining the pedigree method with heat-tolerance analysis, rice quality analysis, and resistance screening. The DNA of 60 selected single plants from the F10 series of lines and 4 parents was extracted. Primers for the target sites were designed. The target DNA fragments were captured by PCR and sequenced. Finally, the genotyping analysis of the target sites was carried out. The SLYm1R high-density rice whole-genome SNP chip was used for the analysis of functional genes. 【Result】Genotype analysis is carried out to analyze the degree of genetic relationship or similarity based on the magnitude of the base substitution rate. The parental materials Ezhong 5 and Yunxiangruan have a relatively distant relationship with other parental materials, while 09598 has a relatively close relationship with Yuanfengzhan. The base substitution rates among the three newly obtained lines are as follows: 0.0099545 (170531-170532), 0.0338213 (170531-170533), and0.0371913 (170532-170533). Within each line, the base substitution rate is 0, indicating that there are differences among the three lines, but there is no genetic difference within each line. Through successive generations and expansion propagation, new germplasms were formed, which were named ZY531, ZY532, and ZY533 respectively. The results of functional gene analysis show that the functional genes of the ZY532 series of germplasms are respectively derived from 4 parents, aggregating excellent genes from multiple parents. For example, the Os-MOT1;1 gene is derived from Yunxiangruan, which can reduce abiotic stresses such as molybdenum accumulation; the Bph3 gene is derived from 09598 and Ezhong 5, which can enhance the resistance to brown planthoppers; the OsGSK2 gene is derived from 09598, Yuanfengzhan, and Yunxiangruan, which can increase the length of the mesocotyl and is suitable for direct seeding; the Badh2 gene is derived from Yunxiangruan, making the rice fragrant; multiple blast resistance genes are derived from different parents and can also be aggregated into the innovative resources, enabling it to obtain good blast resistance. ZY532 has excellent rice quality, good blast resistance, and strong heat resistance. ZY532 also has good heat resistance, and the heat resistance of the hybrid combination prepared reaches level 3. 【Conclusion】When using dominant genic male sterility to cultivate new varieties, due to the complex genetic background, the breeding cycle is often long. Combining high-throughput SNP marker detection can quickly screen out stable lines and more types, which not only broadens the genetic basis but also improves the breeding efficiency. It is an efficient breeding method.
【Objective】Spike-related traits constitute a key factor influencing wheat yield. This study conducted a genome-wide association study (GWAS) on wheat spike-related traits to identify significant loci controlling these traits, thereby providing theoretical references for research on genetic improvement of wheat spike-related traits. 【Method】Using a panel of 261 winter wheat varieties (lines), we measured spike-related phenotypic traits and performed genome-wide association studies (GWAS) with the wheat 90K SNP array, employing the Fixed and Random Model Circulating Probability Unification (Farm CPU) model. Stable and significant loci identified through this analysis were further subjected to haplotype analysis. 【Result】Under three environmental conditions, all 11 panicle-related traits exhibited extensive phenotypic variation, with coefficients of variation (CV) ranging from 3.63 to 64.29. The heritability estimates for these traits varied between 0.42 and 0.84. Highly significant differences (P<0.001) were observed among genotype, environment, and genotype × environment interactions. Genome-wide association study (GWAS) identified 171 loci significantly associated with the 11 traits (P<0.001), including 20 pleiotropic loci detected in two or more environments. These loci were associated with eight panicle traits: panicle length (3 loci), peduncle length (7 loci), sterile spikelet number (1 locus), fertile spikelet number (2 loci), total spikelet number (2 loci), grains per panicle (1 locus), grain weight per panicle (2 loci), and thousand-grain weight (2 loci). The phenotypic contribution rates of these loci ranged from 0.95% to 18.54%. A pleiotropic locus (Ra_c10072_677) significantly associated with both grain weight per panicle and grains per panicle was identified on chromosome 7B, demonstrating phenotypic contribution rates ranging from 2.62% to 6.16%. The marker wsnp_Ex_rep_c69639_68590556, which showed consistent association with peduncle length across two or more environmental conditions (explaining 5.94% of the genetic variation), was selected for haplotype analysis. Three haplotypes (Hap1, Hap2, and Hap3) were characterized, with distribution frequencies of 77.40%, 13.70%, and 8.80%, respectively. Phenotypic analysis revealed that 261 winter wheat cultivars (lines) carrying haplotype Hap3 (30.58 cm) exhibited significantly greater peduncle length (P<0.001) compared to those with Hap1 (28.67 cm) and Hap2 (27.49 cm). The haplotype distribution frequencies showed significant geographic divergence: Hap1 predominated in the Northern Winter Wheat Region, Hap2 was more prevalent in the Huang-Huai Winter Wheat Region, while Hap3 displayed no substantial frequency (>5%) across all winter wheat regions. For stably detected loci across three environments, candidate gene mining identified four genes associated with panicle development. These genes, functionally annotated as encoding MYB transcription factors and F-box domain-containing proteins, represent key candidates influencing panicle architecture. 【Conclusion】The spike traits of wheat exhibited significant variation across different genotypes. A total of twenty stably associated loci were identified across two or more environments. Three distinct haplotypes significantly associated with the peduncle length were detected on chromosome 7B, and four candidate genes potentially related to spike traits were screened out.
【Objective】In precision agriculture, the detection of crop seedlings can be interfered with by factors such as soil weeds, occlusion between seedling leaves, and multi-scale datasets. Based on the object detection algorithm, this paper improved the YOLOv8s algorithm and designed the wheat leaf tip detection model YOLO-Wheat to solve problems, such as leaf occlusion of wheat seedlings in the field, interference from soil weeds, and multi-view data with multiple scales, thereby enhancing the accuracy of wheat seedling leaf detection and providing a theoretical basis for wheat seedling detection at the seedling stage in precision agriculture. 【Method】Close-up and distant images of wheat seedlings were collected respectively through mobile phone cameras and on-board RGB cameras during the emergence period to construct a crop image dataset. In the network model, a pyramid structure of multi-scale feature fusion (high-level screening-feature fusion pyramid, HS-FPN) was adopted. This structure used high-level features as weights, filters low-level feature information through the channel attention module, and combined the screened features with the high-level features. Enhancing the feature expression ability of the model could effectively solve the problem of multi-scale data. Integrate the efficient local attention (ELA) local attention mechanism in the network model was used to enable the model to focus on the leaf tip information of wheat and to suppress the interference of soil background factors of weeds. Meanwhile, the loss function of YOLOv8s (complete IoULoss, CIoULoss) was optimized, and the inner-Iou Loss auxiliary bounding box loss function was introduced to enhance the network's attention to small targets and to improve the positioning accuracy of wheat leaf tips. In terms of training strategies, transfer learning was employed. The model was pre-trained using close-up images of wheat leaf tips, and then the parameters of the model were updated and optimized using distant images. 【Result】The YOLO-Wheat model was compared with five object detection models, namely Faster-RCNN, YOLOv5s, YOLOv7, YOLOv8s, and YOLOv9s. The YOLO-Wheat model was the best in wheat leaf tip detection, with a recognition accuracy rate of 92.7% and a recall rate of 85.1%, respectively. The mean Average Precision (mAP) values were 82.9%. Compared with the Faster-RCNN, YOLOv5s, YOLOv7, YOLOv8s and YOLOv9s models, the recognition accuracy mAP values of YOLO-Wheat have increased by 17.1%, 13.6%, 11.0%, 8.7% and 3.8% respectively; the recall rates increased by 13.1%, 6.7%, 4.5%, 1.8% and 1.3%, respectively. Compared with the Faw-RCNN, YOLOv5s, YOLOv7, YOLOv8s and YOLOv9s models, the mAP values of YOLO-Wheat have increased by 16.2%, 9.8%, 5.0%, 5.9% and 0.7%, respectively. 【Conclusion】This method could effectively solve the problem of multi-scale data, achieve precise detection of small targets at the leaf tips of wheat seedlings in complex field environments using unmanned aerial vehicle (UAV) images, and provide technical support and theoretical reference for intelligent leaf counting of wheat seedlings in complex fields.
