Field operations in rice cultivation, such as dry direct seeding and transplanting, require higher path tracking accuracy of agricultural machinery. To meet the requirements for operational consistency and uniform crop distribution in rice production, we designed an assisted steering control system for wheeled tractors, aiming to assist human operators in achieving precise path tracking during rice field operations.

First, we constructed a driver steering model using data from driving simulator tests, and combined it with the kinematic model of wheeled tractors to develop an integrated vehicle control model that incorporates the driver-in-the-loop. Then, we used a polytopic linear parameter varying system to describe the uncertainties in the model, including time-varying driver model parameters and vehicle speed. Based on this model, an output feedback robust controller was designed to ensure robust stability of the system under uncertainties described by polyhedral sets. Additionally, a region pole placement method was employed to improve the transient response performance of the system. Finally, driver-in-the-loop tests were conducted to evaluate the performance of the designed controller.

The results show that this method effectively enhances the path tracking performance of wheeled tractors while alleviating the physical and mental burdens on the operator. In single lane-change maneuvers and complex serpentine driving conditions, the maximum lateral tracking error is reduced by 66.7% and 55.6% compared with that of the PID control algorithm, respectively, while the physical workload of the driver was reduced by 26.86% and 37.6%, and the mental workload was reduced by 4.2% and 31.3%, respectively.

The proposed control strategy offers a technical foundation for enhancing the operational quality of rice and other crop fieldwork.

To address the problems of high labor intensity and low efficiency in manual measurements of heading date in rice breeding, this study proposes an intelligent prediction method that integrates UAV imagery, the YOLOv10b object detection algorithm, and a random forest regression model to achieve automated and efficient prediction of heading date in rice breeding plots.

Using rice breeding plots as the research subject, UAV imagery was acquired at 15 observation time points from booting to maturity. Based on the YOLOv10b algorithm, a rice panicle detection model was trained to recognize and count panicles in plot images. Subsequently, the numbers of panicles at each observation time point were used as input variables, and the number of days from transplanting to heading was used as the prediction variable to construct and compare multiple heading date prediction models. Based on this, feature importance analysis and dimensionality reduction were performed to develop a simplified model.

Experimental results showed that the YOLOv10b model achieved a precision of 91.7%, a recall of 92.1%, and an F₁ score of 0.919 in the panicle detection task. In the heading date prediction task, the random forest model constructed using the sequential features of panicle counts from all 15 observation time points achieved an R² of 0.84, an RMSE of 1.57 days, and an MAE of 1.12 days. Furthermore, after feature dimensionality reduction based on feature importance analysis, a simplified model constructed using 9 observation time points achieved an R² of 0.83, an RMSE of 1.65 days, and an MAE of 1.16 days. This approach effectively reduced feature dimensionality while maintaining prediction accuracy, thereby improving the model's efficiency and practicality.

The proposed method can effectively reduce the workload of manual measurements, improve the efficiency of heading date monitoring, and realize automated prediction of heading date in breeding plots. It provides a feasible data foundation and technical pathway for intelligent breeding and precision agricultural management.

This study aims to investigate the function of the OsLRP1 gene (Os01g72490) in rice lateral root development by phenotypic characterization and expression analysis of the lrp1 mutant.

The lrp1 mutant was first identified through PCR amplification and sequencing. Bioinformatics methods were then employed to analyze the protein structure, conserved domains, transmembrane domains, and signal peptides of OsLRP1. Multiple sequence alignment and phylogenetic tree construction were conducted to explore the evolutionary relationship between OsLRP1 and LRP1 proteins from other species. Quantitative real-time PCR (qRT-PCR) was used to detect the expression levels of OsLRP1 in different rice tissues. Confocal laser scanning microscopy was utilized to observe the subcellular localization of the OsLRP1 protein. Finally, an auxin treatment experiment was performed to determine whether the regulation of lateral root development by OsLRP1 is mediated by auxin.

Phenotypic analysis revealed that compared with the wild-type Zhonghua 11, the lrp1 mutant exhibited a 32% reduction in lateral root primordia number and a 21% decrease in lateral root number. At the maturity stage, the panicle length and 100-grain weight of lrp1 were reduced by 12.5% and 7.4%, respectively, compared with the wild-type. Bioinformatics analysis showed that the OsLRP1 gene belongs to the SHI transcription factor family, encoding a protein of 340 amino acids. Conserved domain analysis indicated that it is a member of the DUF (Domain of Unknown Function) family. No signal peptide or transmembrane domain was detected in the OsLRP1 protein. Subcellular localization in tobacco demonstrated that the OsLRP1 protein is localized in the nucleus. qRT-PCR results indicated that OsLRP1 is expressed in roots, stems, and leaves, with the highest expression level in leaves. Two auxin response elements (AuxREs) were identified within the 1-kb promoter region upstream of OsLRP1. The auxin treatment experiment confirmed that lateral root development in the lrp1 mutant is affected by auxin.

In summary, OsLRP1 regulates lateral root development in rice in an auxin signaling dependent manner and is involved in rice growth, development, and stress responses, further elucidating its crucial role in rice lateral root development.

Low-temperature stress poses a significant threat to rice production, and the development of cold-tolerant varieties through the identification of cold resistance-related genes represents a critical strategy to address this challenge. Meta-QTL analysis offers a powerful approach to integrate quantitative trait loci governing complex agronomic traits, such as cold tolerance, across diverse genetic backgrounds. By improving the precision and reliability of candidate gene localization, this method provides a robust foundation for the cloning and breeding application of cold tolerance genes in rice.

We conducted a comprehensive meta-analysis of 353 quantitative trait loci (QTLs) associated with cold tolerance at the seedling stage, derived from 38 independent studies. These QTLs were integrated using BioMercator 4.2, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to identify candidate genes within the Meta-QTL regions.

A total of 82 Meta-QTLs were identified, of which 67 (81.71%) had confidence intervals of less than 1 Mb. These regions encompassed 9282 annotated genes, including 25 previously reported genes associated with cold tolerance at the seedling stage. GO analysis revealed 99, 230, and 119 genes associated with abiotic stress response, environmental stress response, and transcription factors, respectively. KEGG enrichment analysis further identified 27, 69, and 9 significantly enriched genes related to abiotic stress response, stress response, and transcription factors, respectively.

Through the integration and analysis of 353 QTLs related to cold tolerance at the seedling stage, 82 Meta-QTLs were identified. GO analysis pinpointed 448 candidate genes, while KEGG analysis further refined this list to 105 differentially enriched candidate genes. These findings provide valuable insights for molecular marker-assisted breeding and gene cloning for cold tolerance at the rice seedling stage.

Leaf is the most important photosynthetic organ in rice. Screening and identification of rice leaf color mutants are beneficial for exploring the molecular regulatory mechanisms of chloroplast development and photosynthesis.

A green-revertible yellow leaf mutant 818-6-8 was obtained from the gene-editing lines of Jingeng 818. Phenotypic identification, genetic analysis, gene mapping, and transcriptome analysis for 818-6-8 were performed.

Compared with the wild type, the mutant showed an obvious yellow-leaf phenotype until the three-leaf stage, accompanied by a significant decrease in chlorophyll content. As it grew, the mutant turned green and gradually became indistinguishable from the wild type at the four-leaf stage. Fewer chloroplasts, abnormal thylakoid morphology and stromal lamellas structure were observed in the mutant. The yellow-leaf trait of 818-6-8 was controlled by a pair of recessive nuclear alleles. The target mutation site was located within a 2.29 Mb interval on chromosome 5, and 20 annotated candidate genes were listed. The expression levels of 2090 genes in the mutant were significantly changed compared to the wild type, of which 1343 genes were up-regulated and 747 genes were down-regulated. Further, the differentially expressed genes were enriched in biological processes such as chloroplast organization and cellular components such as chloroplasts. The expression of genes associated with chlorophyll biosynthesis and chloroplast development of 818-6-8 changed significantly.

These results support the next studies for cloning target genes and analyzing chloroplast-development molecular mechanisms, and provide a germplasm resource for ornamental rice breeding.

Tiller angle is an important component of rice plant type, which directly affects rice planting density and ultimately influences rice yield. Exploring and studying new QTLs/genes that regulate rice tiller angle and applying them to plant type improvement is one of the effective ways to improve rice yield.

The recombinant inbred lines (RILs) derived from the crossing and selfing of the Indian japonica rice variety M494 and indica rice variety Zhong 9B were used as experimental materials. They were planted at Fuyang and Lingshui experimental bases of China National Rice Research Institute in Zhejiang and Hainan Province from 2021 to 2023. QTL mapping and analysis of rice tiller angle were conducted based on the high-density Bin map constructed by resequencing method.

A total of nine QTLs were detected in three years, distributed on chromosomes 1, 3, 9, 11 and 12. The phenotypic variation explained by each QTL ranged from 2.54% to 57.00%, and the additive effects were all derived from the large tiller angle parent M494. Among them, the major QTL qTA9-1 for tiller angle was located in the range of 20.71-20.80 Mb on chromosome 9, with a physical map interval of 86.36 kb region. Moreover, it was repeatedly detected in 2021 and 2022, and the highest contribution rate could reach 57.00%. This region overlapped with the cloned tiller angle gene TAC1. The RT-PCR results showed that the expression level of TAC1 in the large tiller angle genotype line M109 was significantly higher than that in the small tiller angle genotype line M67. Further sequencing analysis revealed that compared with the qta9-1 genotype line M67, the qTA9-1 genotype line M109 had a C to T mutation at the 3' splice site of intron 4 of the reported TAC1, which is the TAC1 functional SNP, and we speculate that qTA9-1 is TAC1. In 2021 and 2022, a novel QTL controlling tiller angle was repeatedly detected in the 15.20-16.00 Mb interval of chromosome 3, which exhibited epistatic interaction with the major tiller angle QTL qTA9-1. A minor QTL locus regulating tillering angle was repeatedly detected in the 21.10-22.02 Mb interval of chromosome 12.

Nine QTLs for tiller angle were detected using the RIL population constructed by crossing M494 with Zhong 9B combining with the high-density genetic map, including the cloned gene TAC1, and a novel QTL for tiller angle was detected on chromosome 3. The above results provide a theoretical basis and genetic resources for further fine mapping of rice tiller angle genes and genetic improvement of plant type of rice varieties.

Soil salinization is an important abiotic factor affecting agricultural production in China, and the identification of salt tolerance-related genes/QTLs in rice is crucial for ensuring stable rice production in saline-alkali soils.

Aigela and Xudao 10, which exhibit significant differences in salt tolerance related traits, were used as parent plants to create a doubled haploid (DH) population. Salt tolerance was identified under field and hydroponic conditions. Genotyping of the targeted population was performed using the rice 1 K mGPS SNP chip. Phenotypic data combined with genotype data of DH population were used for QTL mapping with the QTL IciMapping 4.2 software.

Through QTL mapping analysis, a total of 22 salt tolerance related QTLs were detected, including 10 QTLs for grain yield per plant, 4 for grains per panicle, 2 for root length, and 6 for root dry weight. These QTLs are located on chromosomes 1, 6, 8, 10, 11, and 12, explaining phenotypic variation of 3.37%-15.52% with LOD values ranging from 4.04 to 9.65. Based on the gene annotation, Arabidopsis homologous gene comparison, spatiotemporal expression analysis and sequence comparison within the mapping interval of qST11, a candidate gene (LOC_Os11g30560) encoding a hydroxysteroid dehydrogenase (OsHSD1) was identified.

Among the 22 QTLs detected in this study, 11 were known genes or loci related to salt tolerance, and the remaining 11 were newly identified. Meanwhile, the candidate gene analysis was conducted on the newly identified salt tolerance QTL (qST11), laying a foundation for cloning of salt-tolerance related genes and deciphering their genetic regulatory networks.

Given the challenges of water scarcity and abundant light resources in the arid northwest region, this study aims to investigate the relationship between farmland soil moisture and crop photosynthesis to improve comprehensive resource utilization. The goal is to establish a complementary soil moisture-light mechanism, thereby promoting sustainable resource utilization.

From 2019 to 2021, field experiments were conducted at three typical experimental sites in the Yellow River irrigation area of Ningxia Hui Autonomous Region. Using traditional full irrigation as the control, different drip irrigation treatments with varying water levels were established to analyze the effects of different degrees of water stress on photosynthesis, key growth indicators, and yield components during critical growth stages of rice.

Water stress reduced the photosynthetic rate and the transpiration rate of rice and negatively affected yield components, but improved water use efficiency. The maximum net photosynthetic rate occurred at the booting stage, during which that under water stress treatments decreased significantly by 4.5%-26.6% compared to CK. The minimum value appeared at the milk-ripe stage, with a significant decrease of 4.2%-46.9% under water stress compared to CK. The maximum transpiration rate was observed at the booting stage, decreasing by 2.2%-16.8% under water stress relative to CK. The minimum transpiration rate occurred at the seedling stage, decreasing by 7.5%-40.7% under water stress compared to CK. Under water stress, rice plant height, straw height, straw dry weight, filled grain rate, 1000-grain weight, and yield decreased by 2.2%-24.3%, 0.3%-12.6%, 0.8%-11.6%, 0.5%-7.9%, 0.3%-9.2%, and 2.3%-71.5%, respectively, compared to CK. However, water consumption decreased by 33%-46%. Under mild stress, water use efficiency at the yield level increased by 1.9%-21.5% compared to CK, while leaf-level water use efficiency decreased by 2.1%-6.8%. These results indicate that mild water stress can reduce water consumption while maintaining relatively high yield, thereby improving water resource utilization efficiency. Using the TOPSIS model combined with the information weight method and entropy weight method to evaluate different water stress treatments, it was found that maintaining the soil moisture lower limit at 90%-100% of field capacity can optimize yield, quality, and water use efficiency.

In arid and semi-arid regions, precisely regulated deficit irrigation strategies should be further promoted to maximize the drought resistance potential of rice. As an effective approach for water-saving agriculture, this can reduce water consumption while maintaining stable grain production.