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.

Rice, as one of the main food crops, provides a vital source of dietary energy for over half the world’s population. The OsFAD3 gene encodes fatty acid desaturase, catalyzing the conversion of linoleic acid (LA) to alpha-linolenic acid (ALA) in rice. However, the genetic characterization of OsFAD3 and its role in the conversion of LA to ALA remains elusive. Here, we validated the effects of two homologous genes, OsFAD3-1 and OsFAD3-2, on the ALA and LA/ALA ratio in rice grains using near-isogenic lines. Two major haplotypes of OsFAD3-1 are identified with different effects on the ALA and LA/ALA ratio in rice germplasm. High expression of OsFAD3-1 is associated with high ALA accumulation and eating quality of rice grains. Overexpression of OsFAD3-1 driven by a seed-specific promoter increases the ALA content up to 16-fold in the endosperm. A diagnostic marker is designed based on an 8-bp insertion/deletion in the OsFAD3-1 promoter, which can recognize OsFAD3-1 alleles in rice. These results indicate that OsFAD3-1 is a useful target gene in marker-assisted breeding programs to improve varieties with high ALA and appropriate LA/ALA ratio in brown rice.

(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.

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 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.

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.