Indica-japonica hybrid rice cultivars show great yield potential but poor eating quality and require more nitrogen (N) input relative to japonica rice. However, the effect of N levels on the eating quality of indica-japonica hybrid rice is little known. A field experiment was carried out to investigate the effects of four N levels on two indica-japonica hybrid rice cultivars (Yongyou12 and Yongyou17) differing in eating quality. The results showed that the contents of amylose chains and water-insoluble storage proteins, especially prolamin, increased largely under a high N level, leading to deterioration of the rice-eating quality, although a low N level (100 N kg/ha) had a less negative effect on the eating quality. Moreover, both of the indica-japonica hybrids had high ratios of inferior grains (IG), and the ratio of IG increased with the N level. Grain weight and the immature ratio of IG were reduced and increased with the N level, respectively, which are also factors for deterioration of the eating quality. The two cultivars differed greatly in the responses of eating quality to the N level, with Yongyou17 being more sensitive than Yongyou12. The current results indicated that a high N level deteriorates the eating quality of indica-japonica hybrid rice mainly due to a large increase of IG.

The brown rice rate (BRR), milled rice rate (MRR), and head rice rate (HRR) are important indicators of rice milling quality. The simultaneous detection of these three metrics holds significant economic value for rice milling quality assessments. In this study, hyperspectral imaging was employed to estimate the rice milling quality attributes of two rice varieties (Xiushui121 and Zhehujing26). Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Networks (CNNs), and Backpropagation Neural Networks (BPNNs) were used to establish both single-task and multi-task models for the prediction of milling quality attributes. Most multi-task models demonstrated a higher prediction accuracy compared with their corresponding single-task models. Among single-task models, BPNNs outperformed the others in predicting BRR and HRR, with correlation coefficients (r) up to 0.9. SVR excelled in forecasting the MRR. In multi-task learning, BPNNs exhibited relatively better performance, with r values exceeding 0.81 for all three indicators. SHapley Additive exPlanations (SHAP) analysis was used to explore the relationship between wavelength and rice milling quality attributes. This study confirmed that this nondestructive detection method for rice milling quality using hyperspectral imaging combined with machine learning and deep learning algorithms could effectively assess rice milling quality, thus contributing to breeding and growth management in the industry.

Aluminum (Al) toxicity inhibits root growth, while nitrogen is an essential nutrient for plant growth and development. To explore the effects of nitrate (N) on Al toxicity and accumulation in root of wheat, two wheat genotypes, Shengxuan 6 hao (SX6, Al-tolerant genotype) and Zhenmai 168 (ZM168, Al-sensitive genotype), were used in a hydroponic experiment with four treatments (control without N or Al, N, Al, and Al+N, respectively). The results showed that N increased the inhibition of root elongation and aluminum accumulation in root. The Al-sensitive genotype suffered more serious Al toxicity than the Al-tolerant genotype. Histochemical observation clearly showed that Al prefers binding on the root apex 7–10 mm zones, and the Al-sensitive genotype accumulated more Al in these zones. Compared with other treatments, the Al+N treatment had significantly higher O₂⁻, superoxides dismutase (SOD), catalase (CAT), peroxidase (POD) activities, H₂O₂, Evans blue uptake, malondialdehyde (MDA), ascorbic acid (AsA), pectin, and hemicellulose 1 (HC1) contents in both genotypes. Under Al+N treatment, O₂⁻ activity, Evans blue uptake, MDA, and HC1 contents of SX6 were significantly lower than those of ZM168, but SOD, CAT, and POD activities and AsA content exhibited an opposite trend. Therefore, aluminum toxicity and accumulation in root of wheat seedlings were aggravated by nitrate.

Prolamin and glutelin are the major constituents of rice protein. The rapid and non-destructive detection of prolamin and glutelin content is conducive to the accurate assessment of rice quality. In this study, hyperspectral imaging combined with regression models and feature wavelength selection was employed to detect the rice prolamin and glutelin content. Feature wavelength selection was achieved using the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and convolutional neural network (CNN)-based Gradient-weighted Class Activation Mapping++ (GradCAM++). Partial least squares regression (PLSR), support vector regression (SVR), back-propagation neural network (BPNN), and CNN models were established using the full spectra and the feature wavelengths. The BPNN models showed the best prediction performance for prolamin and glutelin. The optimal BPNN models achieved a correlation coefficient (r) greater than 0.8 for both proteins. Performance differences were observed between models using feature wavelengths and those using the full spectra. The GradCAM++ method was used to select feature wavelengths with different threshold values, and the performance of different threshold values were compared. The results demonstrated that hyperspectral imaging with multivariate data analysis was feasible for predicting the rice prolamin and glutelin content. This study provided a methodological reference for detecting prolamin and glutelin in rice, as well as the other protein types.

Optimizing nitrogen (N) management is crucial for high-quality rice (Oryza sativa L.) production. However, how N affects grain quality at different positions within a panicle remains unclear. This study evaluated the effects of different N application regimes on the milling, appearance, eating, and nutritional quality of grains at varying panicle positions. We used a japonica cultivar Wuyunjing 31 in a controlled pot experiment with three N treatments: N32:0 (early heavy N), N16:16 (split application with late N topdressing), and N16:0 (low-N control). Results showed that late N topdressing (N16:16) significantly improved head rice yield across all grain positions, which was linked to higher storage protein accumulation (especially glutelin) and larger length-to-width ratio. Conversely, late N application deteriorated appearance quality by increasing the chalky grain rate and chalkiness. This negative effect was most pronounced in superior grains on upper and middle branches. Furthermore, the N16:16 treatment consistently decreased amylose content while increasing albumin, prolamin, and glutelin levels, demonstrating a clear trade-off between carbon (C) and N sinks. We speculated that these intra-panicle differences result from increased competition for carbon resources between starch and protein synthesis pathways. Overall, precision N management should account for spatial differences in grain development to effectively balance rice yield and quality.