In modern plant breeding, anther culture is an important biotechnological tool that shortens the breeding cycle and improves efficiency, thereby playing a

In this study, we applied eight different light quality treatments and investigated their effects on the proliferation and physiological characteristics of Aquilaria sinensis group-cultivated seedlings in order to screen the best light quality for optimizing group-cultivation fast multiplication technology. The results showed that the highest multiplication rates were obtained with blue light and red light, which were significantly higher than those of white light. Blue light was the most effective in promoting the synthesis of photosynthetic pigments, while red light and blue violet light were favorable for the accumulation of soluble sugars. Correlation analysis showed that the multiplication rate was significantly and positively correlated with plant height, chlorophyll b, total chlorophyll, and soluble sugar content. The comprehensive evaluation indicated that blue light, blue-violet light, and red light was most suitable for fostering proliferation of, and physiological status improvement in, group-cultivated A. sinensis seedlings, with their superior performance likely attributable to the combined effects of specific spectral properties and appropriate photosynthetic photon flux density (PPFD) levels. The results of this study provide technical support for light environment regulation for the efficient and rapid propagation of group-cultured A. sinensis seedlings.

This paper addresses the challenges in vehicle re-identification, including high intraclass variance, low interclass variance, lack of fine-grained annotation, and misalignment of feature space. Our research is grounded in deep learning and weakly supervised learning theories, examining these challenges from multiple perspectives. A novel vehicle re-identification framework, the Learning Multiple Consistent Embeddings Network (MCEN), is proposed to address the difficulty of accurately locating and extracting key areas of vehicles, which is a result of limited fine-grained annotations. First, a multi-branch, multi-attention network is constructed, and a part diversity regularization term is introduced to encourage different attention branches to concentrate on deep features with distinct semantic meanings. Next, a part consistency regularization term is introduced to align features extracted by each branch within their respective Euclidean spaces, mitigating inter-class inconsistency and ensuring the alignment of local features. The network is trained in an end-to-end manner. The experimental results show that MCEN outperforms existing methods. In the VeRi-776 dataset, MCEN achieves an 8.3% improvement in mAP and a 3.1% increase in Top-1 accuracy over the PRN model. Unlike traditional feature-based methods and local feature detection approaches, MCEN regularization strategies improve robustness against occlusion and variations in camera angles. Furthermore, in the VehicleID dataset, MCEN shows superior performance in all three subsets (small, medium, and large), surpassing the state-of-the-art methods. These results validate the effectiveness of our proposed model in addressing real-world challenges in vehicle re-identification.