Yi Zhang

Sun-cured tobacco is cured by means of natural sunlight, a process that is highly susceptible to fluctuations in external environmental conditions, hindering standardized production and consequently leading to poor quality consistency. Therefore, light is a key environmental factor regulating the curing process of sun-cured tobacco, and as a core component of light, light quality can directly regulate the synthesis and accumulation of metabolites within sun-cured tobacco leaves, thereby governing the formation of their aromatic properties and core quality traits. However, existing studies have rarely explored the mechanisms underlying how light quality regulates aroma formation and core quality in sun-cured tobacco, leaving the intrinsic regulatory mechanisms largely unclear. To address this knowledge gap, the present study investigated how light quality regulates the aroma of sun-cured tobacco. Using the ‘Cun Sanpi’ variety, we applied three light treatments during the curing stage: natural light (NL), NL supplemented with red light (NL+R), and NL supplemented with blue light (NL+B). We analyzed leaf quality and metabolic dynamics through conventional chemical analysis, sensory evaluation, and GC-MS non-targeted metabolomics. The results indicated that light quality significantly altered tobacco quality. The NL+R treatment led to increased sugars, decreased nicotine, low mucosa irritation, and a pleasant aftertaste. In contrast, the NL+B treatment resulted in higher nitrogen and nicotine levels, which corresponded with poorer sensory quality. The NL treatment yielded leaves of intermediate quality. Metabolomic analysis identified 40 differential aroma-related metabolites, primarily carbohydrates and organic acids. These were significantly enriched in 23 metabolic pathways, with core pathways involving carbohydrate metabolism, amino acid metabolism, and secondary metabolite biosynthesis. We found that light quality drives differential metabolite accumulation by modulating metabolic rhythms and specific pathways: NL+R activated ubiquinone and terpenoid-quinone biosynthesis; NL+B regulated glycolysis/gluconeogenesis and propanoate metabolism; and NL influenced photosynthesis. This study identifies key metabolites, specific pathways, and their correlation with sensory traits in light-quality-mediated regulation of sun-cured tobacco. Our findings provide a theoretical foundation for optimizing light conditions during curing to improve tobacco quality in a targeted manner.

Accurate plant counting is essential for tobacco yield estimation and planting density regulation. However, manual quadrat surveys are inefficient and error-prone, and conventional detectors often suffer from feature loss and background confusion under complex field conditions. This study proposes an improved YOLOv8n framework, YOLOv8-AKConv-MLCA, tailored for UAV imagery of plain-field and mountain-field tobacco. First, an Alterable Kernel Convolution (AKConv) module is embedded inside the original C2f blocks to replace all conventional convolutions, enabling adaptive sampling and richer multi-scale representation of small and densely distributed targets. Second, a mixed local channel attention (MLCA) module is inserted between the last C2f-AKConv block and SPPF to fuse local spatial cues with global channel dependencies, suppressing clutter and occlusion effects. Extensive experiments on UAV datasets show that the proposed model achieves counting accuracies of 97.20% (plain-field) and 96.13% (mountain-field), improving over baseline YOLOv8 by 3.98% and 3.25%, respectively. Detection metrics likewise improve: mean average precision (mAP) reaches 0.936 and 0.914 in the two scenarios, surpassing SSD (0.844, 0.827), Faster R-convolutional neural network (0.865, 0.842), and a Transformer-based variant (YOLOv8-Trans, 0.923, 0.907). Relative to YOLOv8, maximum gains of 12.1% in precision, 1.9% in recall, and 7.3% in mAP are observed. Crucially, real-time throughput is preserved, with inference speeds of 219–227 frames per second across datasets. Grad-CAM visualizations further confirm that YOLOv8-AKConv-MLCA concentrates attention on canopy regions and suppresses background interference, offering intuitive evidence of enhanced feature learning. Overall, the proposed framework delivers a strong accuracy–efficiency trade-off and robust generalization under complex terrain, providing an effective solution for automated tobacco plant counting and supporting precision cultivation and smart agricultural management. Code and trained weights are available upon reasonable request for replication and evaluation.

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model’s robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves.