Carotenoids are critical aroma precursors in tobacco, and their targeted degradation offers a promising strategy to improve tobacco flavor and quality. However, efficient microbial strains for this purpose remain underexplored. In this study, a novel carotenoid-degrading bacterium, Paenibacillus oceanisediminis CJ-2, was isolated from aged tobacco leaves. CJ-2 exhibited superior carotenoid degradation ability. Whole-genome sequencing revealed key genes involved in carotenoid catabolism, including CRTZ (R-carotene 3-hydroxylase), CRTX (zeaxanthin glucosyltransferase), and CCD7 (9-cis-epoxycarotenoid dioxygenase). Importantly, inoculation of flue-cured tobacco with CJ-2 increased carotenoid-derived volatiles by 60% and total flavor compounds by 2.67-fold compared to untreated controls, with 17 key differential aroma compounds identified. Sensory evaluation demonstrated that CJ-2 fermentation significantly improved tobacco quality, particularly in aroma quantity, smoothness, and aftertaste. These findings establish P. oceanisediminis CJ-2 as an efficient microbial biocatalyst for accelerating tobacco aging and enhancing aroma quality. This study provides a practical bioprocess for tobacco fermentation with broader implications for aroma enhancement in fermented agricultural products.

Early aging is critical for colour formation in tobacco leaves, yet the underlying metabolic mechanisms remain elusive. This study integrated image processing with untargeted and targeted LC-MS to construct a metabolic network characterising colour evolution in flue-cured tobacco (cv. Cuibi-1, CB-1) during early aging (0-120 days). Results indicated that leaf colour significantly brightened during the first 30 days, followed by deepening and reddening. Specifically, L* and b* values showed an initial increase followed by a decrease, whereas the a* value exhibited a sustained upward trend. The constructed metabolic network was anchored in saccharide metabolism, connecting downstream phenolic, flavonoid, sphingolipid, and terpenoid pathways. Sixteen differentially accumulated metabolites (DAMs) were identified across categories including alcohols, flavonoids, lignans, nucleotides, phenolic acids, saccharides, and xanthophylls. These DAMs exhibited distinct change patterns and significant correlations with colour parameters (L* and a*, r ranging from -0.88 to -0.58 and 0.56-0.74, respectively; P < 0.05). For the first time, this study elucidated the metabolic mechanisms governing colour changes in CB-1 tobacco leaves during early aging for the first time, providing novel insights for assessing aging progression and maintaining high-value raw material quality.

Starch content plays a critical role in the quality of tobacco, influencing key attributes such as aroma and sensory quality. Traditional methods for assessing starch content, such as colorimetry and high-performance liquid chromatography (HPLC), have the disadvantages of wasting time and costing expensive. This limits the applicability of measuring starch content in large-scale production. In addition, most existing prediction tasks rely on a single machine learning or deep learning approach, indicating the potential for further enhancement in accuracy. To address this issue, this study developed an ensemble learning model based on the stacking method to improve the prediction accuracy of starch content, using hyperspectral and image data collected from unmanned aerial vehicles (UAVs). Feature selection was performed using synergistic interval partial least squares (SiPLS) on the combined spectral result. A stacking model was developed by integrating support vector regression (SVR) and gated recurrent units (GRU) with multi-layer perceptron (MLP) as the meta-learner. The results indicated that the stacking model achieved high accuacry with a coefficient of determination (R2) of 0.97 and reduced the root mean square error (RMSE) to 1.50 %. Compared to SVR and GRU, the R2 values improved by 0.09 and 0.03, respectively, while the RMSE decreased by 2.71 % and 0.53 %, respectively. This methodology significantly enhances prediction accuracy and facilitates precise control over tobacco quality.

Accurate acquisition of tobacco disease information is crucial for timely intervention and loss reduction. However, most existing tobacco disease recognition models are developed based on small-scale scenarios and struggle to overcome problems in UAV remote sensing images such as complex backgrounds, low resolution, and large scale variations of diseased plants. Furthermore, their large model sizes hinder effective deployment on edge devices, constraining practical field-level precision management. To address these challenges, this paper proposes RT-YOLOv10, a lightweight disease detection algorithm based on YOLOv10, specifically designed for UAV imagery. RT-YOLOv10 introduces RFAConv and parameter-free attention SimAM during the downsampling stage to enhance feature extraction accuracy. During the upsampling stage, it employs DySample to more accurately restore color, texture, edge, and other effective information crucial for disease recognition. To strengthen the network's ability to capture and fuse features of multi-scale diseased plants (varying in size and shape), we designed the MS_C2f and MS_CSP modules. Their embedded attention mechanisms effectively focus on key information, highlight the disease subjects, and significantly reduce false positives and missed detections caused by interference from complex backgrounds like weeds and sun glint. Further, through the design of a dual-branch collaborative densely connected neck structure, deep interaction of multi-level semantic information is promoted, enhancing the network's learning and representation capabilities for features of different disease types. Experimental results demonstrate that the standard version, RT-YOLOv10-s, achieves 97.2 % Precision, 93.6 % Recall, 97.5 % mAP50, and 83.9 % mAP50-95 with a low parameter count of 7.4 M and 33.3 GFLOPs, outperforming other models in all metrics. The more lightweight version, RT-YOLOv10-t, maintains high accuracy while reducing parameters to only 2.3 M. Compared to YOLOv5-s, YOLOv6-s, YOLOv7-Tiny, YOLOv8-s, YOLOv9-s, and YOLOv10-s, this represents reductions of 67.1 %, 86.6 %, 61.7 %, 79.3 %, 76.3 %, and 71.6 % in parameters, respectively. RT-YOLOv10 meets the demands of practical applications, enabling deployment on edge devices with extremely limited computational resources. It provides an effective means for large-scale, precise monitoring of tobacco diseases.

Introduction:The state monitoring of tobacco leaves during the curing process is crucial for process control and automation of tobacco agricultural production. While most of the existing research on tobacco leaves state recognition focused on the temporal state of the leaves, the morphological state was often neglected. Moreover, the previous research typically used a limited number of non-industrial images for training, creating a significant disparity with the images encountered in actual applications. Methods:To investigate the potential of deep learning algorithms in identifying the morphological states of tobacco leaves in real industrial scenarios, a comprehensive and large-scale dataset was developed in this study. This dataset focused on the states of tobacco leaves in actual bulk curing barn in multiple production areas in China, specifically recognizing the degrees of yellowing, browning, and drying. Then, an efficient deep learning method was proposed based on this dataset to enhance the predictive performance. Results:The prediction accuracy achieved for the yellowing degree, browning degree, and drying degree were 83.0%, 90.5%, and 75.6% respectively. The overall average accuracy, satisfied the requirements of practical application scenarios with a value of 83%. Discussion:Our proposed framework effectively enables morphological state recognition in industrial curing, supporting parameter optimization and enhanced tobacco quality.

Accurately obtaining the total nitrogen and nicotine content of tobacco plants and their vertical distribution within the canopy is crucial for smart management and quality assessment. However, the complex field environment and uneven vertical distribution pose significant challenges for precise estimation. This study proposed a spectral and texture feature fusion method based on deep learning to improve estimation accuracy, and an improved YOLOv8 model (AO-YOLOv8) was developed for tobacco leaf instance segmentation. After segmentation, the average spectral features from six image channels were extracted, and 474 texture features were obtained using Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), Fourier Transform, Gabor Filter, and Wavelet Transform. Four deep neural networks, including LSTM, RNN, MLP, and FCNN, were then applied to establish estimation models of nitrogen and nicotine content at both the leaf and plant scales. The results showed that AO-YOLOv8 achieved an mAP50 of 87.3 and an mIoU of 83.4 in the leaf instance segmentation task, representing improvements of 6.99% and 8.88% over the original YOLOv8, and effectively detected and separated overlapping leaves under complex conditions. The fusion of spectral and texture features significantly improved prediction accuracy, with the LSTM network achieving the best performance, yielding R2 values of 0.8634 and 0.8735 for nitrogen and nicotine prediction at the leaf scale in laboratory conditions. In the field environment, the LSTM-based models for plant-scale nitrogen and nicotine estimation achieved R2 values of 0.6771 and 0.5735, respectively, which outperformed models using spectral features alone. In conclusion, this study enabled accurate estimation and visualization of the vertical distribution of nitrogen and nicotine content in field-grown tobacco plants, providing an efficient, low-cost, and non-destructive solution for tobacco production and quality control.

Recently published information on spatial variability of soil organic carbon (SOC) in cultivated areas and the relative controlling factors that drive the variations across different ecological regions have rarely been reported in China. To address this gap, 180 soil samples were collected from typical tobacco-growing fields across the Huanghuai, Northeast, and Southwest regions of China to determine the spatial variability of SOC, climate factors, soil carbon fractions, soil texture, soil nutrients, and microbial communities. Results showed that organic SOC and microbial diversity display significant variations across different ecoregions in China. In the HuangHuai and Northeast regions, increased microbial diversity facilitated the accumulation of organic carbon. Conversely, in the Southwest region, variations in precipitation and soil texture serve as critical determinants of microbial community abundance. In the Huang-Huai region, the C/N ratio play a pivotal role in influencing microbial dynamics. Notably, an increase in soil nitrogen significantly enhanced microbial diversity, thereby promoting SOC accumulation in the Northeast region. Collectively, these findings underscore that precipitation, soil texture, total nitrogen, and C/N ratio were key driver of microbial community composition and SOC across diverse ecological regions. These results suggest that recognizing the role of environmental conditions and soil characteristics can improve soil management and enhance carbon sequestration in various ecological contexts.

Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (StWC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the StWC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm3/cm3) indicated a slower decrease compared to healthy tobacco plants (0.021 cm3/cm3). In accordance with this phenomenon, the daily variation of StWC near roots of diseased tobacco plants (0.023 cm3/cm3) was significantly less than that of healthy tobacco plants (0.048 cm3/cm3). Moreover, the abnormal changes of StWC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of StWC was continuously less than 0.037 cm3/cm3. Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection.

Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical components. In order to clarify the relationship between the changes of tobacco color and chemical components, here we established several prediction models of chemical components with the color values of tobacco based on machine learning algorithms. The results of correlation analysis showed that tobacco moisture content was highly significantly correlated with the parameters such as a*, H* and H°, the reducing sugar and total sugar content of tobacco was significantly correlated with the color values, and the starch content was highly significantly correlated with the color values except for b* and C*. The random forest models performed best in predicting tobacco moisture, reducing sugar, total sugar and starch constructed with the R2 of the model validation set was higher than 0.90, and the RPD value was greater than 2.0. The consistent between the predictions and measurements verified the availability and feasibility using color values to predict some chemical components of the tobacco leaves with high accuracy, and which has distinct advantages and potential application to realize the real-time monitoring of some chemical components in the tobacco curing process.

Monitoring the states of tobacco leaves during the flue-curing process is crucial for automating the tobacco industry. While much of the existing research on tobacco leaves state recognition focuses on the temporal state of the leaves, the morphological states are often neglected. Moreover, these studies typically use a limited number of non-industrial images for training, creating a significant disparity with the images encountered in real-world applications. To resolve these issues and investigate the potential of deep learning algorithms in identifying the morphological states of tobacco leaves in real industrial scenarios, this work has first developed a comprehensive, large-scale dataset. This dataset focuses on the states of tobacco leaves in real-world flue-curing houses, specifically recognizing the degrees of yellowing, browning, and drying. Then, a deep learning benchmark for this dataset using multiple deep learning networks is established. Furthermore, an efficient deep learning method is proposed to enhance the predictive performance of the deep backbone network. This is achieved by integrating the spectral characteristics of tobacco leaves images and filtering out color noise. The experimental results demonstrate the effectiveness of our approach in this task. The prediction accuracies achieved for the yellowing degree, browning degree, and drying degree are 83%, 90.5%, and 75.6% respectively. The overall average accuracy is 83%, satisfying the requirements of practical application scenarios.

Color is one of the most important indicators for the flue-cured tobacco quality. The color change of tobacco has a great relationship with the natural pigments in the tobacco. The relationship between color characteristics and the content of natural pigments in tobacco leaves during curing was investigated. The middle part of variety K326 tobacco was taken at each key time point during the curing process to determine the changes of color characteristics, moisture, pigment and polyphenol content. The results showed that moisture content of wet basis of tobacco gradually decreased from 72 to 18% during the curing process, the b* value increased and then decreased, and the a* value increased significantly. The lutein and β-carotene content decreased to 63.83 μg/g and 28.3 μg/g, respectively. The total polyphenols content increased to 50.19 mg/g. Meanwhile, the a* value was significantly and positively correlated with polyphenols content and negatively correlated with pigments content. Cluster analysis showed that the samples were divided into three categories: samples with the curing time of 0 h, 24–72 h, and 84–132 h. These results demonstrated that the color change of tobacco during curing process can be divided into three stages from the perspective of chemical composition, which are strongly related to the degradation of pigments and the transformation of polyphenols.

为研究省域范围内不同气象因子对烤烟化学成分的影响,采用2010-2019年陕西省6个烤烟主产区C3F等级烟叶的化学成分和对应采样点的气象资料,通过回归分析、相关分析、灰色关联等方法探讨主要气象因子对烟叶化学成分的影响.结果表明:各产区的气象条件差异较大,其中生育期平均气温和降水量的表现尤为明显,咸阳产区生育期气温最低(为18.6℃)、安康产区生育期气温最高(为24.4℃),汉中产区生育期降水量最高(为689 mm),咸阳产区生育期降水量最低(为473 mm);安康、汉中、商洛和宝鸡4个烤烟产区的C3F烟叶烟碱含量为2.12％～2.32％,处于适宜水平,咸阳和延安产区烟叶烟碱含量相对较低;各产区烟叶的烟碱和总氮含量与生育期平均气温呈显著或极显著正相关,氮碱比和糖碱比与生育期平均气温及伸根期和旺长期的相对湿度呈显著或极显著负相关,说明烤烟生育期平均气温和相对湿度是影响陕西烤烟产区烟叶品质的主要因素.

Black shank, caused by Phytophthora nicotianae, is a devastating disease of tobacco in the world. Dimethomorph has been widely used to control this disease, but the potential for fungicide resistance is unknown. The baseline sensitivities of 71 P. nicotianae isolates, tested by mycelial growth inhibition, showed a unimodal distribution with a mean EC 50 value of 0.3502 (±0.0332) µg/ml. Eight dimethomorph-resistant mutants were obtained by repeated exposure to dimethomorph in vitro. The mutants with a high resistance level had a resistance factor greater than 220, while the mutants with a low resistance level had a resistance factor of less than five. The resistant mutants had almost equal fitness as their wild-type parents in mycelial growth, temperature sensitivity, and pathogenicity. No cross-resistance between dimethomorph and fluazinam, metalaxyl, or fluopicolide was observed. The results suggest that the risk of P. nicotianae developing resistance to dimethomorph is moderate. A comparison of four full-length cellulose synthase genes between wild-type and dimethomorph-resistant isolates revealed two types of point mutations in the high-resistance mutants. The amino acids changed from glutamine to histidine at position 1077 and from valine to leucine at position 1109, conferring a high resistance to dimethomorph. Based on the point mutations, allele-specific polymerase chain reaction methods were developed to detect dimethomorph resistance in P. nicotianae populations.

为考察气候变化对烤烟生长适宜性的影响,利用烤烟气候适宜性评价、气候趋势分析和气候风险度评价等方法,分析了陕西6个烤烟产区主要气候因素及烤烟气候适宜性指标的年际波动情况,探讨了1980—2015年间各产区烤烟种植主要限制气候因素以及气候因素的倾向率,并对各产区烤烟种植风险度进行了评价.结果显示,陕西各产区烤烟生育期间降雨量年际波动较大,是烤烟气候适宜性的主要限制因素.安康、汉中和商洛等区域烤烟气候综合适宜性较高,年际稳定性较好,适宜发展成为陕西核心烤烟种植区.气候趋势分析表明,陕西多数烤烟产区气温呈显著升高趋势,宝鸡和咸阳产区的烤烟气候适宜性显著提高.

以2018—2019年207份C3F初烤烟叶样品为材料,研究了不同香型区域烟叶水溶性糖组分含量及其与感官品质的关系.结果表明:(1)烤烟果糖、葡萄糖、蔗糖、麦芽糖占总糖的比例均值分别为46.43％、48.15％、2.29％、3.13％.(2)清甜香型、蜜甜香型和醇甜香型区烤烟总糖、还原糖含量极显著高于焦甜焦香型、焦甜醇甜香型和清甜蜜甜香型区;(3)各香型区烤烟水溶性糖组分含量与占比特点为:清甜香型、蜜甜香型和醇甜香型果糖、葡萄糖含量高,麦芽糖含量适中,葡萄糖占比高;焦甜焦香型果糖、葡萄糖含量低,麦芽糖含量高,麦芽糖占比高;焦甜醇甜香型果糖、葡萄糖、麦芽糖含量低;清甜蜜甜香型葡萄糖、蔗糖、麦芽糖含量低,果糖含量高,果糖占比高,蔗糖占比低.(4)典型相关分析结果表明,果糖、葡萄糖、蔗糖和麦芽糖含量与烟气浓度呈负相关;果糖含量与香气质和杂气呈正相关,与劲头呈负相关;葡萄糖、蔗糖和麦芽糖含量与香型风格凸显程度呈正相关.

为评价贵州省主要植烟县(市、区)上部烟叶成熟期的气象条件,利用贵州省63个县(市、区)2011—2017年8—9月气温和降雨量数据,分析了上部烟叶成熟期的气象因素,并筛选出推迟至9月中旬采收上部烟叶时具备适宜气温和降雨量条件的县(市、区).结果表明:除毕节市、贵阳市、六盘水市的大部分植烟县(市、区)外,其他49个县(市、区)9月中旬的平均气温达到或超过20℃;9月中旬平均气温的年度稳定性大体上呈现出从西向东逐渐增强的趋势,除毕节市、六盘水市的主要县(市、区)外,其他区域年度间变异相对较小;贵州省主要植烟县(市、区)9月中旬的降水量呈现较明显的西高东低的特点,8月下旬至9月中旬总降水量除毕节市西部、六盘水市西部、黔西南州西部等县(市、区)外,其他48个县(市、区)低于150 mm;遵义市、黔东南州、铜仁市、黔南州、安顺市大部分植烟县(市、区)以及贵阳市、毕节市的少数植烟县(市、区)具备推迟至9月中旬采收的气温和降雨量条件.

为了满足烟叶选材过程中跨区域匹配烟叶的需求,研发融合了降维和相关性分析方法的烟叶质量数据可视分析方法.通过基于香型区分类的降维算法、对比算法和针对烟叶质量数据的可视化交互方法,为研究人员提供了对烟叶质量数据进行探索式空间划分和相关性分析的可视化分析手段.全国烟叶质量数据分析案例和专家论证,证明该方法可以较好实现烟叶质量数据分析.

为合理优化烟叶烘烤工艺参数,运用物联网技术采集了河南3个产烟区烘烤温湿度数据,分析烘烤过程的历时和温湿度指标,并结合国内外烘烤工艺进行对比分析.结果表明:①中部和上部烟叶烘烤总时长分别集中在6.5～8.0 d和7.0～8.5 d,上部烟叶烘烤过程中湿球温度的波动相对较大.②豫西和豫南烟区变黄期、定色期和干筋期时长占烘烤总时长的比例约为40％、35％和25％,豫中烟区变黄期历时明显较长(占烘烤总时长55％左右).③各烟区变黄期、定色期和干筋期的主要稳温阶段分别在40～42℃、43～48℃和61℃以上,变黄后期至干筋期的湿球温度较三段式工艺低1～2℃.④相关分析结果显示,变黄后期～定色中期湿球温度调控与烘烤进程密切相关.结合国内外烘烤工艺,可适当延长变黄期38℃和定色期54℃的稳温时间,合理调控变黄后期至定色期的湿球温度,避免大幅度波动,以促进烘烤过程中烟叶内在化学成分的合理转化.

为解析碳纳米溶胶促进烟草钾素吸收的生理机制,利用砂培试验和非损伤微测技术(Non-invasive micro-test technology,NMT),研究了碳纳米溶胶对4个烟草品种K+通道基因表达、根系K+流速、植株钾素吸收和积累的影响.结果表明:碳纳米溶胶能促进烟草K+内流通道基因的表达,促进K+内流,抑制K+外流,且不同品种、不同部位的K+通道基因对碳纳米溶胶的响应各不相同.在碳纳米溶胶处理下,RG17、鄂烟1号、云烟87和中烟100的K+外流通道基因NTORK1表达量相比对照分别降低31.1％、59.9％、25.6％和32.0％;K+内流通道基因NKT1在鄂烟1号和云烟87根系中的表达显著增加,增幅分别为210.6％和130.9％.碳纳米溶胶处理增加了各烟草品种根系的K+流速,与对照相比,RG17、鄂烟1号、云烟87和中烟100经碳纳米溶胶处理后K+内流速率分别增加38.17、39.71、13.96、8.61 pmol·cm-2·s-1.此外,经碳纳米溶胶处理后,鄂烟1号、云烟87和中烟100植株钾素积累量较对照分别增加了18.6％、15.6％和46.6％.可见,碳纳米溶胶通过调节根系K+通道基因的表达可增强根系对钾素的吸收并促进烟草体内钾素的积累,但不同烟草品种对碳纳米溶胶的响应存在明显差异.