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Abstract Flue-curing and redrying are important processing stages before tobacco fermentation, closely linked to microbial actions that influence the fermentation process. It is necessary to investigate the effects of flue-curing and redrying on diversity and succession of tobacco fungal communities. It was shown that a total of 9 phyla, 33 classes, 94 orders, 266 families, 646 genera, and 6,396 amplicon sequence variants (ASVs) were identified in the fungi communities of 36 samples from different processing stages (before flue-curing, after flue-curing, before redrying and after redrying) based on high-throughput sequencing technology. Dominant genera shared by tobacco leaves at different stages were Alternaria and Sampaiozyma. About 80% of fungi in stored tobacco leaves after redrying originated from fresh tobacco leaves before flue-curing, while the rest were primarily enriched in the post-harvest processing environment. After flue-curing, major molds like Aspergillus and Penicillium were notably enriched. The distribution of fungal communities suggested that the flue-curing and redrying had a significant impact on fungal composition. Functional annotation of fungal communities at the guild level exhibited differences during processing stages. Main fungal functional groups were identified. In summary, our study elucidated dynamic changes in the composition of fungal communities and highlighted key stages in mold enrichment during tobacco leaf processing, laying groundwork for mildew prevention and control during tobacco leaf fermentation.

Abstract Enterobacter ludwigii has been proven by numerous studies to be an effective plant growth promoter. Enterobacter ludwigii T977 was isolated from leaves of Nicotiana tabacum L. Yunyan 97 which showing high starch degrading ability. The optimal fermentation carbon source of strain T977 was starch, with optimal starch concentration as 2.5 g/L, and the most suitable fermentation nitrogen source for the strain T977 was ammonium acetate, with optimal concentration as 0.25 g/L. The spaying treatment of strain T977 could reduce the starch content of upper leaves from 3.77% to 1.43%, the total sugar and reducing sugar decreased slightly, the starch content of middle leaves decreased from 5.63% to 3.18%, the content of total sugar and reducing sugar increased in middle leaves, and the other chemical components were in the appropriate range. Here, we reported 4.77 MB whole genome of a starch-degrading E. ludwigii T977 that encodes 4501 proteins, 11 α-amylases in GH13 family were identified, and the amylase (GM000159) with signal peptide may play important role in degradation of starch in tobacco leaves. Our study may provide an effective microbiological mean for reducing starch content in tobacco leaves.

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii.