Search

IntroductionIndividual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.MethodsIn this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.Results(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

In this study, the differences in physicochemical properties, microbial community structure, and metabolic characteristics between various fortified Muqu and their corresponding high-temperature Daqu (HTD) were investigated using multiphase detection methods. The results demonstrated that the physicochemical properties, community structure, dominant bacterial composition, and metabolic components varied significantly among the different types of fortified HTD. The differences between HTDs became more pronounced when fortified HTD was used as Muqu. Compared to HTD, Muqu exhibited a more complex volatile profile, while HTD contained higher levels of characteristic non-volatile components. The cultivable bacteria count in Muqu was significantly higher than that in HTD, while the cultivable fungi count was slightly lower than that in HTD. The fungal profiles in HTD were primarily associated with starch hydrolysis and ethanol synthesis, while bacterial activity was more prominent in Muqu. Additionally, pyrazine synthesis was mainly attributed to fungi in Muqu and bacteria in HTD. Source Tracker analysis indicated that 8.11% of the bacteria and 26.76% of the fungi originated from Muqu. This study provides a theoretical foundation for the controlled production of HTD, contributing to improvements in its quality and consistency.

The optimization of plant density plays a crucial role in cotton production, and deficit irrigation, as a water-saving measure, has been widely adopted in arid regions. However, regulatory mechanisms governing nitrogen absorption, transportation, and nitrogen use efficiency (NUE) in cotton under deficit irrigation and high plant density remain unclear. To clarify the mechanisms of N uptake and NUE of cotton, the main plots were subjected to three irrigation amounts based on field capacity (Fc): (315 [W1, 0.5 Fc], 405 [W2, 0.75 Fc, farmers’ irrigation practice], and 495 mm [W3, 1.0 Fc]). Subplots were planted and applied at three densities: (13.5 [M1], 18.0 [M2, farmers’ planting practice], and 22.5 [M3] plants m−2). The results revealed that under low-irrigation conditions, the cotton yield was 5.1% lower than that under the farmer’s irrigation practice. In all plant densities and years, the nitrogen uptake of cotton increased significantly with the increase in irrigation. However, excessive irrigation resulted in nitrogen accumulation and migration, mainly concentrated in the vegetative organs of cotton, which reduced the NUE by 9.2% compared with that under farmers’ irrigation practice. Concerning the interaction between irrigation and plant density, under low irrigation, the nitrogen uptake of high-density planting was higher, and the yield of seed cotton was only 2.9% lower than that of the control (the interaction effect of farmers’ irrigation × plant density), but the NUE was increased by 10.9%. Notably, with the increase in irrigation amount, the soil nitrate nitrogen at the 0–40 cm soil layer decreased, and high irrigation amounts would lead to the transfer of soil nitrate nitrogen to deep soil. With the increase in plant density, the rate of nitrogen uptake and the amount of nitrogen uptake increased, which significantly reduced the soil nitrate nitrogen content. In conclusion, deficit irrigation and high plant density can improve cotton yield and NUE. We anticipate that these findings will facilitate optimized agricultural management in areas with limited water.

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson’s correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB.

Elevating the flavor profile of strong flavors Baijiu has always been a focal point in the industry, and pit mud (PM) serves as a crucial flavor contributor in the fermentation process of the fermented grains (FG). This study investigated the influence of wheat flour and bran (MC and FC) as PM culture enrichment media on the microbiota and metabolites of FG, aiming to inform strategies for improving strong-flavor Baijiu flavor. Results showed that adding PM cultures to FG significantly altered its properties: FC enhanced starch degradation to 51.46% and elevated reducing sugar content to 1.60%, while MC increased acidity to 2.11 mmol/10 g. PM cultures also elevated FG’s ester content, with increases of 0.36 times for MC-FG60d and 1.48 times for FC-FG60d compared to controls, and ethyl hexanoate rising by 0.91 times and 1.39 times, respectively. Microbial analysis revealed that Lactobacillus constituted over 95% of the Abundant bacteria community, with Kroppenstedtia or Bacillus being predominant among Rare bacteria. Abundant fungi included Rasamsonia, Pichia, and Thermomyces, while Rare fungi consisted of Rhizopus and Malassezia. Metagenomic analysis revealed bacterial dominance, primarily consisting of Lactobacillus and Acetilactobacillus (98.80–99.40%), with metabolic function predictions highlighting genes related to metabolism, especially in MC-FG60d. Predictions from PICRUSt2 suggested control over starch, cellulose degradation, and the TCA cycle by fungal subgroups, while Abundant fungi and bacteria regulated ethanol and lactic acid production. This study highlights the importance of PM cultures in the fermentation process of FG, which is significant for brewing high-quality, strong-flavor Baijiu.

Key points Degradable mulching film alter the diversity of cotton non-rhizosphere soil fungi. Degradable mulching film alter the soil microbial composition of cotton. Degradable mulching film dose not alter the diversity of cotton rhizosphere fungi. Degradable mulching film dose not alter the diversity of cotton soil bacteria.

High-density planting is an effective technique to optimize yields of mulched cotton. On the other hand, deficit irrigation is an emerging water-saving strategy in cotton cultivation, especially suitable for arid and water-scarce areas. However, the relationships between deficit irrigation, high-density planting, and regulation mechanisms of canopy light radiation and light use efficiency (LUE) in cotton is not yet clear. To clarify the mechanism of light interception (LI) and the LUE of cotton canopies, three irrigation treatments [315 (50% Fc), 405 (75% Fc, farmers’ irrigation practice), and 495 mm (100% Fc), where Fc was the field capacity] with three plant densities [13.5, 18.0 (farmers’ planting practice), and 22.5 plants m2] were applied. The findings of this research revealed that, under deficit irrigation, the above-ground dry matter (ADM) was reduced by 5.05% compared to the farmers’ irrigation practice. Over both years and across all plant densities, LI and LUE under deficit irrigation decreased by 8.36% and 4.79%, respectively, relative to the farmers’ irrigation practices. In contrast, LI and LUE for the highest irrigation level increased by 10.59% and 5.23%, respectively. In the case of the interaction (plant density and irrigation level), the ADM under deficit irrigation and high-density combination increased by 7.69% compared to the control (farmers’ irrigation × sowing practices interaction effects). The LI and LUE also exhibited an increase in 1.63% and 6.34%, respectively. Notably, the LI effect of the middle and upper cotton canopy under film drip irrigation reached 70%. A lower irrigation level resulted in a higher percentage of LI in the lower canopy region. The leaf area index, light interception rate, and extinction coefficient escalated with the increase in plant density. Under deficit irrigation treatment, the LI of the 0–30 cm canopy in high plant density settings increased by 8.6% compared to the control (farmers’ irrigation × sowing practices interaction effects). In conclusion, deficit irrigation and increased plant density improved the interception of LI and LUE of cotton canopy. These findings may help the farmers to optimize their agricultural management strategies in water-deficient areas.

Studying the correlation between microbiome metabolism and flavor of fermented foods has garnered significant attention recently. Understanding the contribution of metabolic regulation and environmental stress to microecosystems is essential for exploring the mechanisms of action of traditional fermented foods. Here, the interaction between microbial communities was investigated using a Xiaoqu fermentation system, processed as “simulative microecosystems,” in which starters were composed of Rhizopus-specific species/strains, Meyerozyma guilliermondii, and Bacillus licheniformis. The differences between community succession and metabolites were also explored. The results indicated that Rhizopus species/strain specificity affected starch hydrolyzation, resulting in a remarkable difference in the type and content of organic acids. This further suggested that the differences in nutrient abundance and organic acids influenced the colonization of microorganisms in the fermentation system, thereby influencing the succession of their communities. The fungi in the community predominantly originated from starters, whereas the bacteria were derived from both the environment and starter. Environmentally colonized microbes were the major contributors to the co-occurrence network and were strongly correlated with network. Regional characteristics of fermented foods were closely related to environmental microbes. These results contribute to the understanding of microbial assembly and flavor metabolism in fermented foods and provide strategies for quality regulation.

Despite the importance of pyrazines in Baijiu flavor, inoculating functional strains to increase the contents of pyrazine in Daqu and how those interact with endogenic communities is not well characterized. The effects of inoculating Bacillus licheniformis with similar metabolic capacity on pyrazine and community structure were assessed in the Daqu complex system and compared with traditional Daqu. The fortification strategy increased the volatile metabolite content of Daqu by 52.40% and the pyrazine content by 655.99%. Meanwhile, results revealed that the pyrazine content in Daqu inoculated isolate J-49 was 2.35–7.41 times higher than isolate J-41. Both isolates have the almost same capability of 2,3-butanediol, a key precursor of pyrazine, in pure cultured systems. Since the membrane fatty acids of isolate J-49 contain unsaturated fatty acids, it enhances the response-ability to withstand complex environmental pressure, resulting in higher pyrazine content. PICRUSt2 suggested that the increase in pyrazine was related to the enzyme expression of nitrogen metabolism significantly increasing, which led to the enrichment of NH4+ and 2,3-butanediol (which increased by 615.89%). These results based on multi-dimensional approaches revealed the effect of functional bacteria enhancement on the attribution of Daqu, laid a methodological foundation regulating the microbial community structure and enhanced the target products by functional strains.

While plastic film mulching and proper high-density planting are important methods that can improve maize yield, years of accumulated residual film have created soil pollution and degraded soil, and thus has impeded sustainable agriculture development. Here, we compared the stalk and root lodging resistances of three maize cultivars grown at two planting densities both with (FM) and without (NM) plastic film mulch. Our aim was to provide a theoretical basis that may help assure a future of successful no-film planting with increased planting density. The results showed that, compared with FM, the average dry weight per unit length and bending strength of basal internode decreased for all cultivars at both planting densities in the NM treatment. At 9.0 × 104 plants ha−1, the stalk breaking force (SFC) of Xinyu77, KWS9384, and KWS2030 in the NM treatment decreased by 4%, 21%, and 22%, respectively. At 12.0 × 104 plants ha−1, SFC of Xinyu77 and KWS2030 increased by 14% and 1%, respectively, while KWS9384 decreased by 10%. Additionally, the root diameter, length, volume, width, depth, and the vertical root-pulling force of maize decreased. Although the lodging resistance of maize grown without film mulch was lower than that of maize grown with it, those adverse effects can be mitigated by selecting suitable cultivars and by using proper high-density planting and appropriate cultivation measures.

Xiaoqu is a fermentation starter used in the production of Xiaoqu jiu, which is also a traditional Chinese liquor. The quality and microbial community characteristics of Xiaoqu is closely related with the yield and flavor feature of fresh Xiaoqu jiu. The present study aims to explore the mystery behind microbial diversity and volatiles of Xiaoqu through polyphasic detection methods such as the Illumina MiSeq platform and the metabolite analyzing method. Results showed that differences in microbial community diversity among samples were significant. The hydrolytic ability was positively correlated with α- and β-diversity of bacteria, but negatively correlated with that of fungi. Staphylococcus and Weissella were the dominant bacteria, while Rhizopus and Candida were the dominant fungi. The abundance of bacteria in sample No3 ranged from 33.66 to 91.53%, while sample No4 the abundance of fungi ranged from 58.51 to 48.72%. The difference of microbial community diversity resulted in a discrepancy of volatile profiles and interaction relationship among the genus. Twenty-four dominant bacteria and seven dominant fungi were correlated with 20 different volatiles. This study provides a scientific perspective of the uniformity and stability of Xiaoqu jiu and might aid in controlling its manufacturing process.