The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson’s correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R²: 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R² increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management.

This article addresses the challenges of water scarcity and climate change faced by cotton cultivation in the Xinjiang region of China. In response, a precise irrigation model based on reinforcement learning and the crop model DSSAT is proposed. The experimental site chosen for this study is Changji City in northwest China’s Xinjiang province. Integrating the cotton model, CSM-CROPGRO, from the DSSAT model with reinforcement learning algorithms, a decision system was developed to provide accurate irrigation strategies that maximize cotton yield. The experimental results demonstrated that our approach significantly improved cotton yield and, compared to genetic algorithms, reduced water consumption while increasing production. This provides a better solution for developing cotton cultivation in the Xinjiang region. Additionally, we analyzed the differences in irrigation strategies among different decision scenarios, and the results showed that the reinforcement learning method achieved higher yields in water application trends during different periods. This research offers new ideas and methods for improving cotton-crop-management decisions. The study’s focus on maximizing cotton yield while reducing water usage aligns with the sustainable management of water resources and the need for agricultural adaptation to changing climate conditions. It highlights the potential of reinforcement learning methods in improving irrigation decision-making and their applicability in addressing water scarcity challenges. This research contributes to the advancement of cotton crop management and provides valuable insights for agricultural decision-makers in the Xinjiang region and beyond.

The aim of this study was to investigate the effect of two-stage variable temperature drying (VTD) on the quality and drying efficiency of paddy rice in the hot air-drying process. A constant temperature of 50 °C (CTD) was used as a control group. VTD and CTD methods were applied in a 15 ton batch type recirculating grain dryer. Three aspects (appearance quality, physical and chemical properties, taste quality) of the paddy rice samples from the dryer were measured and compared. It was observed that paddy rice with an initial moisture content of 25.3% (wet basis) was dried to 14% (wet basis). Compared to CTD, the VTD method could reduce the drying time and fissuring rate by 0.7 h and 42%, respectively. It had a head rice yield (HRY) of 78.45%, compared to 76.45% by CTD. The fatty acid content of the VTD samples was 2.28% lower than those of CTD, and it exhibited a 34% decrease in amylose content. These results show that two-stage VTD is an advanced hot air-drying method that can be used to improve the quality of dried paddy rice, maintain efficiency, and reduce the cost of the drying process by minimizing the rate of energy consumption.

Aspergilus flavus is the main pathogenic fungus that causes food mold. Effective control of A. flavus contamination is essential to ensure food safety. The lipopeptides (LPs) produced by Bacillus strains have been shown to have an obvious antifungal effect on molds. In this study, an antagonist strain of Bacillus velezensis with obvious antifungal activity against A. flavus was isolated from the surface of healthy rice. Using HPLC-MS analysis, the main components of LPs produced by strain E2 were identified as fengycin and iturins. Further investigations showed that LPs could inhibit the spore germination, and even cause abnormal expansion of hyphae and cell rupture. Transcriptomic analyses showed that some genes, involved in ribosome biogenesis in eukaryotes (NOG1, KRE33) and aflatoxin biosynthesis (aflK, aflR, veA, omtA) pathways in A. flavus were significantly down-regulated by LPs. In conclusion, this study provides novel insights into the cellular and molecular antifungal mechanisms of LPs against grain A. flavus contamination.

Drought stress disrupts the molecular-level water balance in plants, and severe water deficiency can be fatal for cotton plants. However, mild water deficits or short-term drought stress may enhance crop resilience, increasing yields. The present study aims to determine the optimal watering time and irrigation amount to induce drought tolerance in cotton seedlings during drought training. Specifically, the investigation focuses on identifying the ideal day for watering and the corresponding irrigation volume that effectively triggers the transition of cotton plants into a state of enhanced resistance to drought stress during the seedling stage. In this study, the CSM-CROPGRO-Cotton model was utilized, and our objectives were to (i) evaluate the predictive capability of CSM-CROPGRO-Cotton for yield estimation in field experiments in Xinjiang and (ii) simulate and assess the range of time during the seedling stage when cotton plants can withstand drought stress without reducing yields, identifying irrigation strategies that induce drought training while maintaining yield under mild water deficiency. The model was validated using yield data from field experiments conducted in 2023. The validation criteria included a normalized root mean square error $\left(nRMSE\right)>10\%$ and a coefficient of determination $\left(r^2\right)>85\%$ for yield; for the leaf area index (LAI), the criterion was $\left(r^2\right)>90\%$, with a degree of agreement of $\left(d\right)>75\%$. The results demonstrated the accuracy of the CSM-CROPGRO-Cotton model in predicting cotton yield. Based on the validated CSM-CROPGRO-Cotton model, this study employed the LINUX crop model batch-processing technique to efficiently simulate 357 different irrigation strategies by adjusting the amount of “first irrigation” and timing. The findings revealed that in the irrigation scheme for cotton during the seedling stage, when the amount of first irrigation was in the lower range of 10 mm to 15 mm, the cotton plants underwent drought training during the early growth stage, and their yields did not exhibit drastic fluctuations due to reduced amounts of first irrigation. The suitable period for first irrigation for drought training was from 25 June to 6 July, and the amount of first irrigation could save approximately 57.14% in irrigation water. This implies that subjecting cotton plants to a certain level of drought training can enhance their stress tolerance and increase yields. This finding holds great significance for cotton cultivation in drought-prone regions.