The leaf area index (LAI) is a critical biophysical parameter that reflects crop growth conditions and the canopy photosynthetic potential, serving as a cornerstone in precision agriculture and dynamic crop monitoring. However, traditional LAI estimation methods relying on single-source remote sensing data and often suffer from insufficient accuracy in high-density vegetation scenarios, limiting their capacity to reflect crop growth variability comprehensively. To overcome these limitations, this study introduces an innovative multi-source feature fusion framework utilizing unmanned aerial vehicle (UAV) multispectral imagery for precise LAI estimation in winter wheat. RGB and multispectral datasets were collected across seven different growth stages (from regreening to grain filling) in 2024. Through the extraction of color attributes, spatial structural information, and eight representative vegetation indices (VIs), a robust multi-source dataset was developed to integrate diverse data types. A convolutional neural network (CNN)-based feature extraction backbone, paired with a multi-source feature fusion network (MSF-FusionNet), was designed to effectively combine spectral and spatial information from both RGB and multispectral imagery. The experimental results revealed that the proposed method achieved superior estimation performance compared to single-source models, with an R² of 0.8745 and RMSE of 0.5461, improving the R² by 36.67% and 5.54% over the RGB and VI models, respectively. Notably, the fusion method enhanced the accuracy during critical growth phases, such as the regreening and jointing stages. Compared to traditional machine learning techniques, the proposed framework exceeded the performance of the XGBoost model, with the R² rising by 4.51% and the RMSE dropping by 12.24%. Furthermore, our method facilitated the creation of LAI spatial distribution maps across key growth stages, accurately depicting the spatial heterogeneity and temporal dynamics in the field. These results highlight the efficacy and potential of integrating UAV multi-source data fusion with deep learning for precise LAI estimation in winter wheat, offering significant insights for crop growth evaluation and precision agricultural management.

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R²) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

Wheat plays a crucial role in global food security; however, in recent years, Fusarium Head Blight (FHB) has severely impacted both wheat yield and quality. Strong oxidative free radicals, with high oxidation potential and rapid reaction rates, offer an effective approach for pollutant degradation and microbial inactivation. In this study, the control effect of strong oxidizing radicals on FHB was evaluated by comparing the untreated control group (JM23), which was infected with FHB, to the experimental group (FG06), which was treated with strong oxidizing radicals following FHB infection. The results show that FG06 achieved a control effectiveness of 87.87%. The study also assessed grain characteristics and milling quality. Statistical analysis revealed that FG06 had a slightly lower flour extraction rate (71.24%) compared to the control wheat (JM23), but it exhibited competitive flour whiteness (81.30) and a gluten index of 85.50%. The dough stability at 10 min was 27.00 FE, while several gelatinization parameters were significantly lower than JM23. However, FG06 had higher protein content (10.94%), flour protein content (10.70%), ash content (0.58%), wet gluten content (28.70%), dry gluten content (9.40%), and sedimentation value (73.00 mL), all significantly higher than those of JM23. Additionally, FG06 had a gelatinization temperature of 68.61 °C, similar to JM23. Overall, Strong oxidizing radicals as an alternative to conventional pesticides not only effectively controls FHB but also maintains or even enhances wheat milling and processing quality, promoting more sustainable agricultural practices.

Fusarium head blight (FHB), caused by Fusarium graminearum, is a significant fungal disease that adversely affects wheat production and food security. This study systematically evaluated the fungicidal efficacy of strong oxidative radicals (SORs) against F. graminearum and their effects on wheat growth and yield through a combination of in vitro and field experiments. In vitro experiments revealed that solutions containing different concentrations of radicals effectively suppressed the fungus. The results suggested that SOR solutions exhibited potent fungicidal activity against F. graminearum. At a concentration of 4.0 mg/L, the spore mortality rate was 96.8%, and at 5.0 mg/L, the rate reached 99.4%. The optimal concentration for the elimination of F. graminearum spores was determined to be 2.5 × 10⁵ CFU/mL. The optimal treatment duration for SORs was 10 min. Furthermore, field trials investigated the effects of SORs on wheat growth, and agronomic traits were assessed, along with their efficacy in controlling FHB in field trials, both as a standalone treatment and in combination with commercial pesticides. The results indicated that the application of SORs alone achieved an 87.9% control efficacy, demonstrating significant potential for disease control. Furthermore, SORs positively influenced wheat agronomic traits such as plant height, spike length, grain weight per plant, grain number per plant and grain yield, providing a promising new approach for the green control of FHB.

With the development of the agro-processing industry, the efficient cryogenic treatment and resource utilization of porcine bile—a high-value byproduct—has received increasing attention. This study investigates the dynamic behaviour and freezing characteristics of porcine bile droplets upon impact on cold substrates under varying conditions of surface temperature (−10 °C to −20 °C) and impact velocity (0.18–0.59 m/s). The effects of droplet size, dimensionless numbers (Weber, Reynolds, Bond, Ohnesorge, and Prandtl), and thermal gradients were systematically analyzed. A thermoelectric cooling substrate combined with high-speed imaging was used to quantitatively characterize the spreading ratio, retraction ratio, and freezing time of droplets. The results show that the maximum spreading ratio increases with higher impact velocity but decreases with lower substrate temperature. Lower substrate temperatures significantly shorten the freezing time, with a maximum reduction of up to 45%, particularly for smaller droplets. Droplets with high Weber numbers (We > 3) form flattened ice layers with preserved retraction patterns, while those with low Weber numbers (We < 1) generate smooth, hemispherical ice caps. For the first time, the thermophysical properties of porcine bile were incorporated into the framework of droplet impact dynamics on cryogenic surfaces. The findings reveal multiscale freezing mechanisms of biological fluids at low temperatures and provide a theoretical basis for optimizing processes such as freeze-drying and cryogenic sterilization in agro-product processing.

Wheat powdery mildew is a major fungal disease threatening global wheat production. To develop an effective and environmentally friendly control strategy, this study systematically evaluated the disease-suppressive efficacy of strong oxidative free radicals across a series of treatment parameters, including radical concentrations (3.0–8.0 mg/L), spraying durations (20–60 s), solution pH levels (5–8), spraying heights (0–20 cm), and treatment timings corresponding to different infection stages (0–120 h post-inoculation). Response surface methodology (RSM) was used to optimize these variables with the objective of maximizing disease control efficacy. The results showed that control efficacy increased with radical concentration up to 5.0 mg/L, beyond which a saturation effect was observed. The most effective conditions included a spraying duration of 50 s and a height of 6.5 cm. Maximum suppression was achieved when the treatment was applied within 0–12 h post-infection. Moreover, adjusting the solution pH to a range of 5–7 further enhanced the efficacy. The RSM-based predictive model demonstrated high accuracy (R² = 0.9942), and the optimized parameters—6.65 mg/L radical concentration, 50.84 s spraying duration, and treatment at 15.67 h post-infection—yielded a predicted control efficacy of 97.64%, with a validation error below 0.5%. This study provides a quantitative basis for the precise and sustainable deployment of free radical-based treatments in wheat disease management.