This study investigated the effects of ultrasonic frequency, ultrasonic power, irradiation height and temperature on the drying characteristics, quality and microstructure of wolfberry by ultrasonic-assisted far-infrared drying. By fitting five commonly used thin-layer drying mathematical models, it was found that the coefficient of determination (R²) of the Weibull model was 0.99400–0.99825, the root mean square error (RMSE) was 1.2162 × 10^-4–4.5209 × 10^-4, and the reduced chi-square (χ²) was 0.00207–0.00663, which was the best fit. Under the application of ultrasound, the average drying rate of wolfberry increased. Compared with natural drying, the polysaccharide content increased by 33.2 % at 250 mm irradiation height, and the total phenol content increased by 44.9 % at 40 kHz ultrasonic frequency. The antioxidant activity was the strongest, and the total flavonoids content was the highest (2.594 mg/g) at 24 W ultrasonic power. By comparing the microstructure of wolfberry under different drying methods, such as a fresh sample, natural drying, hot air drying, and ultrasonic-assisted drying, we found that the ultrasonic assistance increased the number of micropores on the surface of wolfberry, reduced the damage to epidermal cells, reduced the mass transfer resistance of the drying process and accelerated the drying process. This study shows that ultrasonic-assisted far-infrared drying technology played a significant role in the heat and mass transfer of wolfberry drying, and had great potential in the commercial processing of wolfberry.

The present study aimed to investigate the effect of rotary microwave vacuum drying (RMVD), radio frequency vacuum drying (RFVD), vacuum far infrared drying (VFID), vacuum drying (VD), hot air drying (HD) and natural drying (ND) on the drying characteristics, active ingredients and microstructure of Codonopsis pilosulae slices. Compared with the fitting results of the four models, the Weibull model is the most suitable drying model for Codonopsis. The RFVD and HD color difference values were smaller compared to ND. The effective moisture diffusivity (Deff) under different drying methods was between 0.06 × 10⁻⁸ m²/s and 3.95 × 10⁻⁸ m²/s. RMVD-dried products had the shortest drying time and retained more active ingredients. The microstructure analysis revealed that the porous structure of RMVD is more favorable for water migration. RMVD is a promising dehydration method for obtaining high-value-added dried Codonopsis products.

To maximally maintain fruits and vegetables quality after harvest, this study used ultrasonic (US) and ultra-high pressure (UHP) techniques as pretreatments for radio frequency vacuum (RFV) drying of peach slices, and investigated the effects of different pretreatments (US, UHP, UHP-US, and US-UHP) on drying characteristics, physicochemical qualities, texture properties, and sensory evaluation of peach slices. Results showed that the drying rate was increased by 15.79 ∼ 54.39 % and the contents of pectin, hemicellulose, total phenolic, total flavonoid, phenolic acids, individual sugar annd antioxidant of the samples were significantly increased after US combined with UHP pretreatment (P < 0.05). US-UHP + RFV dried peach slices obtained brighter color, better texture attributes of hardness, cohesiveness, chewiness, springiness, and resilience. The dehydrated samples pretreated by UHP-US had the best overall acceptance, appearance, and crispness with lower off-odor and sourness compared to the dehydrated peach slices with US and UHP pretreatment. Notably, the highest cellulose and organic acids were found in dehydrated peach slices by control, followed by samples US, and samples with UHP pretreatment. The microstructure showed that the internal organization of peach slices appeared as uniform and regular honeycomb porous structure after US-UHP pretreatment. The findings may provide theoretical reference for the development of energy-efficient and high-quality drying technology for fruits and vegetables.

To overcome the problems of a long conventional drying time, low energy efficiency, and poor product quality, a segmented drying approach was developed for fresh wolfberry (Lycium barbarum) using a radio frequency (RF)-hot air drying process, which was investigated under different parameters of plate spacing (80, 90, 100 mm), vacuum degree (0.015, 0.025, 0.035 Mpa), and hot air temperature (50, 55, 60 °C). Analysis of the wolfberry’s drying characteristics, comprehensive quality, and microstructure indicated that: combined drying was faster and less time-consuming than natural drying or hot air drying, and components such as polysaccharides, ascorbic acid, and betaine in wolfberries were effectively retained. Based on the acceptable drying rate, stable temperature application, and avoidance of arcing effects, the optimal combined segmented drying parameters were determined to be as follows: a plate spacing of 90 mm, vacuum degree of 0.025 MPa, and air temperature of 55 °C. For the dried wolfberries under these conditions, the total drying time was 17 h and the berries had an improved comprehensive quality, the content of total soluble sugars was 0.62 g/g, total phenol was 10.01 mg/g, total flavonoids was 2.60 mg/g, V_C was 3.18 mg/100 g, betaine was 3.48%, oxidation resistance represented by an inhibition rate was 66.14%, color was better, and rehydration rate was 48.56%. The microstructure was more regular because of the special dielectric heating characteristics of RF vacuuming. Despite the differing drying characteristics of individual materials, the overall RF-hot air combined drying process was found to achieve high-quality dehydration of wolfberries.

In this study, the effect of an ultrasound (US) pre-treatment on the process of drying Cistanche slices through far-infrared vacuum drying was investigated with various experimental factors, including the US treatment time (25, 35, 45 min), frequency (20, 40, 60 kHz) and power (150, 180, 210 W). The results showed that compared with the samples without US, the material drying time after the US treatment was reduced by 16–36.8%. The effective moisture diffusion coefficients of Cistanche slices under different US conditions ranged from 1.61122 × 10⁻⁸ to 2.39274 × 10⁻⁸ m²/s, which agreed with food processing ranges. In addition, the phenylethanoid glycoside, iridoid, polysaccharide, total phenol and total flavonoid contents in Cistanche were significantly increased after US pre-treatment. However, the dried products obtained with the 45 min US treatment had greatly damaged internal structures, collapsed and seriously deformed surfaces, and low contents of active ingredients. Overall, the US pre-treatment could significantly improve the drying quality of Cistanche slices.

In response to the issues of missed detection, false positives, and low recognition rates for specific weed species during weed detection, a YOLOv8-based improved weed detection algorithm named EDS-YOLOv8 is proposed. Improvements were made in three main aspects. First, the YOLOv8 backbone network was enhanced with EfficientViT and RepViT architectures to improve the detection capability of dense-type weeds. Second, different attention mechanisms were added, such as SimAM and EMA, to learn 3D weights and achieve full fusion of features. BiFormer was introduced for dynamic sparse attention and resource allocation. Third, significant module improvement involved introducing dynamic snake convolution into the C2f module to further enhance detection capabilities for deformable objects, especially needle-shaped weeds. The improved model is validated on the established weed dataset. The results show that combining the original backbone network with dynamic snake convolutions yields the highest performance improvement. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 5.6%, 5.8%, 6.4%, and 1%, respectively, and ablation experiments on the effects of the three improvement methods on model performance show that using EfficientViT as the backbone network while simultaneously improving the crucial module and adding the SimAM attention mechanism effectively enhances the model’s performance. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 6%, 5.9%, 6.4%, and 0.7%, respectively.