Penicillium italicum (P. italicum) can cause significant economic loss of fruits and vegetables. Although garlic oil (GO) is an effective antimicrobial agent, the unstability and hydrophobicity limit its use as an environmentally friendly alternative to the conventional antibiotics against P. italicum. In this study, we focused on the fabrication and characterization of a functional GO nanoemulsion (NE) using ultrasonic technique and revealed the antifungal mechanism of the GO NE on P. italicum based on morphological, structural and molecular analyses. The optimal hydrophilic lipophilic balance (HLB) value determined for GO was 14 through the combination of Tween 80 and Span 80. Then the Box-Benhnken Design (BBD) was applied to produce the GO NE and the effects of different fabrication parameters on the particle size were evaluated. The optimal GO NE was selected with the GO concentration of 5.5%, the Smix concentration of 10%, the ultrasonic time of 5 min and the power of 50%. This GO NE had the smallest particle size of 52.27 nm, the best antifungal effect and the most stability. Furthermore, the antifungal mechanism of the GO NE on P. italicum was evaluated by extracellular conductivity, micro-Raman spectra, fluorescence imaging and scanning electron microscopy (SEM) imaging. The results presented that the GO NE retained the antifungal active ingredients. The fungal cell structure and morphology were malformed after treated with the GO NE and the lipids, nucleic acids and protein of P. italicum were destructed. Finally, the optimal GO NE was applied in vivo and P. italicum in citrus was successfully inhibited. It indicated that the optimal GO NE had the better antifungal activity against P. italicum than the pure GO. Besides, the minimum inhibitory concentration (MIC) of GO after preparing into the NE was changed from 3.7% to 0.01265% with about 300 times improvement of bioavailability. Therefore, the synthetic GO NE which promoted the bioavailability of GO was recommended as a promising alternative to inhibit P. italicum in vegetables and fruits.

Seed vigor is one of the essential contents of agricultural research. The decline of seed vigor is described as an inevitable process. Recent studies have shown that the oxidative damage caused by reactive oxygen species (ROS) is the main reason for the destruction of various chemicals in seeds and eventually evolves into seed death. The traditional vigor tests, such as the seed germination test and TTC staining, are commonly used to assess seed vigor. However, these methods often need a large number of experimental samples, which will bring a waste of seed resources. At present, many new methods that are fast and nondestructive to seeds, such as vibrational spectroscopic techniques, have been used to test seed vigor and have achieved convincing results. This paper is aimed at analyzing the microchanges of seed-vigor decline, summarizing the performance of current seed-vigor test methods, and hoping to provide a new idea for the nondestructive testing of a single seed vigor by combining the physiological alterations of seeds with chemometrics algorithms.

At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-quality apples, separate fruit trays were designed to convey apples and used to prevent apples from being bruised during image acquisition. A semantic segmentation method based on the BiSeNet V2 deep learning network was proposed to segment the defective parts of defective apples. BiSeNet V2 for apple defect detection obtained a slightly better result in MPA with a value of 99.66%, which was 0.14 and 0.19 percentage points higher than DAnet and Unet, respectively. A model pruning method was used to optimize the structure of the YOLO V4 network. The detection accuracy of defect regions in apple images was further improved by the pruned YOLO V4 network. Then, a surface mapping method between the defect area in apple images and the actual defect area was proposed to accurately calculate the defect area. Finally, apples on separate fruit trays were sorted according to the number and area of defects in the apple images. The experimental results showed that the average accuracy of apple classification was 92.42%, and the F1 score was 94.31. In commercial separate fruit tray grading and sorting machines, it has great application potential.