The physiological and biochemical processes of Aspergillus flavus (A. flavus) are complex. Monitoring the metabolic evolution of products during the growth of A. flavus is critical to the overall understanding of the fungal and aflatoxin production detection mechanism. The dynamic growth process of A. flavus and the aflatoxin B₁ (AFB₁) accumulation in culture media was investigated with a visible/near-infrared hyperspectral imaging (Vis/NIR HSI) system in the range of 400 to 1000 nm. First, the growth of A. flavus and the synthesis pattern of AFB₁ were monitored on maize agar medium (MAM) culture for 120 h with a 24-h time-lapse imaging interval. Second, to classify the A. flavus growth, a principal component analysis (PCA) was employed, and a support vector machine (SVM) model was established with the PC₁–PC₃ as inputs. The results suggested that the PCA-SVM method could distinguish the A. flavus growth time with a classification accuracy larger than 0.97, 0.91, and 0.92 for calibration, validation, and cross-validation, respectively. Third, regression models to predict the AFB₁ accumulation using hyperspectral images were developed by comparing different pre-processing methods and key wavelengths. The successive projection algorithm (SPA) was adopted to distill the key wavelengths. The experimental results indicated that the standard normal variate transformation (SNV) with the partial least squares regression (PLSR) achieved the optimal regression performance with an R_C value of 0.98–0.99 for calibration and R_V values of 0.95–0.96 for validation. Finally, a spatial map of the AFB₁ concentration was created using the PLSR model. The spatial regularity of the AFB₁ concentration was comparable to the measurement performed. The study proved the potential of the Vis/NIR HSI to characterize the A. flavus growth and the concentration of AFB₁ on the MAM over time.

The classification of silkworm cocoons is essential prior to silk reeling and serves as a key step in improving the quality of raw silk. At present, cocoon classification mainly relies on manual sorting, which is labor-intensive and inefficient. In this paper, a cocoon detection algorithm S-YOLOv8_c based on the cooperation of MobileSAM and YOLOv8 for the mountage cocoons was proposed. The MobileSAM with a designed area thresholding algorithm was used for the semantic segmentation of mountage cocoon images, which could mitigate the effect of complex backgrounds and maximize the discriminability of cocoon features. Subsequently, the BiFPN was added to the neck of YOLOv8 to improve the multiscale feature fusion capability. The loss function was replaced with the WIoU, and a dynamic non-monotonic focusing mechanism was introduced to improve the generalization ability. In addition, the GAM was incorporated into the head to focus on detailed cocoon information. Finally, the S-YOLOv8_c achieved a good detection accuracy on the test set, with a mAP of 95.8%. Furthermore, to experimentally validate the sorting ability, we deployed the proposed model onto the self-developed Cartesian coordinate automatic cocoon harvester, which indicated that it would effectively meet the requirements of accurate and efficient cocoon sorting.

The physical parameters and mechanical properties of the tussah cocoon are of vital importance for the design of a tussah cocoon leaf stripper. In this paper, the freshly harvested tussah cocoons wrapped with fresh leaves (TCFL) and the plucked and dried tussah cocoons (TCDL) were taken as research objects. The physical parameters and mechanical properties of the tussah cocoon with wrapped leaves, leafless tussah cocoons, tussah cocoon shells, tussah pupae, cocoon handle, and oak leaves were measured. The experiment showed that firstly, the radial deformation of the cocoon shell of the tussah cocoon should be controlled to be within 1.05 mm and the axial deformation to be within 1.6 mm. The pressure on the fresh leaf cocoon shell and the dry leaf cocoon shell cannot exceed 14.54 ± 3.16 N and 32.43 ± 7.04 N, respectively. Secondly, when the oak leaves are peeled off, the tensile force on the fresh leaves and dry leaves should be greater than 5.05 ± 3.63 N and 3.35 ± 1.85 N, respectively. Finally, the cocoon stalk can have a negative impact on the leaf-stripping process. These results can provide theoretical support for the design of a tussah leaf stripper to avoid damaging the cocoon shell and tussah pupa during the leaf stripping process.

Mulberry (Morus spp.) leaves, fruits, branches, and root bark are rich in bioactive compounds, including 1-deoxynojirimycin (1-DNJ) and γ-aminobutyric acid (GABA), supporting their potential use in food, medicinal, and feed applications. Their high moisture content, however, makes fresh materials highly susceptible to postharvest quality deterioration, making drying essential for stabilization and high-value utilization. Drying technologies involve trade-offs among efficiency, energy consumption, sensory quality, rehydration, and bioactive-compound retention. This review provides a comprehensive overview of pretreatment and drying technologies for mulberry materials, with particular attention to differences in raw-material characteristics, processing conditions, analytical methods, and reporting bases that limit direct comparisons among studies. Current evidence suggests that low-temperature, low-oxygen, or short-duration technologies, including vacuum freeze-drying, microwave drying, and microwave-vacuum drying, may better preserve quality in certain thermosensitive products, although their benefits remain product- and process-dependent. Hot-air, solar, infrared, heat-pump, and hybrid drying remain practical options for bulk products but require optimization to balance quality, energy efficiency, and scalability. For juice and functional powders, carrier selection, powder properties, and bioaccessibility require further study. Overall, the available evidence is heterogeneous, and some conclusions rely on limited mulberry-specific data or extrapolation from related plant matrices. Future research should emphasize standardized quality evaluation, harmonized reporting, kinetic modeling, multi-objective optimization, online monitoring, energy and carbon-footprint assessment, and industrial-scale validation.