Fresh aquatic products, due to their high water activity, are susceptible to microbial contamination and spoilage, resulting in a short shelf life. Drying is a commonly used method to extend the shelf life of these products by reducing the moisture content, inhibiting microbial growth, and slowing down enzymatic and chemical reactions. However, the drying process of aquatic products involves chemical reactions such as oxidation and hydrolysis, which pose challenges in obtaining high-quality dried products. This paper provides a comprehensive review of drying processing techniques for aquatic products, including drying preprocessing, drying technologies, and non-destructive monitoring techniques, and discusses their advantages and challenges. Furthermore, the impact of the drying process on the quality attributes of dried products, including sensory quality, nutritional components, and microbial aspects, is analyzed. Finally, the challenges faced by drying processing techniques for aquatic products are identified, and future research prospects are outlined, aiming to further advance research and innovation in this field.

Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R² values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed.

The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R², MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

Podocarpus macrophyllus is an evergreen tree with significant ornamental, economic, and medicinal value, widely used in landscape gardening and bonsai production. However, systematic research on the optimal substrate ratios required for its efficient cultivation remains relatively scarce. This study compared the effects of two cultivation substrates (SJ1: 80% native soil + 20% fine sand and SX2: 25% native soil + 25% coarse sand + 25% peat soil + 25% coconut coir) on the growth of P. macrophyllus. Soil physicochemical properties and plant physiological and biochemical indices were measured, and the rhizosphere microbial community structure was analyzed using Illumina MiSeq high-throughput sequencing. The results show that P. macrophyllus grown in the SX2 substrate exhibited significantly greater ground diameter, plant height, chlorophyll content, and soluble protein content than those in the SJ1 substrate. Microbial community analysis indicates that the two different substrates had little impact on alpha diversity. In the bacterial community, the dominant phylum in the SJ1 substrate was Acidobacteriota, whereas in the SX2 substrate, it was Pseudomonadota. In the fungal community, Ascomycota was the dominant phylum in both SJ1 and SX2. Redundancy analysis (RDA) reveals that water content and total porosity were the primary factors influencing the bacterial community structure. Based on physiological indicators and microbial community composition, the SX2 substrate was more conducive to the growth of P. macrophyllus in terms of plant height and ground diameter. Therefore, this study provides valuable insights for substrate selection and optimization in the cultivation of P. macrophyllus.

The detachment of railway tunnel lining constitutes a grave danger to train operation safety and drastically curtails the tunnel’s service life. This study endeavors to efficiently detect the void defects in railway tunnel lining by creating a finite element model of tunnel lining structures. Utilizing this model, the study simulates the nonlinear acoustic wave propagation cloud maps for three representative tunnel lining structures: void-free, air void, and water void. This facilitates a thorough examination of the acoustic signal characteristics in the wavefield, time domain, and frequency domain. To satisfy the precision and efficiency demands of tunnel lining void detection, this study has devised and developed a portable acoustic detector that incorporates automatic analysis and processing capabilities and is furnished with a high-performance rare-earth magneto-strictive acoustic excitation device. This detection system can swiftly detect and assess typical void defects in tunnel lining. To further validate the effectiveness of this system, this study conducted lining defect detection in the Pingdao Railway Tunnel in the eastern Qinling Mountains. The test results show that the detection rate of this system for both air-filled and water-filled voids with a width of 1 m reached 100%, demonstrating its extremely high application value.