This study was conducted to investigate the drying kinetics and internal and external mass transfer characteristics of walnuts for an understanding of the drying mechanism. The drying characteristics, mass transfer characteristics, and color of walnut during hot air drying (HAD) were investigated under different initial moisture content (IMC) (0.35, 0.39, and 0.43 g water/g wet mass) and drying temperatures (50, 60, 70, and 80 °C). The results indicated that the IMC and drying temperature both have significant effects on the drying process of walnut, showing the higher the IMC, the longer the preheating time, the smaller the effective moisture diffusivity (D_eff) and mass transfer coefficient (h_m), and the longer the drying time, but reverse results for drying temperature. The values of D_eff and h_m for walnut ranged from 4.94 × 10⁻¹⁰ to 1.44 × 10⁻⁹ m²/s and 1.24 × 10⁻⁷ to 3.90 × 10⁻⁷ m/s, respectively. The values of activation energy for moisture diffusion and mass transfer ranged from 21.56 to 23.35 kJ/mol and 28.92 to 33.43 kJ/mol, respectively. Multivariate linear prediction models were also established for estimating the D_eff and h_m as a function of the HAD process parameters. The drying temperature has a greater effect on the walnut kernel lightness than the IMC. The Verma et al model could be used to describe the HAD process of the walnut. The findings contribute to the understanding of moisture transfer mechanisms in walnuts and have practical value for the evaluation and improvement of drying systems.

To understand the moisture transfer mechanism of walnuts during the combination of hot air (HA) and microwave–vacuum (MV) drying (HA-MVD) process, the drying characteristics and moisture diffusion characteristics of walnut during HA-MVD were investigated. The results indicated that the HA-MVD of walnuts occurred mainly in the falling-rate stage. The value of effective moisture diffusivity (D_eff) dropped continuously with the decrease in moisture content (MC) during the HA drying, while switching to MV drying could truncate the decrease in D_eff and still maintain a high value until the end of drying. The HA temperature, MC of the transition point, microwave power, and MV thermostatic temperature have significant effects on the moisture diffusion characteristics of walnuts. The values of D_eff for walnuts ranged from 2.33 × 10⁻⁹ m²/s to 6.89 × 10⁻⁸ m²/s. The third-order polynomial prediction model of D_eff related to the sample MC and drying conditions was established to describe the dynamic change in the D_eff of walnuts during the HA-MVD process. The application of MVD in the final stage of drying could rapidly increase the internal vapor pressure of the walnuts, accelerate the diffusion speed of the internal moisture, and re-enhance the drying rate. The findings have practical value for the development of efficient and energy-saving drying methods in the walnut industry.

Walnut shell–kernel separation is an essential step in the deep processing of walnut. It is a crucial factor that prevents the increase in the added value and industrial development of walnuts. This study proposes a walnut shell–kernel detection method based on YOLOX deep learning using machine vision and deep-learning technology to address common issues, such as incomplete shell–kernel separation in the current airflow screening, high costs and the low efficiency of manually assisted screening. A dataset was produced using Labelme by acquiring walnut shell and kernel images following shellshock. This dataset was transformed into the COCO dataset format. Next, 110 epochs of training were performed on the network. When the intersection over the union threshold was 0.5, the average precision (AP), the average recall rate (AR), the model size, and floating point operations per second were 96.3%, 84.7%, 99 MB, and 351.9, respectively. Compared with YOLOv3, Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot MultiBox Detector algorithms (SSD), the AP value of the proposed algorithm was increased by 2.1%, 1.3%, and 3.4%, respectively. Similarly, the AR was increased by 10%, 2.3%, and 9%, respectively. Meanwhile, walnut shell–kernel detection was performed under different situations, such as distinct species, supplementary lighting, or shielding conditions. This model exhibits high recognition and positioning precision under different walnut species, supplementary lighting, and shielding conditions. It has high robustness. Moreover, the small size of this model is beneficial for migration applications. This study’s results can provide some technological references to develop faster walnut shell–kernel separation methods.

Clarifying the dissipated energy required for crack expansion is an effective way to control material crushing. Therefore, based on the material fracture probability model and fractal theory, the energy range required for crack extension was determined, and the morphology of the cracks was quantified. This study investigates the influence of walnut size on crack propagation characteristics; this includes its effects on the crack initiation threshold energy, representing resistance to crack initiation, and the crack bifurcation threshold energy, representing resistance to crack bifurcation. The results show that crack extension has a well-defined threshold energy below which cracks do not initiate or bifurcate. The size of walnuts significantly impacts crack propagation characteristics, showing that both crack initiation threshold energy and crack bifurcation threshold energy decrease with increasing walnut sizes. In addition, there is a positive correlation function between the crack initiation threshold energy and the crack bifurcation threshold energy. The experimental results can offer fresh insights into material fracture prediction and serve as a reference for numerical simulations.

The conditioning of moisture content is an effective way to improve walnut-crushing performance. In this study, firstly, walnuts with different moisture contents were used to conduct the crushing experiments. The distributions of fragment sizes of shells and kernels with different moisture contents were analyzed by an image processing and sieving method, respectively. The results show that moisture content significantly affects the fragmentation degree of the shell and kernel, as shown by the differences in the fractal dimensions of shell fragments and the average fragment size of kernel fragments. The critical moisture contents of the shells and kernels, corresponding to the specific states of broken shells and whole kernels, were determined. Then, taking the critical moisture content as the target moisture content, the combination of hot air and microwave vacuum drying applied to dry walnuts was presented and the process parameters were optimized. Optimized process parameters include a hot air temperature of 63.23 °C, a moisture content of the transition point of 24.88%, microwave power of 588.24 W and a microwave vacuum thermostatic temperature of 49.01 °C. At this point, the unit time drying rate, unit mass drying energy consumption, mean square error of moisture content and kernel color parameters were 0.215%/min, 3.03 kW·h/kg, 2.93% and 6.42, respectively. It was confirmed that drying the walnuts to a critical moisture content using an optimized process could both maximally improve the crushing performance and significantly reduce the drying time. The findings provide important information for conditioning the crushing state of walnuts and improving crushing performance and have practical value for the improvement of drying systems.

This study designed two levels of quantitative fertilizer distribution to investigate precision fertilization applications in orchards in South Xinjiang, China, which have vast rows and narrow plant spaces. The machine comprised a base frame, a ditching device, a fertilizing apparatus and an earth-covering device. The design parameters of the flow stabilization screw, conveyor screw and single-ring fertilizer quantity were summarised using theoretical analysis. The single-ring fertilizer quantity of the conveyor screw was verified via an experiment by combining EDEM software. Three-factor and three-level Box–Behnken tests were conducted using the spiral rotation speed of the conveyor, advancing of the speed and the opening degree of fertilizer outlet as the test factors and using the coefficient of variation (CV) of uniformity as the test index—thus obtaining the optimal working parameters. The simulation test results revealed that the single-ring fertilizer quantity of the fertilizing apparatus was 145.6 g, fulfilling the design requirements. The prototype testing results showed that the CV of uniformity was 6.521% when the spiral rotation speed of the conveyor, the opening degree of the fertilizer outlet and the advancing speed were 66 RPM, 42% and 2.7 km/h—thus meeting the needs of precision fertilization operations. The two designed levels of the quantitative fertilizer distributors were applied to fertilization processes in orchards with wide-row spaces and narrow plant spaces in South Xinjiang, China and were able to effectively carry out the precision fertilization applications. These data could also provide references for the optimization of spiral quantitative fertilizer distributors.