In the context of cutting grass in orchards, the practice of leaving cut weeds in the orchard rows hinders the decomposition of the weeds and the absorption of nutrients by the fruit trees. To address this issue, a grass-cutting machine with an integrated sweeping disc was designed to remove weeds from orchard rows and sweep them to the roots of the trees to promote their absorption of nutrients. A coupled simulation platform was established using multi-body dynamics ADAMS and the discrete element method EDEM. The weed-shedding and sweeping device was dynamically analyzed through an ADAMS–EDEM collaborative simulation that enabled the use of a second-order regression orthogonal rotation experiment and response surface methodology. The optimal parameters for the cutting tools, cutter shaft speed, and the number of cutting tools included 23 cutting tools arranged in a single helical pattern for the cutting device, a cutter shaft speed of 728 rpm, and claw-shaped blades as the cutting tools. A prototype machine was built based on the optimized parameters and tested in the field. The results indicated that, when there were 250 m² of weeds, the cutting rate reached 92.96%. The machine was highly maneuverable, and the average remaining weed height in the orchard was 110 mm, which met the national standards and local agricultural requirements. The new orchard grass-cutting and sweeping device meets the technical demands of orchard grass operations in the Xinjiang region of China.

It is easy to cause increases in temperature and the gasification of water in materials, facilitated via supercharging and the generation of instantaneous strong pressure under the collaborative action of a microwave and a vacuum, thus facilitating the internal cell swelling of materials, changes in fibre structures, and the formation of loose and uniform microstructures. In this experiment, mulberries were dehydrated using microwave–vacuum drying technology. The drying characteristics were disclosed by using crispness as the evaluation index and multiple drying parameters (e.g., products’ surface temperature, microwave power, chamber vacuum level and drying height) as the control variables. The optimised Two-term model can predict the dehydration process of mulberries under multiple drying variables, as determined through the experimental data. The optimal drying variables were determined according to the crispness of the dried mulberries. The optimal puffing quality of mulberries could be gained under a product surface temperature = 50 °C, microwave power = 5.45 W/g, a chamber vacuum level = 0.08 MPa and a drying height = 0 cm. The diffusion coefficient of the available water of the mulberries during the microwave–vacuum drying process ranges from 4.98 × 10⁻⁸ to 3.81 × 10⁻⁷, and the activation energy for drying is 183.923 KJ/mol.

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.

In order to explore the equivalent static condition of the impact damage of the Korla fragrant pear under free fall, the Korla fragrant pear was chosen as the research object in this paper, and the height of free fall and rebound were recorded by a high-speed camera to obtain the impact damage energy of the fragrant pear. In addition, a mechanical compression test was conducted on the fragrant pear to obtain its static deformation energy, and the equivalent static condition of the impact damage was calculated based on the mechanical energy conservation formula using the damage volume of the fragrant pear as the evaluation standard. The test verification was carried out on the damaged fragrant pear using a scanning electron microscope test. This study showed that the falling and rebound height of the fragrant pear within the range of the test parameters and under the free fall test conditions conform to linear fitting. The coefficient of determination (R²) was 0.9951, and the impact damage occurred when the corresponding energy reached 205.89 × 10⁻³ J. When the damage energy was within 203.4 × 10⁻³ J and 498.23 × 10⁻³ J, the static damage volume of the fragrant pear was linearly correlated to the impact damage volume, R² was 0.9944, and the maximum deviation between the predicted damage volume and the actual damage volume of the fragrant pear in the verification test was 4.4%. These data can provide a reference for research on the equivalent method of impact damage of the Korla fragrant pear, and provide theoretical guidance to the fruit industry to reduce damage and increase efficiency.

A multi-duct orchard sprayer is designed in this paper to address the problem of low droplet coverage during plant protection in walnut orchards in South Xinjiang, China. This spray comprises a base frame, a pesticide tank, an air delivery system, a copying base frame and a drive system. Effects of the sprayer structure on the flow field were simulated using computational fluid dynamics. Operation parameters were optimised using the response surface analysis and validated using a field test. The results demonstrated that five jet tubes in the simulation design could fulfil the design requirements of GB/T 32250.3-2022. The upper, middle and lower droplet coverages were 74.92%, 90.01% and 69.9%, respectively, when the sprayer’s advancing speed, the angle of jet tubes and the nozzle diameter were 0.53 m/s, 70.27° and 0.81 mm, respectively. The machine can effectively enhance the spraying efficiency and droplet coverage of each canopy during plant protection operations in the new walnut orchards of South Xinjiang, China, and provide references for designing and optimising fruit tree spraying machinery.

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.