A field-based apple detection and grading device was developed and used to detect and grade apples in the field using a deep learning framework. Four features were selected for apple grading, namely, size, color, shape, and surface defects, and detection algorithms were designed to discriminate between the four features using machine vision and other methods. Then, the four apple features were fused, and a support vector machine (SVM) was used for infield apple grading into three grades: first-grade fruit, second-grade fruit, and other-grade fruit. The results showed that for a single index, the accuracy of detecting the apple size, the fruit shape, the color, and the surface defects, were 99.04%, 97.71%, 98%, and 95.85%. The grading accuracies for the first-grade fruit, second-grade fruit, other-grade fruit, and the average grading accuracy based on multiple features were 94.55%, 95.71%, 100%, and 95.49%, respectively. The field experiment showed that the average grading accuracy was 94.12% when the feeding interval of the apples was less than 1.5 s and the walking speed did not exceed 0.5 m/s, meeting the accuracy requirements of field-based apple grading.

The most common harvesting method of Lycium barbarum L. (L. barbarum) is manual harvesting, resulting in low efficiency and high cost. Meanwhile, the efficiency of vibration harvesting, which is considered an efficient mechanical harvesting method, can be significantly improved if the optimized resonance frequency of the shrub can be obtained. To vibration harvest fruit efficiently, a 3D model of the shrub was established based on measurements of the shape parameters, and material mechanics models of the branches were established based on physical tests. The modal analysis of the shrub based on finite element method (FEM) simulation was performed to obtain the range of resonance frequency, and the modal experiment of the shrub using acceleration sensors and an impact hammer was conducted to obtain the accurate resonance frequency. Based on the results of the modal analysis and experiment, the optimized resonance frequency was determined to be 2 Hz. The field experiment showed that the fruit fell off when the branches were vibrated at this frequency. The results provide the design basis for the efficient vibration harvesting of L. barbarum.

A pneumatic oscillating Chinese wolfberry harvester was designed to improve the efficiency of manual harvesting and minimize fruit damage by mechanical harvesting. The shedding model of Chinese wolfberry under pneumatic forces was established based on kinetic analysis, and the flow field characteristics were investigated using computational fluid dynamics (CFD). The position and shape of the fan outlet were determined by comparing the effects of a linear reduction outlet and a Laval reduction outlet on the outlet airflow rate. The size of the oscillating mechanism was optimized with nonlinear constraints to achieve uniform oscillation of the airflow. A three-factor, three-level orthogonal test was conducted on the NingQi 7 wolfberry variety. The flow rate at the outlet, the oscillation frequency, and the outlet distance were the test factors, and the net harvesting rate of ripe fruits, the harvesting rate of green fruits, and the damage rate of ripe fruits were the test indices. The results showed that the net harvesting rate of ripe fruits was 85.21% at an outlet distance of 10 cm, an outlet flow rate of 70 m·s⁻¹, and an oscillation frequency of 2 Hz. This study provides references for the design of pneumatic harvesters for Chinese wolfberry.

The application method of granular fertilizers and wheat seeds depends on the colter design and parameters. In this research, a new double disc colter is studied to apply the wheat seeds to the horizontal band 12 cm in width and apply granular fertilizers deeper 2 cm than the wheat seed level precisely to the middle of the band. Applying granular fertilizers and wheat seeds at different levels increases the granular fertilizer dose without harm to the wheat seeds. Furthermore, applying high doses of wheat seeds to the horizontal band decreases the competition between the seeds and suppresses the weeds. Therefore, preparing a plain seedbed after applying the fertilizers and distributing wheat seeds to the horizontal band was the research objective. The comparison experiments of the base and designed double disc colters were provided in the soil bin determining the horizontal and vertical forces and the placement of the fertilizers and seeds. The discrete element method (DEM) was used to track the soil particle behavior interacting with the double-disc colter. The simulation results and actual experiment results were satisfactory when the AB length of the wing orifice was 60 mm.

The interaction properties of the seeds have to be calibrated to simulate the realistic behavior of the seed bulk. Here, a simple and accurate calibration method of DEM interaction properties of seeds with adequate equipment to simulate each seed’s behavior remains a challenge. In this research, the rotary drum is chosen as simple equipment to calibrate particle–particle and particle–material interaction properties, as there is a lack of research on whether the rotary drum is adequate equipment to calibrate particle–material interaction properties. Therefore, this article calibrates particle–particle and particle–material static and rolling interaction coefficients using a rotary drum. The calibration of particle–material static and rolling friction coefficients are described using the rotating drum with a 45 degrees inclination. The particle–particle static and rolling friction coefficients were calibrated according to the angle of repose when the rotary drum is vertical.

The current mechanical harvesting methods of Lycium barbarum L. are labor intensive and cause too much damage, but vibrating and comb-brushing harvesting can increase the efficiency while minimizing the damage. However, optimizing the main factors and their parameter values of vibrating and comb-brushing harvesting is challenging. To achieve the high-efficiency and low-damage harvesting of L. barbarum, firstly, the mechanical models of the materials used in the experiments were established based on the physical tests. Then, the vibrating and comb-brushing harvesting simulations were conducted based on FEM to acquire the ranges of the parameter values. The effects of the rotating speed, material, and amplitude on the harvesting rate of ripe fruit and harvesting rate of unripe fruit, as well as the damage rate of ripe fruit were determined based on RSM. Finally, the optimized parameters were obtained and verified using the field experiments. The field experiments showed that the harvesting rate of ripe fruit was 85.8%, the harvesting rate of unripe fruit was 10.5%, and the damage rate of ripe fruit was 9.7%. The findings provided the optimal parameter values, which were a design basis for the vibrating and comb-brushing harvesters of L. barbarum.