The cleaning system of rapeseed pickup harvesters can often be confined to residue blockage, high seed impurity rates, and high loss rates in hilly and mountainous terrains. In this study, a dual-air-duct cleaning device was designed to fully meet the requirements of large-scale rapeseed harvesting. The cleaning system also consisted of a scraper conveyor, an acceleration roller, a positive-pressure fan, a cyclone separation cylinder, and a negative-pressure fan. The acceleration roller was designed to prevent blockages for the rapid removal of the residues in the cleaning chamber. Structural parameters were set as a length of 180 mm, a diameter of 100 mm, a spiral blade angle of 25°, and four blades. The kinematic and dynamic analysis was performed on the residues during cleaning. The main influencing factors on the performance of the device were identified as the acceleration roller speed, the airflow direction angle, and the speed of the positive pressure fan. Fluent-EDEM gas-solid coupling simulations were conducted on the rapeseed residue cleaning to validate the performance of the dual-air-duct cleaning device. The simulation parameters were set with an acceleration roller speed of 1 100 r/min, a positive-pressure fan speed of 1 600 r/min, and a positive-pressure fan airflow direction angle of 20°. The residues moved in the direction of the airflow during cleaning, particularly with the uniform trajectories of the relative movement and the minimal impact of the residues on the airflow. The airflow shared the greater impact on the residue motion. The motion trajectories of different residue components showed that the velocity of the residues underwent two significant changes: one was from the action of acceleration rollers, and another was from the positive pressure fan airflow. The simulation results indicated that there was a great variation in the velocity of the different components, thus allowing for the better separation of seeds, stems, and impurities. Effective cleaning of the residues was realized after separation. The statistical analysis revealed that the dual-air-duct cleaning device achieved a seed impurity rate of 2.29% and a loss rate of 2.71% after simulation. The better cleaning of rapeseed seeds was realized with optimal structural parameters. A series of bench tests were carried out with a feeding rate of 0.2 kg/s. Among them, the influencing factors were taken as the acceleration roller speed, positive-pressure fan airflow direction angle, and positive-pressure fan speed. While the seed impurity rate and loss rate were taken as the evaluation indices. Single-factor tests were conducted within the following ranges of the parameters: the acceleration roller speed (800-1 200 r/min), positive-pressure fan airflow direction angle (5°-25°), and positive-pressure fan speed (1 200-2 000 r/min). The optimal combination of the parameters was determined as the acceleration roller speed (1 000-1 200 r/min), airflow direction angle (15°-25°), and fan speed (1 400-1 800 r/min). After that, a three-factor and three-level orthogonal experiment was performed to verify the simulation. The SPSS software was used for the range and variance analysis. The results showed that the positive pressure fan speed shared an extremely significant effect on the impurity and loss rates. The acceleration roller speed exhibited a significant effect on the impurity rate and an extremely significant effect on the loss rate. The airflow direction angle had a significant effect on both of them. A weighted evaluation was also made to clarify the impact of each experimental factor on the seed impurity and loss rates. The loss reduction was prioritized to keep the seed impurity rate low in the actual cleaning. As such, the weight of the impurity rate was set at 0.4 and the loss rate weight at 0.6. A comprehensive score was used as the evaluation standard. Three main influencing factors on the cleaning performance were: positive-pressure fan speed, acceleration roller speed, and airflow direction angle. The optimal combination of parameters was achieved in the acceleration roller speed of 1 100 r/min, airflow direction angle of 20°, and positive-pressure fan speed of 1 400 r/min. The dual-air-duct cleaning device achieved a seed impurity rate of 2.35% and a loss rate of 2.75%. Field trials with the optimal parameters showed that there was smooth material feeding with no blockage in the pickup header. The average impurity rate was 3.05%, and the average loss rate was 3.47%. The test results confirmed that the average impurity and loss rates were below 5%, fully meeting the national standards of rapeseed harvesting. This finding can provide theoretical support to optimize the cleaning devices of the rapeseed harvester in hilly and mountainous areas.

Chestnut industry has been confined to low productivity, high labor expenses, and operational hazards. Information technology can be expected to deal with these obstacles in the chestnut production. Fruit object detection can also offer essential technical assistance and solutions to the intelligent division of chestnut harvesting. In this study, a chestnut fruit object detection (YOLOv8-PBi) was proposed using the lightweight improved YOLOv8s. The research object was taken in the natural environment of Hubei Province, China. This approach brought various advancements, where the YOLOv8 was used as the basis network. Initially, the C2f-PConv module was constructed to minimize the floating-point number and computation amount using partial convolution (PConv), in order to improve the utilization of computational capacity. A weighted bidirectional feature pyramid network (BiFPN) was then employed to enhance the cross-scale connections and feature fusion performance. Thirdly, the convergence speed and detection performance were enhanced to change the bounding box loss function, in order to employ the dynamic non-monotone focusing technique Wise intersection over union (WIoU). Lastly, transfer learning was used to raise the accuracy and generalizability of the model. The enhanced YOLOv8-PBi model was scored by 89.4%, 74.9%, and 84.2% in the accuracy, recall, and average precision, respectively. The model weight was 46.22% less, compared with the original base network YOLOv8s. The gains were observed in the accuracy, recall, and average precision of 1.3, 1.5, and 1.8 percentage points, respectively. Additionally, the PC inference speed increased from 81.9 to 108 frames per second. The average detection accuracy increased by 0.8 percentage points after substituting the original bounding box loss function, CIoU, for WIoU. Both the gradient descent and fitting speeds increased significantly. BiFPN was replaced with the original feature fusion network, in order to increase the model recall rate by 0.9 percentage points. The model was allowed to concentrate more precisely on the attributes specific to chestnut fruit objects. The enhanced performance was achieved in the C2f-PConv approach, with an average accuracy of 3.5, 9.0, 7.5, and 9.9 percentage points greater than the standard lightweight feature extraction networks of MobileNetV3, MobileNetV2, GhostNet, and ShuffleNetV2. The enhanced WIoU loss function demonstrated that the WIoU had the lowest loss value and the fastest iterative convergence speed, compared with the CIoU, EIoU, and SIoU. The model size was the smallest, compared with the mainstream object identification models, such as SSD, Fast-RCNN, YOLOv5s, YOLOv5m, YOLOv7-tiny, and YOLOv8s. The average accuracy was found to be 21.33, 47.82, 4.4, 1.9, 6.2, and 1.8 percentage points greater than those of the other models, respectively. The model was finally deployed on the edge-embedded devices using TensorRT acceleration. The better performance was achieved in the detection frame rate of 43 frames per second and a detection speed of 23.26 ms, fully meeting the device deployment requirements. YOLOv8-PBi enhanced the misdetection and missed detection scenarios in the environments of backlight, overcast, and occlusion, compared with YOLOv8s. This finding can provide a technical foundation to identify the chestnut fruits in the process of intelligent chestnut harvesting.