In order to realize the real-time detection of an unmanned fishing speedboat near a ship ahead, a perception platform based on a target visual detection system was established. By controlling the depth and width of the model to analyze and compare training, it was found that the 5S model had a fast detection speed but low accuracy, which was judged to be insufficient for detecting small targets. In this regard, this study improved the YOLOv5s algorithm, in which the initial frame of the target is re-clustered by K-means at the data input end, the receptive field area is expanded at the output end, and the loss function is optimized. The results show that the precision of the improved model’s detection for ship images was 98.0%, and the recall rate was 96.2%. Mean average precision (mAP) reached 98.6%, an increase of 4.4% compared to before the improvements, which shows that the improved model can realize the detection and identification of multiple types of ships, laying the foundation for subsequent path planning and automatic obstacle avoidance of unmanned ships.

Considering the high requirements of current kiwifruit picking recognition systems for mobile devices, including the small number of available features for image targets and small-scale aggregation, an enhanced YOLOX-S target detection algorithm for kiwifruit picking robots is proposed in this study. This involved designing a new multi-scale feature integration structure in which, with the aim of providing a small and lightweight model, the feature maps used for detecting large targets in the YOLOX model are eliminated, the feature map of small targets is sampled through the nearest neighbor values, the superficial features are spliced with the final features, the gradient of the SiLU activation function is perturbed, and the loss function at the output is optimized. The experimental results show that, compared with the original YOLOX-S, the enhanced model improved the detection average precision (AP) of kiwifruit images by 6.52%, reduced the number of model parameters by 44.8%, and improved the model detection speed by 63.9%. Hence, with its outstanding effectiveness and relatively light weight, the proposed model is capable of effectively providing data support for the 3D positioning and automated picking of kiwifruit. It may also successfully provide solutions in similar fields related to small target detection.

In order to investigate the mechanism of interaction between a vertical rotary tiller and the soil to obtain the best structural and operational parameters of the tool, improve the operational quality of the rotary tiller, achieve the effect of soil breaking and leveling to meet the agronomic requirements of tillage, a series of simulation tests was conducted through the design of 3D models of rotary tillers with different structural forms and the building of discrete element models of the action between the rotary tiller and the soil. The virtual simulation of different operating parameters and structural parameters of vertical rotary tiller was carried out by EDEM, and the effects of operating parameters, tool bending angle, and cutter installation number on the operating effect were analyzed. The parameter combination with the best operating effect was obtained through orthogonal test analysis and verified by soil trough test. A soil tank test showed that, with the optimal combination of operating and structural parameters, the average soil breaking rate of the vertical rotary tiller was 81.3%, which meets the agronomic requirements of tillage operation, and the actual power consumption was 6% higher than the simulation value, which was sufficiently close to verify the validity of the simulation. This study can lay a foundation for optimization research on the vertical miniature rotary tiller.

We developed an adaptive algorithm to reduce rice loss in harvesting, promote threshing and improve the quality and efficiency of small- and medium-sized rice harvesters operating in southern China’s hilly and mountainous areas. Using a fuzzy PID control algorithm, the harvester adapts to the rice harvesting conditions in southern China, and monitors rice feed volume changes and instantly adjust the traveling speed to optimize feed volume levels and threshing quality. We compared and analyzed the algorithm and the traditional PID control regulation effect in the simulation experiment. The algorithm had a quicker response speed and stable accuracy. In the field trial, the average error rate was 3.4%, and the maximum error rate was 5.1%, with most data points centered around the ideal feeding rate of 3.2 kg/s. Our results showed that the algorithm’s stability, accuracy, and real-time performance met the threshing loss reduction requirements of southern China’s rice harvesting operations.

To solve the problem of poor steering consistency for each steering wheel of a four-wheel, independent-steering, high-clearance paddy field management machine, given that the true steering angle of the front wheel cannot be directly obtained through the left and right front wheels steering angle value, a BP (Back Propagation) neural network equivalent steering angle prediction method based on signal feature extraction is proposed in this paper, which can be used to obtain the equivalent steering angle of the front wheel. First, the kinematics model of the paddy field management machine was constructed with the prediction of the steering angle of the management machine as the object. The body was set up in two application environments of cement ground and paddy field, two moving speeds (25 cm/s, 50 cm/s) and three preset steering angles (0°, ±10°) to form six motion modes. The steering angle of the front wheel of the body, the three-axis acceleration of the body, and the angular velocity of the z-axis under twelve conditions were collected. Combining the collected data with the actual trajectory data of the differential BeiDou, the feasibility analysis of the equivalent steering angle prediction was carried out. With the aim of determining an appropriate combination of input factors for optimal equivalent steering angle prediction by the BP neural network, we used FFT (Fast Fourier Transform) and power spectrum conversion to extract and analyze the signal features of the airframe attitude data and obtain the frequency characteristics of the peak power point of the power spectrum, respectively. Through a factor combination test, the optimal BP neural network training factors were determined and, finally, we confirmed that the rotation angles of the front wheels and the z-axis angular velocity may be used as effective training factors. In our investigation on the test set, the mean square error of the equivalent steering angle was found to be less than 0.66°, demonstrating that our approach is effective for obtaining the true steering angle of the front wheel.