Xin Huang

Moisture content critically affects the stability, nutritional value, and shelf life of pellet feed during production and storage, yet conventional measurement methods are limited by slow response, poor accuracy, strong temperature sensitivity, and poor on-site applicability. To overcome these issues, this study proposes a dielectric-based detection method incorporating electrode geometry optimization and temperature-adaptive compensation, using sheep pellet feed as the test material. COMSOL Multiphysics simulations revealed the effects of electrode thickness, spacing, and overlap area on the fringe field; the optimal configuration of 1 mm thickness, 40 mm spacing, and 15,000 mm² overlap area reduced fringe field width by 32.7% and improved field uniformity by 41.2%. A cubic polynomial coupling model relating temperature, moisture content, and relative permittivity was established via a two-factor, five-level full factorial design with polynomial response surface regression, yielding a coefficient of determination of 0.996, an adjusted R² of 0.994, and a root-mean-square error of 0.123, thereby enabling temperature-adaptive compensation. An STM32F103VET6-based prototype with capacitance sensing, temperature acquisition, display, and storage was developed. Across moisture contents of 10% to 20% and temperatures of 15 to 35 °C, it achieves a mean absolute error of 0.350%, a mean relative error of 2.600%, a repeatability coefficient of variation of 1.120%, and a one-hour drift below 0.080%. The device offers a fast, low-cost, and interference-resistant solution for real-time feed quality monitoring.

To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environments. The proposed method integrates a YOLOv8-ResCBAM trunk detection model, a reverse ray projection fusion algorithm, and geometric constraint-based navigation line fitting techniques. The object detection model enables high-precision real-time detection of pomegranate tree trunks. A reverse ray projection algorithm is proposed to convert pixel coordinates from visual detection into three-dimensional rays and compute their intersections with LiDAR scanning planes, achieving effective association between visual and LiDAR data. Finally, geometric constraints are introduced to improve the RANSAC algorithm for navigation line fitting, combined with Kalman filtering techniques to reduce navigation line fluctuations. Field experiments demonstrate that the proposed fusion-based navigation method improves navigation accuracy over single-sensor methods and semantic-segmentation methods, reducing the average lateral error to 5.2 cm, yielding an average lateral error RMS of 6.6 cm, and achieving a navigation success rate of 95.4%. These results validate the effectiveness of the vision and 2D LiDAR fusion-based approach in complex orchard environments and provide a viable route toward autonomous navigation for orchard robots.

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments.

The sugarcane lifting–cutting System (SLS) is the core component of the sugarcane harvester, and the influence of its stalk lifting height on the mechanism of perennial root-cutting damage is still unclear. The test was conducted with the vertical height of centroid (VHC), forward velocity, and cutter rotational speed. Numerical quantification of perennial root based on the cutting breakage effect and cutting breakage grade (CBG) classification was used as the evaluation index. The test results showed that the VHC had a highly significant effect on the CBG of the perennial root. The CBG of the stalk decreased significantly with the lifting height. The average CBG was greater than 2.0 when the stalk lifting height was severely collapsed. The CBG ≤ 1.6 was considered qualified, and the requirements were met when the VHC of the stalk was about 0.85~1.0 m at a forward velocity of 1~3 km/h. The inverted angle of the stalk being lifted was selected to be 45° as the evaluation standard to measure the qualified lifting of the spiral sugarcane lifter. This study provides technical support for the optimal design and selection of operating parameters of the sugarcane harvester.

Research shows that the accurate acquisition of flight parameters of the plant protection UAV and accurate evaluation of flight parameter quality have great significance for improving the effect and precision of spraying. In order to further improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, this study conducted an evaluation and experiment of the flight parameter quality of the plant protection UAV using a laser tracker. The experimental results showed that the current plant protection UAV used the average altitude and speed of the onboard sensors to determine whether it reached the preset flight operation parameters, but this interpretation method could not accurately reflect the actual flight situation. Laser trackers could obtain more accurate flight parameters, especially instantaneous flight parameters. Compared with the laser tracker, the flight trajectory, altitude, and speed of the UAV reflected by onboard sensors were erroneous and tended to be smooth and stable. This method can obtain more accurate flight parameters, improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, and provide data support and a reference for the precision spraying and performance improvement of the plant protection UAV.

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.