The application of robotics has great implications for future food security, sustainable agricultural development, improving resource efficiency, reducing chemical pesticide use, reducing manual labor, and maximizing field output. Aiming at the problems of high labor intensity and labor shortage in the fields of pesticide application, weeding, and field information collection, a multifunctional and electric field management robot platform is designed, which has four switching steering modes (Ackermann steering, four-wheel steering, crab steering, and zero-radius steering), and its wheel-track can be automatically adjusted. Commonly used spraying booms, weeders, crop information collectors, and other devices can be easily installed on the robot platform. A multi-sensor integrated navigation system including a satellite positioning system, an RGB camera, and a multi-line lidar is designed to realize the unmanned driving of the robot platform in a complex field environment. Field tests have shown that the robot can follow the set route, and tests under simulated conditions have indicated that it can also dynamically correct paths based on crop rows by using a visual system. Results from multiple trials showed that the trajectory tracking accuracy meets the requirements of various field management operations.

Pollination success is essential for hybrid oilseed rape (OSR, Brassica napus) seed production, but traditional pollination methods are not efficient. The unmanned agricultural aerial system (UAAS) has developed rapidly and has been widely used in China. When flying, the wind field generated by the rotors overcomes the UAAS gravity, and it blows and disturbs the crops below, which helps the pollen spread. In order to investigate the distribution law of the three-dimensional (direction x, y, z) airflow field, experiments involving three levels of flight speed (FS) at 4.0, 5.0, and 6.0 m/s, and three levels of flight height (FH) at 1.5, 2.0, and 2.5 m were conducted in the OSR field by using an electric four-rotor UAAS P20. The effects of FS and FH on airflow velocities ($v_x$, $v_y$, $v_z$) were analyzed. High-speed dynamic camera (HSDC) technology was used to capture the swings of OSR plants under airflow field disturbance. OSR pollen samples were collected during the experiments. The results showed that the airflow field in the direction x was mainly concentrated on the center of the flight path (S3), and the maximum wind velocity of direction x was 8.01 m/s (T1, S3). The direction x airflow field width was distributed almost symmetrically, but the center position shifted easily, due to crosswind. The airflow field in the direction y was distributed on both sides of the center flight path, and the velocity was generally larger, with the maximum at 7.91 m/s (T1, S2). The airflow field in the direction z was distributed irregularly, and the velocity was small. The FH had highly significant impacts on $v_x$ (p < 0.01), and the interaction of FS and FH had significant impacts on $v_x$ (0.01 < p < 0.05), while the FS had no significant impact on $v_x$ (p = 0.70804 > 0.05). The FS, FH, and interaction of FS and FH all had highly significant impacts on $v_y$ (p < 0.01). The swings of the OSR plant captured by the HSDC proved that the UAAS airflow field could effectively blow the OSR plant. The swing amplitude changes showed a positive correlation with airflow velocities ($v_x$) in general. Although the observed OSR plant swung forward and backward repeatedly, there was a law of first forward, and then backward, and forward again at the beginning of each swing. The pollen collected on the sampler verified that the UAAS airflow field could help with pollen spread. The research results provide technical support for UAAS application on supplementary pollination for hybrid OSR seed production.

Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. Existing methods often compromise between real-time performance and detection accuracy, limiting their practical field applicability. This study develops a high-precision, efficient crop row detection algorithm specifically optimized for soybean–corn compound planting conditions, addressing both computational efficiency and recognition accuracy. In this paper, a real-time soybean–corn crop row detection method based on GD-YOLOv10n-seg with principal component analysis (PCA) fitting was proposed. Firstly, the dataset of soybean–corn seedling crop rows was established, and the images were labeled with line labels. Then, an improved model GD-YOLOv10n-seg model was constructed by integrating GhostModule and DynamicConv into the YOLOv10n-segmentation model. The experimental results showed that the improved model performed better in MPA and MIoU, and the model size was reduced by 18.3%. The crop row center lines of the segmentation results were fitted by PCA, where the fitting accuracy reached 95.08%, the angle deviation was 1.75°, and the overall processing speed was 61.47 FPS. This study can provide an efficient and reliable solution for agricultural autonomous navigation operations such as weeding and pesticide application under a soybean–corn compound planting mode.

Aphid is one of the main insect pests of wheat in the flowering stage, so timely and effective control of wheat aphids plays an important role in ensuring wheat yield. The crop protection Unmanned Aerial Vehicle (UAV) is widely used in the control of wheat pests and diseases nowadays. In order to screen out the suitable operation parameters of the crop protection UAV to control the wheat aphids, this study conducted wheat aphid distribution investigation tests and droplet distribution tests. With the P20 electric four-rotor crop protection UAV (Guangzhou Jifei Technology Co., Ltd., Guangzhou, China) as the test equipment, four levels of flight speed (FS: 3, 4, 5, 6 m/s) and three levels of flight height (FH: 1.5, 2, 2.5 m) were combined as operation parameters, tests were carried out to compare the density and uniformity of droplet coverage, and the wheat aphid control tests were carried out by using the optimized operation parameters. The results of the wheat aphid distribution investigation test showed that aphids mainly distributed in the lower layer of the wheat plant canopy, accounting for more than 90.61%. The results of the droplet distribution test showed that with the increase in FS and FH, the coverage density and the droplet distribution uniformity in the upper and lower layers of wheat showed a downward trend under the condition of considering the boundary overlap of spraying width (SW) in multi-routes. Through the comparison of operation efficiency and droplet distribution quality, two combinations of parameters A1 (FS: 3 m/s, FH: 1.5 m) and B1 (FS: 4 m/s, FH: 1.5 m) were selected for the aphid control effect test. The results of the control test showed that the average control effect of A1 (92.05%) on aphids was 10.3% higher than that of B1 (81.75%) 7 days after pesticide application, which indicated that improving the droplet distribution uniformity in the lower layer of wheat could significantly improve the control effect of aphids. This study result could provide reference for the same type of crop protection UAV to control the same type of wheat diseases and insect pests in the same growing stage.

Frost poses a significant threat to agricultural production, leading to reduced crop yields and deterioration in quality. This review systematically provides an overview of the types and causes of plant frost, and delves into the principles, research progress, and application status of three key active frost protection (FP) technologies: air disturbance, sprinkler irrigation, and heating. It also scrutinizes the challenges faced by current FP equipment, such as high costs, complex maintenance, and noise pollution. Air disturbance technology utilizes fans to mix upper and lower air layers, increasing the canopy temperature, with research focusing on fan optimization and unmanned aerial vehicle (UAV) application. Sprinkler irrigation technology releases latent heat through water freezing, with research centering on water saving and automation. Heating technology directly supplies heat, with attention on heat source optimization and mobile heating strategies. Finally, this review outlines the development trends of plant FP equipment and technologies, highlighting the promising application prospects of agricultural UAVs in FP, which can have multi-purpose use and effectively reduce costs.

When endeavoring to study the complex growth conditions of soybean plants under natural conditions, a problem arises due to the similar appearances of both soybean plants and weeds. To address this issue, a soybean plant recognition model based on a laser ranging sensor is proposed. To demonstrate the applicability of the soybean plant recognition model, experiments are conducted using ultrasonic sensors and laser ranging sensors to analyze the diameter, height, and spacing conditions in the model. A test environment is built, and during the pre-test, the laser range sensor detects objects with diameters of 3 mm and 5 mm with two and three measurement points, respectively, at a speed of 0.2 m/s. At a speed of 0.3 m/s, there is one measurement point for objects with 3 mm diameter and two measurement points for objects with 5 mm diameter. At 0.4 m/s, there are also one and two measurement points for objects with diameters of 3 mm and 5 mm, respectively. These results demonstrate that the laser range sensor can more accurately recognize the diameter conditions of soybean plants and weeds and can distinguish between the diameters of soybean plants and weeds. Subsequently, the recognition rate of the model is evaluated by observing whether the weeding mechanism can synchronize seedling avoidance after the soybean plant passes through the sensor. The recognition rates of the optimized model at speeds of 0.2 m/s, 0.3 m/s, and 0.4 m/s are 100%, 98.75%, and 93.75%, respectively. Upon comprehensive analysis, the soybean plant recognition model is determined to achieve a recognition rate of 98.75% at a speed of 0.3 m/s, which is considered a moderate speed, and demonstrates more stable recognition of plant diameters. The test further verifies the reliability and effectiveness of the method for distinguishing between soybean plants and weeds. The research results can serve as a reference for recognizing soybean plants based on the use of laser ranging sensors.