Straw mulching in conservation tillage farmland can effectively promote land utilization and conservation. However, in this farming mode, surface straw suppresses weed growth, affecting weed size and position distribution and obscuring the weeds, which hampers effective weed management in the field. Accurate weed identification and localization, along with efficient herbicide application, are crucial for achieving precise, efficient, and intelligent precision agriculture. To address these challenges, this study proposes a weed detection model for a targeted spraying system. Firstly, we collected the dataset of weeds in a straw-covered environment. Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. Thirdly, we designed a targeted spraying system by combining the proposed model with the targeted spraying device. Through model test and spraying experiments, the results demonstrated that while the model exhibited a 0.9% decrease in average detection accuracy for weeds, it achieved an 8.46% increase in detection speed, with model memory and computational load reduced by 50.36% and 53.16%, respectively. In the spraying experiments, the proposed method achieved a weed identification accuracy of 90%, a target localization error within 4%, an effective spraying rate of 96.3%, a missed spraying rate of 13.3%, and an erroneous spraying rate of 3.7%. These results confirm the robustness of the model and the feasibility of the targeted spraying method. This approach also promotes the application of deep learning algorithms in precision weed management within directional spraying systems.

To avoid the issues of undesired soil compaction and seeding depth variation caused by the downforce fluctuation of the corn no-till planter, the influence of the structural parameters of the air spring on the downforce was researched in this paper, by establishing the gas–solid coupling simulation model of the air spring. The downforce test bench was built to verify the simulation model; the test showed that the vertical output force error of the simulation model was 4.79%, the pitch diameter error was 0.76%, and the pressure error was 5.07%. The cord angle, piston angle and piston diameter were used as influencing factors to carry out single-factor experiments. The influences of structural parameters on downforce were analyzed from four aspects: the vertical output force, the vertical stiffness, the pressure difference and the deformation rate. The results showed that the cord angle reduced the effective area and its change rate during deformation by limiting the radial deformation of the bellow. When the cord angles were 30°, 45° and 60°, the deformation rates were 65.6%, 20.3% and 4.8%, respectively. The cord angle had a positive effect on the vertical output force when the cord angle was in the range of 30~56°, and it had a negative impact in the range of 56~60°. As the cord angle increased, the vertical stiffness decreased. As the piston angle increased, the effective area of the air spring decreased, and the change in internal pressure decreased, reducing its vertical output force and stiffness. The piston diameter had little effect on the internal pressure and deformation rate. It increased the vertical output force and stiffness by increasing the effective area. The structural parameters of the air spring had a significant impact on the stability of the downforce; the structure of the air spring should be optimized according to the downforce demand of the corn no-till planter.

At present, liquid starter fertilizer (LSF) application technologies experience problems with low fertilizer utilization efficiency. In this study, we adopted a method of precise application of LSF near the seeds on seed bed in point form during sowing. A precise application system that can detect seed information in real time and control the solenoid valve to open automatically was developed for this method. The LSF supply system and detection control system were studied in detail. Field experiments were conducted to evaluate the performance of the precise application system in terms of operation quality (qualified index of the length of the LSF, QIL; the amount of the LSF, FA; and qualified index of the distance between the seeds and the LSF, QID) at forward speeds of 4, 6, and 8 km/h and pressures of 0.10, 0.15, 0.20, 0.25, and 0.30 MPa. The results indicated that QIL was 96.4%, the range of FA was 1.34 to 13.86 mL, and QID was 82.6%, which signifies the developed system meets the demands of precise LSF application. This method achieves the target of improving fertilizer use efficiency and provides a reference for developing fertilization devices for precisely applying LSF.

Design and experiment of pneumatic pressure control device for no-till planter