Addressing the challenges of complex structure, limited modularization capability, and insufficient responsiveness in traditional hydraulically driven inter-plant mechanical weeding equipment, this study designed and developed an electric swing-type opening and closing intra-row weeding control system. The system integrates deep learning technology for accurate identification and localization of cabbage, enabling precise control and dynamic obstacle avoidance for the weeding knives. The system’s performance was comprehensively evaluated through laboratory and field experiments. Laboratory experiments demonstrated that, under conditions of low speed and large plant spacing, the system achieved a weeding accuracy of 96.67%, with a minimum crop injury rate of 0.83%. However, as the operational speed increased, the weeding accuracy decreased while the crop injury rate increased. Two-way ANOVA results indicated that operational speed significantly affected both weeding accuracy and crop injury rate, whereas plant spacing had a significant effect on weeding accuracy but no significant effect on crop injury rate. Field experiment results further confirmed that the system maintained high weeding accuracy and crop protection under varying speed conditions. At a low speed of 0.1 m/s, the weeding accuracy was 96.00%, with a crop injury rate of 1.57%. However, as the speed increased to 0.5 m/s, the weeding accuracy dropped to 81.79%, while the crop injury rate rose to 5.49%. These experimental results verified the system’s adaptability and reliability in complex field environments, providing technical support for the adoption of intelligent mechanical weeding systems. Future research will focus on optimizing control algorithms and feedback mechanisms to enhance the system’s dynamic response capability and adaptability, thereby advancing the development of sustainable agriculture and precision field management.

Due to large line spacing and planting distances, the adoption of continuous and uniform pesticide spraying in vegetable farming can lead to pesticide waste, thus increasing cost and environmental pollution. In this paper, by applying deep learning and online identification methods, control technology for target-oriented spraying is studied with cabbages as the research object. To overcome motion blur and low average precision under strong light conditions during the operation of sprayers, an innovative YOLOV5 model implanted with a transformer module is utilized to achieve accurate online identification for cabbage fields under complex environments. Based on this concept, a new target-oriented spray system is built on an NVIDIA Jetson Xavier NX. Indoor test results show that the average precision is 96.14% and the image processing time is 51.07 ms. When motion blur occurs, the average precision for the target is 90.31%. Then, in a field experiment, when the light intensity is within the range of 3.76–12.34 wlx, the advance opening distance is less than 3.51 cm, the delay closing distance is less than 2.05 cm, and the average identification error for the cabbage diameter is less than 1.45 cm. The experimental results indicate that changes in light intensity have no significant impact on the identification effect. The average precision is 98.65%, and the savings rate reaches 54.04%. In general, the target-oriented spray system designed in this study achieves the expected experimental results and can provide technical support for field target spraying.

To address the problem of herbicide residues exceeding the safety standard due to continuous spraying of herbicides on open-field cabbage, we propose an intermittent weed spraying control method integrating cabbage position, cabbage canopy size, and spraying machine operation speed. It is based on an early-stage cabbage target identification method obtained in the early stage and the operation requirements in open-field cabbage. Built with a C37 controller, a stable pressure spray system and an intermittent weed spraying control system for open-field cabbage, an integrated system was designed. Experimental verification was carried out through measurement indexes such as spraying precision, herbicide saving rate, herbicide efficacy, and herbicide residue. Since the industry is faced with a status quo of a lack of relevant operational norms and national standards for the precise weed spraying operation mode, this paper provides a relatively perfect experiment and evaluation method for this mode. The experimental results on the accuracy of weed spraying at different speeds showed that the mean absolute error (MAE), root mean square error (RMSE), and average spray cabbage coverage rate (ASCCR) of intermittent weed spraying increased, but the average effective spray coverage rate (AESCR) decreased with increasing operation speed. When the working speed was 0.51 m/s, the MAE and RMSE of intermittent weed spraying were less than 2.87 cm and 3.40 cm, respectively, and the AESCR was 98.4%, which verified the feasibility of operating the intermittent weed spraying of cabbage. The results of a field experiment showed that the average weed-killing rate of intermittent weed spraying for open-field cabbage was 94.8%, and the herbicide-saving rate could reach 28.3% for a similar weeding effect to that of constant-rate application, which not only met the needs of intermittent weed spraying in open-field cabbage but also had great significance for improving the herbicide utilization rate. Compared with the constant-rate application method, the herbicide residue concentration detected using intermittent weed spraying for cabbage decreased by 66.6% on average, which has important research significance and application value for ensuring the normal growth of crops and the safety of agricultural products.