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.

Conventional continuous pesticide application remains prevalent in agriculture, but its limitations in addressing the spatial–temporal variability of biotic stressors have led to excessive chemical inputs and inefficiency. The emergence of precision agriculture has catalyzed significant advancements in variable-rate spray systems to optimize agrochemical deployment through real-time modulation. This technology demonstrates critical advantages in minimizing the environmental footprint while maintaining crop protection efficacy. Our systematic review analyzes three foundational variable-rate spray architectures—pressure-regulated, flow rate-regulated, and pesticide concentration-regulated mechanisms—evaluating their maturity and implementation paradigms. Pressure-regulated technology relies on the pressure–flow relationship to achieve regulation, but there is a narrow range in flow regulation, atomization stability is insufficient, and there are other defects. Flow rate-regulated technology achieves precise control through the dynamic adjustment of the nozzle orifice area or Pulse-Width Modulation duty cycles, but this technology faces mechanical wear, a nonlinear flow–duty cycle relationship, and other challenges. Pesticide concentration-regulated technology is centered on real-time mixing, which can avoid the residue of chemicals but is highly dependent on fluid characteristics and mixing efficiency. This study proposes improvement paths from the perspectives of hardware optimization, control strategy integration, and material innovation. Through the summary and analysis of this paper, we hope to provide valuable references for future research on variable-rate spray technology.

Unmanned aerial vehicles (UAVs) are widely used in field pesticide spray operations due to their wide applicability and high operational efficiency. However, their high spray height and fine pesticide droplets lead to a greater risk of drift and likely different droplet deposition outcomes compared to the expectation. So far, most of the previous studies have used direct field methods on UAVs’ droplet deposition characteristics and there have been few carried out in wind tunnels. Thus, in this paper, a simulated UAV platform equipped with TeeJet 80-015 VP fan nozzles was utilized to study the droplet deposition characteristics in a wind tunnel. The droplet deposition amount and drift potential reduction percentage (DPRP) under different spray parameters were obtained. The results showed that when the rotor was open, the deposition amount in the target area increased by 2.6 times and the drift deposition amount decreased by 7.3 times when spraying tap water at 3 m/s wind speed and 3 bar pressure. Faster wind speeds led to greater drift deposition amounts and a lower DPRP, but higher pressures resulted in greater drift deposition amounts and a larger DPRP. The 30 g/L PEG-20000 solution has a higher droplet size and smaller relative droplet spectrum width R_S, resulting in the deposition amount in the target area increasing by 9.13% on average and the drift amount decreasing by 24.7% on average, and it can be used as an anti-drift additive when needed. The research results can provide reference and technical support for UAV wind tunnel tests and field operation specifications.

The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings.

The traditional continuous, quantitative spraying technology ignores the severity of pests, diseases and grasses, spatial distribution and other differences, resulting in low effective utilization of pesticides, environmental pollution and other problems. Variable-rate spray technology has become an important development direction in the field of precision agriculture by dynamically sensing crop canopy morphology, pest and disease distribution, and environmental parameters, adjusting the application amount in real time, and significantly improving pesticide utilization. In this study, we systematically review the core progress of variable-rate spray technology; focus on the technical system of information detection, spray volume model, and control system; analyze the current bottlenecks; and propose an optimization path to adapt to the complex agricultural conditions. At the level of information perception, LiDAR, machine vision, and multi-source sensor fusion technology constitute the main perception architecture, and infrared and ultrasonic sensors assist target recognition in complex scenes. In the construction of the spray volume model, models based on canopy volume, leaf area density, etc., are used to realize dynamic application decision by fusing equipment operating parameters, pest and disease levels, meteorological conditions, and so on. The control system takes the solenoid valve + PID control as the core program, and improves the response speed through PWM regulation and closed-loop feedback. The current technical bottlenecks are mainly concentrated in the sensor dynamic detection accuracy, model environmental adaptability, and the reliability of the execution parts. In the future, it is necessary to further promote anti-jamming multi-source heterogeneous sensor data fusion, multi-factor adaptive spray model development, lightweight edge computing deployment, and solenoid valve structural parameter optimization and other technical research, with a view to promoting the application of variable-rate spray technology to the field on a large scale and providing a theoretical reference and technological support for the green transformation of agriculture.

Under soybean–corn intercropping in China, quizalofop-p-ethyl is recommended as a herbicide for stem and leaf treatment after soybean seedling. Nonetheless, herbicide drift during spraying may lead to environmental contamination and damage to the corn plants. In order to clearly show the threshold of the drift deposition amount of quizalofop-p-ethyl that causes herbicide damage to corn, we used a bioassay spray tower to spray quizalofop-p-ethyl herbicide on corn in the laboratory and a boom sprayer to spray quizalofop-p-ethyl herbicide, which drifts to corn in the field, to study and evaluate the damage quizalofop-p-ethyl herbicide causes to corn under different spray volumes and drift deposition rates. The results showed that under a drift deposition rate of 1% of three spray volumes, the corn showed no symptoms of herbicide damage and their plant height was not inhibited 14 days after spray; under a spray volume of 150 L/ha and a drift deposition rate of 5%, the corn showed symptoms of mild herbicide damage but their plant height was not inhibited 14 days after spray, while the corn showed symptoms of moderate herbicide damage and their plant height was slightly and moderately inhibited, respectively, under the spray volumes of 300 L/ha and 450 L/ha; under drift deposition rates of 10% and 30% of three spray volumes, half or more of the corn in each treatment withered and their plant height was severely inhibited or completely inhibited. Under the same spray volume, the symptoms of herbicide damage and the inhibition rate of plant height increased with the increase in the drift deposition rate; under the same drift deposition amount, the symptoms of herbicide damage and the inhibition rate of plant height increased with a decrease in the spray volume. The effect of the drift deposition rate on the symptoms of herbicide damage and plant height was extremely significant, but the spray volume was not significant. The drift deposition rates for 10% inhibition and no inhibition of corn plant height were 5.70% (R₁₀) and 5.05% (R₀) under spray volume of 150 L/ha, 4.56% (R₁₀) and 1.23% (R₀) under 300 L/ha, and 3.31% (R₁₀) and 1.86% (R₀) under 450 L/ha, respectively. When the herbicide was sprayed in the field using a soybean–corn-dedicated plant protection machine under the spray volume of 450 L/ha, the drift deposition rate ranged from 1.22% to 1.69%, and the corn did not produce symptoms of herbicide damage and plant height was not inhibited 14 days after the spray. In actual weeding operations, it is better to ensure that the drift deposition rate of quizalofop-p-ethyl is below R₀ by setting reasonable operational parameters, using anti-drift nozzles or additives, and so on, and, at most, not more than R₁₀. This study clarified the drift hazard of quizalofop-p-ethyl herbicide on corn and the safety value of the herbicide drift deposition amount, which provided data support for the standardized use of quizalofop-p-ethyl herbicide under soybean–corn intercropping and guidance for the safe production of field corn.