Pesticide application by unmanned agricultural aerial vehicles (UAVs) has rapidly developed in China and other Asian counties. Currently, tank-mix spray adjuvants are usually added into pesticide solutions to reduce spray drift and facilitate droplet deposition and control efficacy. The currently used tank-mix adjuvants are all derived from conventional ground sprays, and their mechanisms of action in aerial applications are still unclear. In order to clarify the spraying characteristics and control efficacy of those adjuvants in aerial sprays, the performances of various types of tank-mix adjuvants were compared by analyzing droplet spectrum, drift potential index (DIX) in a wind tunnel, field deposition and control efficacy on wheat rust and aphids. The atomization results showed that the addition of adjuvants could change the droplet spectrum of liquid, and the results suggest that droplet size is an effective indicator of spray drift potential. In the field application, the meteorological conditions are complex and uncontrollable, and the effects of adjuvants on droplet deposition and distribution were not significant. Compared with the control solution, there was no significant difference in the deposition amount of each adjuvant solution, and the CVs of deposition were higher than 30%. Adding adjuvants to the spray solution can significantly improve the control efficacy of pesticides on wheat aphids and rust and also prolong the duration of the pesticide. Our results suggest that tank-mix adjuvants should be added when UAVs are used for aerial application. This study can be used as a reference to the research and development or selection of adjuvants in aerial sprays of UAVs.

Optimizing the working performance of the cutting device for harvesting Chinese little greens is crucial to reducing energy consumption in cutting and improving cutting quality. To explore the mechanical characteristics of leafy vegetables in cutting, the dynamic process of cutting Chinese little greens with a cutter was simulated numerically by using the finite element method based on theoretical analysis. In the numerical simulation, the response-surface methodology (RSM) and central composite rotatable design (CCD) were used to describe the influence rule of sliding–cutting angle (X₁), oblique angle (X₂), and the average cutting speed (X₃) on cutting stress. Then, the stress distribution pattern produced by the cutting blade and the stalks were evaluated by using different working parameters. Subsequently, taking the minimum cutting stress as the target value, the best combination of cutter structure and working parameters were obtained: the sliding–cutting angle was 29°, the oblique angle was 38°, and the average cutting speed was 500 mm/s. At the condition of optimal parameter combinations, the ultimate cutting stress of the upper cutting blade was 0.95 Mpa and that of the bottom cutting blade was 0.77 Mpa. A cutting test was carried out by using a bench test of the cutting performance, and the mechanical properties of cutting at different cutting speeds were studied. Test results showed that at the optimal cutting speed of 500 mm/s, the cutting stress on the cutter was relatively small and the cutting effect reached the best value. The finite element simulation of cutting the little greens reduced the test cost and provided a reference for the development of a cutting device with low power consumption.

To address the challenges of obstacle avoidance and low efficiency by robotic arms during apple picking, this paper proposes an improved informed-RRT* motion planning algorithm to improve path planning performance. By integrating the artificial potential field method, the unique location points within the planning space are identified. Segmenting the path planning further accelerates the path searching efficiency. The algorithm includes target bias and cosine offset strategies, along with bidirectional planning to improve planning efficiency, enhancing the purposefulness of informed-RRT* sampling. Spatially expanding sampling by dynamic step size enhances the robustness of path indexing in complex obstacle environments. The algorithm is compared with RRT* and in-formation-RRT* in several scenarios. The experimental results show that compared to RRT* and IRRT*, the average path cost of the optimization algorithm is reduced by 31.565 mm and 14.935 mm and the average search time is reduced by 7.18 s and 4.33 s. In the complex two-dimensional simulation experiment, compared to RRT* and IRRT*, the average path cost of the optimization algorithm is reduced by 362.4 mm and 343.5 mm and the average search time is reduced by 5.49 s and 1.54 s. In the 3D simulation, compared to RRT* and IRRT*, the average path cost is reduced by 1110.17 mm and 469.97 mm and the average search time is reduced by 37.82 s and 11.26 s. The optimization algorithm effectively reduces the total length of the picking path and the path planning time. The research results provide a reference for apple picking robots to perform collision-free picking tasks.

Accurately extracting the canopies of fruit trees is crucial to improve the estimation accuracy of CNC inversion as well as determine a reasonable application of nitrogen fertilizer. To date, existing studies have mainly focused on canopy extraction in scenarios with no grass or sparse grass cover, paying less attention to scenarios with a full grass cover. Thus, in this paper, a two-stage canopy extraction (TCE) method was proposed to address the issue of canopy extraction in scenarios with full grass cover. Firstly, the height difference between the canopies of pear trees and the ground grass was used to eliminate the interference of the ground grass and achieve a coarse-grained canopy extraction. Then, based on the extracted coarse-grained canopies and CIELAB color space, the color thresholds of the L*, a*, and b* channels were determined to remove the interference factors, e.g., branches, shadows, and trellises, for fine-grained canopy extraction by using data distribution from the three channels based on a histogram and the threshold of confidence interval. In canopy extraction experiments, the accuracy, recall, precision, and F1-score of TCE in scenarios with full grass cover can reach 91.725%, 95.789%, 91.284%, and 93.482%, respectively, demonstrating the effectiveness of TCE in addressing canopy extraction issues in this scenario. Thirdly, the RF algorithm was utilized to select suitable VIs based on R² and RMSE values, and CNC inversion models were constructed. In estimation experiments on CNC inversion, the R², RMSE, and nRMSE of the constructed CNC inversion based on TCE in a scenario with full grass cover were 0.724, 0.243, and 19.120%, respectively. A comparative analysis with the baseline method revealed that accurate canopy extraction contributed to a high estimation accuracy of CNC inversion. Therefore, our proposed method can provide technical support for the efficient and non-destructive monitoring of the canopy nutrient status in pear orchards.