Due to its ability to broaden the transport channel of droplets within the plant canopy and enhance their penetration capacity, air-assisted spray technology is widely used in orchard pesticide application. To achieve uniform distribution of pesticide droplets in the tree canopy and obtain a higher pesticide utilization rate, it is crucial to clarify the coupling mechanism of the airflow field and droplet field generated by the air-assisted sprayer. This paper introduces a three-dimensional modeling method of the fruit tree canopy based on CFD (Computational Fluid Dynamics), offering a theoretical basis for analyzing the airflow demand calculation during different growth periods of the canopy. It also examines the interaction between canopy modeling and airflow, highlighting advancements in airflow regulation equipment and the effects of airflow speed and volume on spraying. The study shows that the precise regulation of airflow velocity and discharge rate is of importance for improving spraying efficiency. It finally points out that future research should focus on developing intelligent regulation equipment for efficient airflow-droplet control, using biomass sensing, which involves measuring the growth characteristics of the tree canopy, to meet the needs of orchards with diverse growth stages and canopy structures. This article could provide guidance for the future study of precision air-assisted spraying technology in orchards.

To solve the problem of effective utilization of orchard green fertilizer, a small one-rotor orchard horizontal rotary rake (OHRR) was developed, which is used for grass collection in the mowing agronomic section of orchard management. In the working process, the disc drives the arms rotating around the vertical shaft, the guide cam that is fixed to the vertical shaft controls tine movement, and the tines turn to different inclination angles in different positions. The kinematic validation model of OHRR was built based on the theory that there is no gap or small overlap between the two adjacent working areas of the tines. This model determines the relationship between the advancing speed, disc rotational speed, rotation radius, arm number, and tine working width. The leakage and repeating rate of OHRR virtual prototype were calculated by tines movement trajectories analysis in multi-body dynamics simulation. Box-Behnken three-factor and three-level test plans for advancing speed, disc rotational speed, and tine working width were designed to obtain the optimal operation parameters of the OHRR: advancing speed was 11.16 km/h, disc rotational speed was 6.98 rad/s, and tines working width was 0.3 m. Taking labor working as the control group, OHRR field tests were evaluated by four indices: strip density, leakage rate, working efficiency, and profitable area. Field tests results showed that the leakage rate of OHRR was 4.56%, which meets the requirements of national standard JB/T10905. The strip density, width, and height of OHRR were 29.44 kg/m³, 0.5 m, and 0.25 m, respectively. These data can provide support for the subsequent loading and transportation operation. The profitable area of OHRR was 6.7 hm², which is suitable for large-scale mechanized orchard management.

With the rapid advancement of modern agriculture, mechanized and intelligent pollination has emerged as a crucial focus for enhancing agricultural efficiency and minimizing labor expenses. Traditional pollination methods, limited by environmental factors and high labor costs, fail to adequately address the production demands of large-scale orchards and vegetable gardens. Consequently, researchers have integrated mechanized equipment, drone technology, robotics, and deep learning algorithms to achieve accurate identification and precise pollination on inflorescences. The research on mechanized and intelligent pollination has not only injected new momentum into the field of fruit and vegetable pollination but also provided key technological support for addressing global agricultural labor shortages and increasing crop yields. This review summarizes recent advances in mechanized and intelligent pollination, focusing on deep learning’s role in object recognition, improvements in pollination equipment, and the effectiveness of intelligent pollination across various fruits or vegetables. Studies indicate that mechanized and intelligent pollination significantly enhances working efficiency and fruit yields, though it continues to face challenges such as technical complexity and high implementation costs. Looking ahead, as robotics and artificial intelligence algorithms continue to advance, mechanized and intelligent pollination is poised for broader adoption in agricultural management practices. This review systematically summarizes the research progress in mechanized and intelligent pollination technologies for fruit and vegetable crops, providing significant theoretical support and reference value for future studies in crop pollination techniques.