Background: Unmanned Aerial Vehicles (UAVs) are increasingly being used commercially for crop protection in East Asia as a new type of equipment for pesticide applications, which is receiving more and more attention worldwide. A new model of pear cultivation called the ‘Double Primary Branches Along the Row Flat Type’ standard trellised pear orchards (FT orchard) is widely used in China, Japan, Korea, and other Asian countries because it saves manpower and is suitable for mechanization compared to traditional spindle and open-center cultivation. The disease and pest efficacy of the flat-type trellised canopy structure of this cultivation is a great challenge. Therefore, a UAV spraying trial was conducted in an FT orchard, and a four-factor (SV: Spray application volume rate, FS: Flight speed, FH: Flight height, FD: Flight direction) and 3-level orthogonal test were designed. Results: These data were used to analyze the effect, including spray coverage, deposit density, coefficient of variation, and penetration coefficient on the canopy, to determine the optimal operating parameters of the UAV for pest efficacy in FT orchards. The analysis of extremes of variance showed that factor FD had a significant effect on both spray coverage and deposition density. Followed by factor FS, which had a greater effect on spray coverage (p < 0.05), and factor SV, FH, which had a greater effect on deposition density (p < 0.05). The effects of different factors on spray coverage and deposit density were FD > FS > FH > SV, FD > FH > SV > FS, in that order. The SV3-FS1-FH1-FD3, which flight along the row with an application rate of 90 L/ha, a flight speed of 1.5 m/s, and a flight height of 4.5 m, was the optimal combination, which produced the highest deposit density and spray coverage. It was determined through univariate analysis of all experimental groups, using droplet density of 25/cm² and spray coverage of 1%, and uniformity of 40% as the measurement index, that T4 and T8 performed the best and could meet the control requirements in different horizontal and vertical directions of the pear canopy. The parameters were as follows: flight along the tree rows, application rate not less than 75 L/ha, flight speed no more than 2 m/s, and flight height not higher than 5 m. Conclusion: This article provides ample data to promote innovation in the use of UAVs for crop protection programs in pergola/vertical trellis system orchards such as FT orchards. At the same time, this project provided a comprehensive analysis of canopy deposition methods and associated recommendations for UAV development and applications.

Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system “perception-decision-control,” the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

Efficient utilization is a pre-requisite for pesticide reduction, and appropriate airflow distribution pattern plays a key role in enhancing the effectiveness of pesticide application by air-assisted orchard sprayers, yet the mechanism of this is unclear. In order to clarify the specific effects of airflow velocity and direction on spraying efficacy, a series of spray tests on pear and cherry and airflow distribution tests in open areas were conducted by a multi-unit air-assisted sprayer on ten different fan settings. Several deposit indicators were analyzed and contrasted with the air distribution. The results showed that an increase in airflow velocity inside the canopy improved the abaxial side deposit coverage of both pear (from 3.33% to 11.80% in the Top canopy and from 6.26% to 11.00% in the Upper canopy) and cherry leaves (from 3.61% to 10.87% in the Top canopy, from 1.36% to 9.04% in the Middle canopy, and from 3.40% to 9.04% in the Bottom canopy), but had no significant effect on the spray penetration. The correlation between deposit indicators and airflow velocities/directions was evaluated, and the results indicated that the enhanced airflow velocities, both in the forward and horizontal direction, improved the abaxial side deposit coverage (CAB) on the outside of pear canopy (p < 0.001), but for cherry, none of the airflow indicators had a significant impact on the CAB independently. On the other hand, the increased airflow direction angle in the cross-row plane for pear, as well as the increased airflow velocities in forward and vertical direction for cherry, both showed negative effects on the adaxial side deposit coverage (p < 0.01). The findings in this study might be helpful to improve the performance of pesticide application in orchards, especially for abaxial side deposition, and could provide a reference for the further investigations about the effect of airflow on spray canopy deposition.

Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on.

The agronomic processes are complex in rice production. The mechanization efficiency is low in seeding, fertilization, and pesticide application, which is labor-intensive and time-consuming. Currently, many kinds of research focus on the single operation of UAVs on rice, but there is a paucity of comprehensive applications for the whole process of seeding, fertilization, and pesticide application. Based on the previous research synthetically, a multifunctional unmanned aerial vehicle (mUAV) was designed for rice planting management based on the intelligent operation platform, which realized three functions of seeding, fertilizer spreading, and pesticide application on the same flight platform. Computational fluid dynamics (CFD) simulations were used for machine design. Field trials were used to measure operating parameters. Finally, a comparative experimental analysis of the whole process was conducted by comparing the cultivation patterns of mUAV seeding (T1) with mechanical rice direct seeder (T2), and mechanical rice transplanter (T3). The comprehensive benefit of different rice management processes was evaluated. The results showed that the downwash wind field of the mUAV fluctuated widely from 0 to 1.5 m, with the spreading height of 2.5 m, and the pesticide application height of 3 m, which meet the operational requirements. There was no significant difference in yield between T1, T2, and T3 test areas, while the differences in operational efficiency and input labor costs were large. In the sowing stage, T1 had obvious advantages since the working efficiency was 2.2 times higher than T2, and the labor cost was reduced by 68.5%. The advantages were more obvious compared to T3, the working efficiency was 4 times higher than in T3, and the labor cost was reduced by 82.5%. During the pesticide application, T1 still had an advantage, but it was not a significant increase in advantage relative to the seeding stage, in which operating efficiency increased by 1.3 times and labor costs were reduced by 25%. However, the fertilization of T1 was not advantageous due to load and other limitations. Compared to T2 and T3, operational efficiency was reduced by 80% and labor costs increased by 14.3%. It is hoped that this research will provide new equipment for rice cultivation patterns in different environments, while improving rice mechanization, reducing labor inputs, and lowering costs.