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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%.

Current aerial plant protection with Unmanned Aerial Vehicles (UAV) usually applies full coverage route planning, which is challenging for plant protection operations in the orchards in South China. Because the fruit planting has the characteristics of dispersal and irregularity, full-coverage route spraying causes re-application as well as missed application, resulting in environmental pollution. Therefore, it is of great significance to plan an efficient, low-consumption and accurate plant protection route considering the flight characteristics of UAVs and orchard planting characteristics. This study proposes a plant protection route planning algorithm to solve the waypoint planning problem of UAV multi-objective tasks in orchard scenes. By improving the heuristic function in Ant Colony Optimization (ACO), the algorithm combines corner cost and distance cost for multi-objective node optimization. At the same time, a sorting optimization mechanism was introduced to speed up the iteration speed of the algorithm and avoid the influence of inferior paths on the optimal results. Finally, Multi-source Ant Colony Optimization (MS-ACO) was proposed after cleaning the nodes of the solution path. The simulation results of the three test fields show that compared with ACO, the path length optimization rate of MS-ACO are 3.89%, 4.6% and 2.86%, respectively, the optimization rate of total path angles are 21.94%, 45.06% and 55.94%, respectively, and the optimization rate of node numbers are 61.05%, 74.84% and 75.47%, respectively. MS-ACO can effectively reduce the corner cost and the number of nodes. The results of field experiments show that for each test field, MS-ACO has a significant optimization effect compared with ACO, with an optimization rate of energy consumption per meter of more than 30%, the optimization rate of flight time are 46.67%, 56% and 59.01%, respectively, and the optimization rate of corner angle are 50.76%, 61.78% and 71.1%, respectively. The feasibility and effectiveness of the algorithm were further verified. The algorithm proposed in this study can optimize the spraying path according to the position of each fruit tree and the flight characteristics of UAV, effectively reduce the energy consumption of UAV flight, improve the operating efficiency, and provide technical reference for the waypoint planning of plant protection UAV in the orchard scene.

Pesticide dose model based on canopy characteristics is the guidance basis for spray parameters adjustment. In tliis study, the calculation formula and canopy deposition characteristics of leaf wall area (LWA) model, tree row volume (TRV) model, and optimal coverage method (OCM) model were described and compared. A tower air-assisted spray test bench was applied to provide fine quality droplets, suitable wind speed and demand spray flow rate for corresponding models, an electric flat board vehicle was applied to drive tree in a straight line to simulate the sprayer movement speed, and droplet deposition distribution were tested in different leaf area density canopy. The results showed that the spray flow rates of three pesticide dose models decreased gradually. LWA model was only related to canopy height, TRV model was related to canopy height and canopy diameter, while OCM model was related to canopy height, canopy diameter and leaf area density. Whether dense or sparse canopy, TRV model basically satisfied the requirement of coverage rate greater than 33% in the entire canopy, OCM model met the requirement of coverage density greater than 70 droplets/cm2. However, LWA model, for dense canopy, unit area deposition of outermost leaves near sprayer was 3.6 times of the apple leaf maximum retention, which had a high loss risk; for sparse canopy, penetration rates of outermost leaves far away sprayer, that is, the drift rate was 21.4%. The discussion leads to the conclusion that for conventional spraying, TRV model represented a substantial improvement compared to LWA model, and OCM model was a reasonable low volume spraying model. This study provides a reference to different growth seasons spray amount adjustments in orchard.

Olive (Olea europaea L.) is one of the first domesticated and cultivated tree species and has historical, social and economical relevance. However, its future as a strategic commodity in Mediterranean agriculture is threatened by diverse biotic (traditional and new/emerging pests and diseases) and abiotic (erosion, climate change) menaces. These problems could also be of relevance for new geographical areas where olive cultivation is not traditional but is increasingly spreading (i.e., South America, Australia, etc). One of the major constraints for olive cultivation is Verticillium wilt, a vascular disease caused by the soil-borne fungus Verticillium dahliae Kleb. In this review we describe how Verticillium wilt of olive (VWO) has become a major problem for olive cultivation during the last two decades. Similar to other vascular diseases, VWO is difficult to manage and single control measure are mostly ineffective. Therefore, an integrated disease management strategy that fits modern sustainable agriculture criteria must be implemented. Multidisciplinary research efforts and advances to understand this pathosystem and to develop appropriate control measures are summarized. The main conclusion is that a holistic approach is the best strategy to effectively control VWO, integrating biological, chemical, physical, and cultural approaches.

Extensive research has been conducted on plant protection unmanned aerial vehicle (UAV) chemical application technology in recent years owing to its importance as a means of pest and disease control. UAV spraying in orchards faces the drawback of drift risk and can be hazardous to non-targeted crops, humans, and the environment. A detailed and systematic analysis must be performed to determine the uniformity and drift risk of plant UAV sprays. In this study, a peach orchard is sprayed with a plant-protection UAV at three different flight velocities and we evaluate the combined pesticide deposition performance of the canopy, ground loss, downwind ground drift, and airborne drift. Additionally, the droplet size and coverage rate in the canopy are calculated by using water-sensitive paper. The results demonstrate that there is significant difference in the droplet size at flight velocities of 1–3 m/s. The droplet size in the lower canopy is slightly smaller than those in the middle and upper parts. Increasing the flight velocity helps the pesticide droplets to spread and penetrate the canopy. However, it also causes a non-uniform pesticide deposition, reduced effective coverage ratio and effective density ratio. Among the three pesticides used in the experiment, imidacloprid exhibits the best deposition efficiency. The deposition amount and normalized deposition amount in the canopy were the highest at a flight velocity of 2 m/s, accompanied by a lower ground loss under the canopy. The highest near-field ground drift is observed at a velocity of 1 m/s, and the far-field airborne drift is highest at 3 m/s. Lastly, this study provides a reference for the commercial application of plant-protection UAVs.

For orchard plant protection, conventional large machines and small sprayers are practically restricted by either narrow planting intervals with dense leaves or their inadequate penetration power, which leads to an unsatisfactory effect of spray. This paper proposes a stereoscopic plant-protection strategy that integrates unmanned air and ground sprayers to spray different parts of canopies to improve uniformity. In order to verify the proposal, a stereoscopic plant-protection system (SPS) was developed, consisting of a small swing-arm sprayer and a T16 plant-protection Unmanned Aerial Vehicle (UAV). Then, optimal operation parameters were determined by Computational Fluid Dynamics (CFD) and orthogonal experiments, and the uniformity was finally quantified by trials. CFD and orthogonal experiments showed that a swing-arm angle of 60° and a forward speed of 0.4 m/s were optimal for the ground sprayer, whilst a height of 2.0 m from the top of canopies and a forward speed of 1.0 m/s were appropriate for the UAV. The trial results showed that the density of vertical droplet deposition varied from 90 to 107 deposits/cm 2 in canopies, and the uniformity was 38.3% higher than conventional approaches. The uniformity of top, bottom, inside and outside canopies was significantly improved. Meanwhile, the density of droplet deposition on both sides of leaves in all test points exceeded 25 deposits/cm 2 , able to meet the standard of spray. This study provides a practical approach for uniform pesticide spray to large-canopy fruit trees. Moreover, the high flexibility of plant-protection UAVs and the significant trafficability of small swing-arm sprayers can solve the problem of large machine entering and leaving orchards.

Plant protection drone spraying technology is widely used to prevent and control crop diseases and pests due to its advantages of being unaffected by crop growth patterns and terrain restrictions, high operational efficiency, and low labor requirements. The operational parameters of plant protection drones significantly impact the distribution of spray droplets, thereby affecting pesticide utilization. In this study, a field experiment was conducted to determine the working modes of two representative plant protection drones and an electric backpack sprayer as a control to explore the characteristics of droplet deposition with different spray volumes in the citrus canopy. The results showed that the spraying volume significantly affected the number of droplets and the spray coverage. The number of droplets and the spray coverage area on the leaf surface were significantly increased by increasing the spray volume from 60 L/ha to 120 L/ha in plant protection drones. Particularly for the DJI T30, the mid-lower canopy showed a spray coverage increase of 52.5%. The droplet density demonstrated the most significant variations in the lower inner canopy, ranging from 18.7 droplets/cm 2 to 41.7 droplets/cm 2 by XAG V40. From the deposition distribution on fruit trees, the plant protection drones exhibit good penetration ability, as the droplets can achieve a relatively even distribution in different canopy layers of citrus trees. The droplet distribution uniformity inside the canopy is similar for XAG V40 and DJI T30, with a variation coefficient of approximately 50%-100%. Compared to the plant protection drones, the knapsack electric sprayer is suitable for pest and disease control in the mid-lower canopy, but they face challenges of insufficient deposition capability in the upper canopy and overall poor spray uniformity. The distribution of deposition determined in this study provides data support for the selection of spraying agents for fruit trees by plant protection drones and for the control of different pests and diseases.

The intelligent pesticide application techniques in orchards have grown rapidly worldwide due to the decrease in agricultural populations and the increase in labor costs. However, whether and how intelligent pesticide application techniques are better than conventional pesticide application remains unclear. Here, we evaluated the performance of the unmanned aircraft vehicle (UAV) and unmanned ground vehicle (UGV) on pesticide application, ecological environment protection, and human’s health protection compared to conventional manual methods. We quantified characteristics from the aspects of working effectiveness, efficiency, environmental pollution, water saving and carbon dioxide reduction. The results showed that the UAV application has the advantages of a higher working efficiency and less environmental pollution and natural resource consumption compared to the UGV and conventional manual methods despite of its worse spray performance The UGV application techniques could improve spray performance at the cost of high environmental pollution. The conventional spray gun technique was unfriendly to environmental and resource protection although it showed a better spray performance. Thus, the balance of improving spray performance and controlling environmental pollution is the key to improve the performance of UAV and UGV technology in the future. The study could be useful in the development of intelligent pesticide application techniques and provide scientific support for the transition of intelligent management in orchards.

The present review provides a perspective angle on the historical and cutting-edge strategies of remote sensing techniques and its applications, especially for insect pest and plant disease management. Remote sensing depends on measuring, recording, and processing the electromagnetic radiation reflected and emitted from the ground target. Remote sensing applications depend on the spectral behavior of living organisms. Today, remote sensing is used as an effective tool for the detection, forecasting, and management of insect pests and plant diseases on different fruit orchards and crops. The main objectives of these applications were to collate data that help in decision-making for insect pest management and decreasing the environmental pollution of chemical pesticides. Airborne remote sensing has been a promising and useful tool for insect pest management and weed detection. Furthermore, remote sensing using satellite information proved to be a promising tool in forecasting and monitoring the distribution of locust species. It has also been used to help farmers in the early detection of mite infestation in cotton fields using multi-spectral systems, which depend on color changes in canopy semblance over time. Remote sensing can provide fast and accurate forecasting of targeted insect pests and subsequently minimizing pest damage and the management costs.

In orchard plant protection application, an anti-drift strategy can effectively reduce drift in the non-target area, reduce spray drift in the environment, and avoid spray leakage and overspraying. To clarify the future development direction of orchard plant protection mechanization technology, this review introduces the development status of an anti-drift spray nozzle and the impact of different types of spray nozzles on the potential of drift, and then, it analyzes the research progress on air-assisted spraying, recycling spraying, profiling spraying, target variable spraying technologies, and plant protection UAVs. It also provides a general analysis of the above spraying technologies on the amount of drift and the impact of pesticide deposition. Finally, combined with the characteristics of orchard plant protection, the paper presents the research and development of anti-drift nozzles, pesticide adjuvant, air-assisted spraying technology, electrostatic, recycling spraying technology, profiling and target variable spraying technology, and plant protection UAVs. The review provides a reference for the development of an anti-drift strategy for orchard plant protection production.