703 results about "Drone" patented technology

The invention provides a division method and system for a forest pest control area, and relates to the technical field of data processing, and the method comprises the steps: building a forest pest control database through collecting pest data and environment data of a control area; and training a pest risk prediction model based on historical data to generate a risk distribution map. And then, in combination with a GIS technology and a deep learning image segmentation algorithm, intelligent division is performed on the target area, and it is ensured that the unmanned aerial vehicle efficiently covers the prevention and control unit. According to the method, the optimal spraying route can be planned by adopting a path optimization algorithm based on the flight capability of the unmanned aerial vehicle, the pesticide carrying capacity and the environmental conditions, spraying parameters are dynamically adjusted by monitoring the wind speed, obstacle information and the like in real time, precise pesticide application is ensured, the pest control efficiency can be improved, repeated spraying and spraying blind areas are reduced, the pesticide use amount is reduced, and the pesticide application cost is reduced. Intelligent and precise forest pest control is achieved, and the method is suitable for large-scale unmanned aerial vehicle autonomous operation scenes.

The invention relates to an unmanned aerial vehicle scheduling management and control method and system based on a nest, and the method comprises the following steps: S1, segmenting an inspection region, and searching an optimal distribution scheme of unmanned aerial vehicles and the nest by using improved particle swarm optimization (PSO); s2, the scheduling control center receives the task request, decomposes the task into sub-tasks according to the task requirement, and allocates proper aircraft nests and unmanned aerial vehicles to process tasks according to the real-time states of the aircraft nests and the unmanned aerial vehicles; s3, calling a high-precision map and a path planning algorithm to plan a flight path; s4, the unmanned aerial vehicle receives the task instruction and the flight plan, and navigation and obstacle avoidance are autonomously completed through a GNSS and an airborne sensor; s5, the dispatching center monitors the state of the unmanned aerial vehicle in real time; and S6, after the unmanned aerial vehicle completes the task, selecting an optimal parking point according to the distribution and state of the aircraft nest. The unmanned aerial vehicle cluster efficiency, the dynamic response capability and the task execution reliability are remarkably improved.

A drone pod may receive, stow, and launch a drone. The drone pod may include a housing, a canopy movably coupled to the housing, a platform movably coupled to the housing, and a drone bay. The drone pod may be configured to move the canopy to expose the drone bay and raise the platform relative to a floor of the drone bay to receive the drone, lower the platform toward the floor of the drone bay and move the canopy to enclose the drone bay to stow the drone, move the canopy to expose the drone bay and raise the platform relative to the floor of the drone bay to launch the drone. The drone pod may be mounted on a vehicle. A system may include a drone pod and a drone, wherein the drone pod is configured to receive, stow, and launch the drone.

The invention discloses an unmanned aerial vehicle multi-mode mine inspection method and system, a medium and a program product, and the method comprises the steps: dividing an inspection target into a short-range object and a long-range object based on a preset inspection target classification list; constructing a three-dimensional grid inspection path according to the position information of the short-range object and the remote object, and setting a plurality of standby inspection entrance points in a detection area of the short-range object; acquiring real-time environment parameters, wherein the real-time environment parameters comprise illumination intensity, atmospheric visibility, electromagnetic field intensity and wind speed and wind direction; calculating the reachability score of each standby inspection entrance point based on the influence degree score of the real-time environment parameters on the unmanned aerial vehicle, and determining the standby inspection entrance point with the highest reachability score as the current inspection entrance point; and generating an inspection path according to the current inspection entrance point, and controlling the unmanned aerial vehicle to inspect according to the inspection path. According to the method, the integrity of the inspection data is improved, the accuracy of the safety evaluation data obtained based on the inspection data is improved, and the safety of a mine is further improved.

An autonomous agricultural drone system for the precise spraying of pesticides, consisting of: an advanced image recognition camera integrated with an artificial intelligence (AI) module specifically trained on pest and plant health datasets, with the camera configured to detect specific pests, identify pest hotspots, and differentiate between healthy and stressed plants; a real-time decision processor that is operationally connected to the image recognition camera, the processor being configured to: Data received from the image recognition camera is interpreted to assess pest density and plant health. adjusts the pesticide output and spraying patterns of the drone based on real-time field conditions, determines the severity of pest infestation in order to avoid excessive use of pesticides and to apply pesticides precisely and only where they are needed; a multitude of intelligent spray nozzles equipped with sensors and actuators, each nozzle configured to dynamically adjust its spray rate based on pest density and plant species, as determined by the real-time decision processor, thereby minimizing chemical use and environmental impact; characterized by the fact that The drone system includes a multi-liquid injection nozzle system that allows the drone to carry and selectively apply multiple solutions, including pesticides and nutrients, in a single flight, with the system configured to switch between solutions in response to real-time data from the image recognition camera and decision processor.

The invention discloses an unmanned aerial vehicle routing inspection path planning method considering risk levels, which comprises the following steps: 1) simulating an experimental map, and establishing a related mathematical model of a path planning algorithm; 2) using an improved BI-RRT * adaptive step length path planning algorithm to carry out primary measurement recording on the path distance between the inspection points of the unmanned aerial vehicle; 3) designing a grey wolf algorithm evaluation function, enabling the evaluation target to be comprehensive optimum of the inspection task point risk and the unmanned aerial vehicle inspection path cost, and enhancing the local optimum separation capability of the algorithm; and 4) carrying out secondary planning on the path according to the sequence to ensure that the path is optimal. According to the method, the routing inspection cost of the unmanned aerial vehicle is considered in the routing inspection process, and routing inspection points with high risk degrees are preferentially inspected; an IRRT *-Connect path planning algorithm based on environmental complexity is designed, and it is ensured that the unmanned aerial vehicle plans an optimal path with high efficiency when facing a complex and narrow area.

The invention discloses a multi-dimensional anti-bird intelligent recognition method and system based on thermal imaging, and relates to the technical field of intelligent monitoring and ecological protection. Multi-modal data is collected through thermal imaging, visible light, millimeter wave radar and a voiceprint sensor, after PTP protocol synchronization and Kalman filtering preprocessing, 3-5-second tracks of birds are predicted by using an LSTM network, and the anti-bird intelligent recognition method and system based on the thermal imaging are obtained. And the cross-modal features are fused through a Transform architecture, so that 95% of classification accuracy is realized. A bird repelling strategy is generated in real time through edge calculation, and the model is updated through cloud federal learning. The system integrates an oil-electric hybrid unmanned aerial vehicle and a ground device, supports dynamic path planning based on a thermodynamic diagram and differentiated repelling of directional sound waves, laser stroboflash and the like, has the night recognition accuracy rate of 92% and the bird repelling response time of 0.8 second, and is suitable for scenes of electric power, airports and the like. Through multi-dimensional perception, dynamic modeling and eco-friendly expelling, the problems of poor environmental adaptability, single strategy and the like of a traditional scheme are solved, and the anti-bird efficiency and the ecological safety are improved.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention belongs to the technical field of agricultural disease monitoring, prevention and control, and particularly relates to a rice blast monitoring and diagnosing method based on a mooring hot-air balloon and an unmanned aerial vehicle system. By constructing a dynamic observation platform of a tethered hot-air balloon and an unmanned aerial vehicle group, multi-variety, multi-temporal and multi-dimensional rice blast diseased plant multi-spectral / hyperspectral data and multi-information such as geography, soil, climate and field management are synchronously obtained, and a spectral database is constructed. An early diagnosis inversion model is constructed based on spectral characteristics of diseased rice plants and data characteristics of an unmanned aerial vehicle and a captive hot-air balloon by using self-supervised learning and deep learning technologies. According to the method, the cooperative advantages of the tethered hot-air balloon and the unmanned aerial vehicle are fully played, comprehensive acquisition and deep fusion of data are realized, the accuracy and timeliness of rice blast monitoring and diagnosis are improved, powerful technical support is provided for healthy development of the rice industry, and agricultural intelligence and sustainable development are assisted.

The invention relates to the field of unmanned aerial vehicle group control, in particular to an intelligent cooperative control method for an unmanned aerial vehicle group. Comprising the steps of receiving and preprocessing environment data, unmanned aerial vehicle state data and task data, and performing data fusion processing on the preprocessed data; performing unmanned aerial vehicle group task allocation by adopting a multi-objective optimization genetic algorithm to generate a task allocation scheme; generating an optimal path based on the task allocation scheme; the unmanned aerial vehicle executes a task according to the optimal path, and the flight path of the unmanned aerial vehicle is adjusted by using a dynamic obstacle avoidance function; a new optimal path is generated according to the current position of the unmanned aerial vehicle, the unmanned aerial vehicle continues to execute the task according to the new optimal path, and the unmanned aerial vehicle returns to the landing point after the task is completed and uploads the collected flight data and environment data. The problems that an existing selection mechanism usually depends too much on a single standard, and most of existing path planning methods only consider a static environment; and the problem that the unmanned aerial vehicle is easy to collide or deviate in the task execution process due to the lack of real-time obstacle avoidance capability is solved.

The invention belongs to the technical field of chlorophyll content inversion, and discloses a canopy chlorophyll content inversion method based on feature extraction and a machine learning model, and the method comprises the following steps: employing a mode of combining an unmanned plane carrying a multispectral camera and ground actual measurement; collecting and processing the chlorophyll content data of the potato canopies, and planting a quilt index; carrying out feature selection by using a Pearson correlation analysis and competitive self-adaptive reweighted sampling algorithm; constructing a chlorophyll content inversion model based on machine learning; and carrying out chlorophyll content inversion model optimization based on a grey wolf optimization algorithm and a sparrow optimization algorithm, and carrying out precision evaluation. According to the canopy chlorophyll content inversion method based on feature extraction and the machine learning model, comparative study is carried out on the single growth period and the whole growth period of potatoes, the optimal inversion model suitable for the single growth period and the whole growth period is determined, and the accuracy and applicability of the study are guaranteed.

The invention discloses a three-dimensional space self-adaptive unmanned aerial vehicle accurate spraying method and system and an unmanned aerial vehicle. The method relates to the technical field of unmanned aerial vehicle spraying, and comprises the following steps: scanning a spraying area on the bottom surface of a viaduct through an unmanned aerial vehicle to obtain unmanned aerial vehicle spraying quality data, and analyzing, optimizing and adjusting the unmanned aerial vehicle spraying quality data. The method comprises the following steps: scanning a spraying area on the bottom surface of a viaduct through an unmanned aerial vehicle to obtain three-dimensional point cloud data, adaptively generating a flight path and a spraying track of the unmanned aerial vehicle based on the data, flying according to a planned path and spraying, collecting spraying quality data through a sensor, analyzing the spraying quality data, and determining the spraying quality of the viaduct. The dynamic liquid level monitoring error quantized value and the unmanned aerial vehicle precise spraying method stability quantized value are obtained and analyzed with threshold values in a database, the unmanned aerial vehicle precise spraying method is optimized and adjusted, the stability of the unmanned aerial vehicle precise spraying method is improved, and the problem that in the prior art, an unmanned aerial vehicle precise spraying method is insufficient in stability is solved.

The invention relates to the technical field of safe flight of unmanned aerial vehicles, in particular to an air-ground multi-mode bird monitoring and early warning method and system for guaranteeing low-altitude safety, and the method comprises the steps: obtaining radar, visual, infrared and other multi-sensor data, and determining a first bird recognition probability, a second bird recognition probability and a third bird recognition probability through the data; and determining a final bird recognition probability based on the basic weight matrix, when the final bird recognition probability is greater than a preset threshold value, obtaining a relative distance and an included angle between the unmanned aerial vehicle and the bird, and establishing a multi-stage early warning mechanism according to the relative distance and the included angle to perform bird early warning. According to the invention, the contradiction between load limitation of small and medium-sized unmanned aerial vehicles and wide-area monitoring requirements is solved, all-weather, all-weather, all-direction, non-blind-area, all-route and real-time bird monitoring, recognition and tracking under a complex urban environment are realized, the real-time monitoring accuracy of birds is improved, early warning is performed in combination with distance and included angle information, and the real-time monitoring accuracy of the birds is improved. The collision between the unmanned aerial vehicle and birds can be more accurately and effectively avoided, and the low-altitude flight safety is guaranteed.

The invention relates to the technical field of image recognition, in particular to an agricultural crop disease and pest detection method and system based on image segmentation, and the method comprises the steps: collecting a vertical view angle image and an inclined view angle image of a tea garden through an unmanned aerial vehicle, carrying out the image registration through SIFT feature extraction, KNN matching and RANSAC filtering, and carrying out the alignment to a reference image coordinate system. And segmenting the foreground region by adopting a U-Net algorithm, and performing post-processing optimization. Positioning a disease area through a color detection method, extracting disease features, and identifying a disease stage by using a CNN algorithm; and using a YOLOv3 algorithm to identify insect body areas, extracting insect pest features, and evaluating insect pest degrees. And finally, based on the disease stage and the pest degree, using an LSTM algorithm to predict the pest diffusion trend, and generating a space thermodynamic diagram according to the reference image coordinate system, thereby improving the detection precision and timeliness, and providing technical support for intelligent agricultural prevention and control.

The present invention relates to a protective foldable cage (100) adapted to flying drones. It comprises several ribs linked at their extremities by a ring and comprising on their length several connection points allowing strings to join each of the adjacent ribs. The length of the string corresponds to the maximal angular shift between the first and the last ribs. The protective cage further comprises easily removable locking means adapted to maintain the ribs at predetermined relative angular position once deployed. The present invention further relates to a system comprising such a protective cage and a drone support, as well as a method of protection of flying drones.

The invention relates to the technical field of image detection, in particular to a pest and disease identification method and system based on unmanned aerial vehicle remote sensing. The method comprises the following steps: acquiring health indexes of each plant; marking suspicious points; spraying preset hormones to the suspicious points; after a first preset time interval, obtaining the health index of the hormone-induced plant again, and distinguishing whether the plant is pathological abnormality or physiological abnormality according to the change of the health index; on the basis of the pathology and physiology distinguishing result, a definite diagnosis threshold value in a preset range of the suspicious point is adjusted, so that when the plant at the suspicious point is pathologically abnormal and the health indexes of other plants in the preset range adjacent to the suspicious point are abnormal, the condition that the other plants are pathologically abnormal can be detected more sensitively; and using the adjusted pest and disease damage early warning threshold value to identify pest and disease damage conditions of plants of which suspicious points are adjacent to a preset range. According to the invention, the pest and disease states of crops can be detected more accurately.

The invention relates to the technical field of data processing, in particular to a global unattended intelligent monitoring method based on multi-source data fusion, and the method comprises the steps: obtaining a real-time feature vector of a to-be-recognized target to obtain a global discrimination score; evaluating the category uncertainty of the target to be identified; according to the category uncertainty, a dynamic weight used for model fusion is generated in a self-adaptive mode; and obtaining a local discriminant by using a pre-trained local detail model, carrying out weighted fusion on the local discriminant and the global discriminant by using a dynamic weight to obtain a final discriminant, and determining the category of the to-be-recognized target based on the final discriminant, thereby realizing adaptive recognition of the unmanned aerial vehicle and the birds. Through the technical scheme of the invention, a nonlinear decision boundary of the unmanned aerial vehicle and birds can be effectively constructed, and the recognition accuracy of a fuzzy target in a complex scene is remarkably improved.

The invention discloses a multi-unmanned aerial vehicle nest cooperative site selection method for forest patrol. The method comprises the steps of obtaining geographic information data and performing data preprocessing; the method comprises the following steps: extracting main ridge lines of a forest region, comprehensively considering three factors of geographical environment, infrastructure and safety guarantee, and preliminarily pre-selecting a plurality of unmanned aerial vehicle nest candidate points; comprehensively considering the electric quantity, the coverage rate, the anti-interference performance and the terrain influence of the unmanned aerial vehicle, constructing a multi-target optimization site selection model, and determining a target function and a constraint condition of the multi-target optimization site selection model; an improved NSGA-II algorithm is adopted to solve the multi-objective optimization site selection model, and an optimal site selection layout scheme of the unmanned aerial vehicle nest is obtained; a Pareto optimal solution set is calculated based on the fuzzy set theory, optimal indexes of all solutions are calculated and sorted, and the solution with the maximum optimal value is a compromise solution of the multi-target problem. According to the method, the cooperative operation efficiency of the unmanned aerial vehicle group is effectively improved, the nest construction and operation cost is remarkably reduced, and the use efficiency and the resource allocation capability of the nest are improved.

The invention discloses a method and a system for assisting rice breeding by using a lodging-resistant index, particularly relates to the field of agricultural breeding, and quantifies the lodging-resistant capability of plants by constructing an evaluation model fused with multi-source phenotypic parameters. Key character data are obtained by combining mechanical testing, manual measurement and unmanned aerial vehicle image acquisition, an LRI index is calculated through standardization processing and image recognition, resistance grading is conducted, and lodging-resistant breeding material screening is achieved. According to the method and the system for assisting rice breeding by using the lodging-resistant index, digitization and intellectualization of a rice lodging resistance evaluation process are realized, and scientificity and efficiency of evaluation are remarkably improved. And by introducing an unmanned aerial vehicle remote sensing and image recognition algorithm, the large-scale sample processing capability is improved, and the timeliness and stability of data acquisition are enhanced. A lodging risk thermodynamic diagram and a recommended breeding list can be output through a matched visual analysis function, visual reference is provided for researchers and breeding decisions, and the method has good application and popularization prospects.

The invention provides a dynamic pesticide spraying device based on a remote sensing crop pest stress degree identification technology, which comprises an unmanned aerial vehicle body, and the bottom end of the unmanned aerial vehicle body is provided with a pesticide liquid packaging assembly; a base is arranged at the bottom end of the liquid medicine containing assembly, and an atomizing nozzle is arranged on the side portion of the base. The atomizing nozzle is communicated with the liquid medicine packaging assembly; a remote sensing acquisition module is arranged on the other side of the base; the remote sensing acquisition module comprises a multispectral sensor, a hyperspectral sensor, a thermal infrared sensor and an NDVI vegetation index camera; the multispectral sensor, the hyperspectral sensor and the thermal infrared sensor are connected with the central processing unit, the NDVI vegetation index camera is connected to the image processing module through the wireless transmission module, and the wireless transmission module is connected with the central processing unit; the central processing unit is connected to the atomizing nozzle; the central processing unit adjusts the flow of the atomization nozzle according to data collected by the remote sensing collection module. The pesticide spraying amount is automatically controlled according to pest and disease damage and growth conditions, and dynamic regulation and control are achieved.

The invention provides a field weed intelligent processing method based on an unmanned aerial vehicle, which is based on an unmanned aerial vehicle image gridding weed density estimation and quantitative spraying control system and is characterized in that the unmanned aerial vehicle image resolution is improved through a super-resolution enhancement technology (ESRGAN), so that small-scale weed features are clearer, and the weed density is improved. Therefore, the weed identification accuracy of the model is improved. Meanwhile, statistics of the number of weeds in different grids is achieved through a density estimation algorithm, the spraying amount is dynamically adjusted according to an estimation result, and quantitative accurate spraying is achieved. According to the method, the weed monitoring precision is improved, the pesticide consumption is greatly reduced, and the environmental protection benefit and the cost benefit of the system are improved.

The invention discloses an agricultural protection unmanned aerial vehicle and a dynamic path optimization method, and belongs to the technical field of unmanned aerial vehicles. The agricultural protection unmanned aerial vehicle comprises an unmanned aerial vehicle body, a mounting part is arranged on the unmanned aerial vehicle body, a pesticide barrel which is vertically arranged and used for containing pesticide liquid is arranged in the mounting part, and a plurality of nozzles which are annularly arranged and used for pesticide spraying operation are arranged at the bottom of the pesticide barrel; the unmanned aerial vehicle body is further provided with an acquisition module used for acquiring a whole area image of a plant operation area and an image processing module used for identifying the acquired image according to plants and marking plant growth density in a partitioned mode. Compared with the prior art, the self-adaptive pesticide spraying device has the advantages that self-adaptive pesticide distribution and spraying are realized according to different growth densities, the conditions of excessive spraying and insufficient spraying of the plants in areas with lower growth densities are avoided, and the spraying efficiency of the plants is improved. The crop planting quality is improved, and the high use value is achieved.

The present invention relates to the field of computer vision, specifically a method and system for real-time identification and control of crop pests and diseases based on multimodal edge computing. The system comprises: acquiring drone multispectral imagery, infrared thermal imaging, and the NDVI index, preprocessing them in combination with field IoT sensor data to generate a multimodal feature input sequence; constructing a spectral-environmental fusion feature matrix based on the Transformer attention mechanism, and utilizing the YOLOv7-Spectral model for pest and disease detection to generate a pest and disease distribution heat map; calculating the pest risk index (BRI) and pesticide application priority map based on historical data; employing an ant colony optimization algorithm to plan drone application paths and optimizing the application plan based on wind speed and humidity parameters; accurately applying pesticides along the optimized paths by the drones and monitoring disease trends; adjusting the model based on application feedback data, and optimizing pest and disease control strategies using federated learning. The present invention improves recognition accuracy, reduces pesticide use, and achieves precise, efficient, and intelligent control.

The invention relates to the technical field of rice monitoring, and particularly discloses a rice planting area monitoring management system and method based on an unmanned aerial vehicle, and the system comprises an area division module which is used for dividing a rice planting area into a plurality of monitoring areas with the same size; the unmanned aerial vehicle shooting module comprises a shooting unit and an identification unit, and the shooting unit is used for shooting rice image information in different areas; the rice pest influence coefficients in different monitoring areas are calculated in combination with diversified data, and are compared with a preset pest influence coefficient threshold interval, so that pest risks in different monitoring areas can be accurately analyzed; and according to the grade of the insect pest risk, whether the insect pests need to be removed by using pesticides is judged, so that the situation that crops, soil, water sources and the like are polluted due to the fact that the insect pests are removed by blindly using the pesticides when the insect pests are in the initial stage is avoided.

The invention discloses an unmanned aerial vehicle threat assessment method and system, and the method specifically comprises the steps: obtaining each static capability index value and an environment meteorological index value of a to-be-assessed unmanned aerial vehicle, and calculating a static capability threat score and a meteorological threat score of the to-be-assessed unmanned aerial vehicle based on the reference values of the static capability index and the environment meteorological index; obtaining a dynamic capability index value of the to-be-evaluated unmanned aerial vehicle, and calculating a dynamic capability threat score of the to-be-evaluated unmanned aerial vehicle based on a pre-trained neural network model; distribution of threat scores of the to-be-evaluated unmanned aerial vehicle is acquired, and weight values corresponding to the threat scores are calculated based on information entropies corresponding to the threat scores; and calculating a comprehensive threat score of each to-be-evaluated unmanned aerial vehicle based on the weight value corresponding to each threat score. Compared with the prior art, a more accurate and effective unmanned aerial vehicle threat assessment strategy is realized; the problem of model over-fitting or insufficient generalization ability is solved; and the method has a stronger capturing capability for a complex environment and a non-linear relationship.

The invention relates to the technical field of path planning, and provides a multi-strategy collaborative enhancement path planning method and navigation equipment. The path planning method comprises the following steps: acquiring an environment map comprising a starting position and a target position; initializing DBO algorithm parameters; initializing a dung beetle population based on Bernoulli chaotic mapping; calculating the fitness value of each dung beetle; determining the boundary of each dung beetle, and continuously iteratively updating the individual optimal fitness value and the global optimal fitness value of each dung beetle; and outputting the optimal path of the unmanned aerial vehicle until the number t of iterations is greater than the preset number T. According to the method, the initial solution is generated by introducing the chaos mapping based on Bernoulli distribution, so that the diversity and the ergodicity of the initial population are enhanced, premature convergence is effectively avoided, and the global search capability of the algorithm is improved.

A device and a method for introducing a drone to an area of interest are presented. The delivery system may include a housing, a drone, either a timer or a receiver, a lock mechanism, and a biasing mechanism. The housing further includes a pair of parts with or without subparts. At least one sensor is attached to the drone. The timer or the receiver is secured to the housing and communicable with the lock mechanism to control function thereof. The lock mechanism is adapted to releasably secure the parts or the subparts. The drone is enclosed within the housing in a CLOSED configuration when the lock mechanism is locked. The biasing mechanism separates the parts or the subparts to an OPEN configuration when the lock mechanism is unlocked. The drone is introducible to the area of interest in the CLOSED configuration and separable from the housing in the OPEN configuration.

The invention discloses an agricultural plant protection unmanned aerial vehicle, and belongs to the technical field of agricultural plant protection, the agricultural plant protection unmanned aerial vehicle comprises a stirring structure, the stirring structure comprises a rotating rod and a rotating frame distributed on the periphery of the rotating rod, the rotating rod and the rotating frame are both arranged in a stirring tank, the rotating frame is in a [shape, and the rotating rod and the rotating frame rotate in opposite directions; the quantitative discharging structure comprises a pesticide box arranged on the rear portion of the stirring tank, the bottom wall of the pesticide box is connected with a closed disc in an attached and rotating mode, a discharging hole is formed in the closed disc, the pesticide box is communicated with the interior of the stirring tank through a feeding pipe fixed to the interior of the stirring tank, and quantitative discharging is achieved through periodic alignment of the discharging hole and the feeding pipe. The bevel gear driving structure drives the rotating rod and the rotating frame to rotate reversely, the reverse rotation generates a strong shearing and stirring effect, the stirring blades with the flowing holes are matched, the liquid medicine mixing uniformity is improved, the quantitative discharging structure achieves accurate control over the medicine feeding amount, and the medicine feeding error is reduced.

A device for launching a drone using spring thrust, in which the means that control the unlocking of an ejection assembly from its armed position include a release housing containing a release chamber, at least one release element which, with the housing, delimits the chamber and is able to move, under the action of the pressure of a gas introduced into the chamber, from a position of rest to a release position in which the at least one release element unlocks the ejection assembly, and return means for returning the at least one release element to the position of rest. The invention also relates to a method for launching drones using the device and to a tool for implementing this method.

The invention discloses a farmland weed and external invasion species detection method and device based on an unmanned aerial vehicle and deep learning, and the method comprises the steps: collecting and marking images containing weeds and external invasion species in a farmland, and forming a data set; the method comprises the following steps: constructing a weed and external invasion species target detection framework AriNanoDet model based on NanoDet, adopting ShuffleNetV2 as a backbone network, utilizing ShuffleNetV2Block in a second stage to a fourth stage of feature extraction, and then weighting channel information of an output feature map by applying an SE module; in the multi-scale feature fusion part, a self-adaptive feature selection mechanism is introduced on the basis of a feature pyramid network, a fused feature map is processed through a detection head, a detection result is optimized, and a model is formed and trained; the trained model is installed on an unmanned aerial vehicle for real-time detection, and positioning is carried out according to the detected coordinates of weeds and external invasion species. According to the invention, automatic monitoring of farmland weeds and external invasion species is realized, the labor cost is saved, the detection precision is high, and the detection process is simple and convenient.