346 results about "Drone" patented technology

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.

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 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 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 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 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.

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 bird diverter applicator for drones is disclosed. The bird diverter applicator for drones reduces the risk of electrocution to ground crews and eliminates the need to de-energize the power lines for installation of the bird diverters.

The present application provides a high-slope inspection method and drone based on a drone, the method comprising: obtaining an inspection position table for the high slope; for each slope section, generating a first flight path corresponding to the drone based on multiple control points corresponding to the slope section, the first flight path including multiple waypoint positions corresponding one-to-one to each control point; for each slope section, determining a target waypoint position that meets the slope surface flatness requirements based on a first image captured by the drone at each waypoint position on the first flight path corresponding to the slope section, determining the drone's shooting angle and shooting distance based on the target waypoint position, correcting the waypoint position in the first flight path based on the shooting distance to generate a second flight path, and controlling the drone to inspect the slope section based on the second flight path. The present application achieves fully automatic high-slope inspection without the need for auxiliary equipment deployment by autonomously planning the flight path and optimizing the shooting angle and shooting distance of the drone.

The invention discloses a wing-flapping unmanned aerial vehicle bird repelling cruise route planning method based on a WSN idea, and belongs to the technical field of unmanned aerial vehicles. In order to solve the problems of low deterrence, short timeliness, small coverage range and the like of a traditional bird repelling means, sensor nodes are generated through an improved particle swarm algorithm, and efficient coverage of a target area is achieved; clustering and partitioning the sensor nodes by adopting an improved k-means algorithm, and dividing a detection area for each unmanned aerial vehicle; and a cruising path is optimized by applying a genetic algorithm, so that the unmanned aerial vehicle is ensured to ensure the biological deterrence effect, the labor cost is reduced, and the inspection efficiency and coverage are improved. The method is suitable for bird damage control in wide areas such as agriculture and forestry orchards, power transmission and distribution lines, high-voltage substations and airports, and has remarkable economic benefits and social benefits.

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 a bird nest removing system based on unmanned aerial vehicle detection and deep learning, and the system employs an image collection module of an unmanned aerial vehicle to carry out the aerial photographing of a target area, the captured image data is transmitted to a ground terminal, and the ground terminal carries out the deep analysis of the transmitted image through a detection module, and generates a detailed detection report. A detection report is input to a judgment module, if it is judged that the bird nest exists in the detection information, an alarm module is triggered immediately, a clearing module of the unmanned aerial vehicle is started and controlled, a clearing or grabbing device is started, fixed-point clearing is conducted on the target bird nest, and meanwhile the unmanned aerial vehicle continuously uses a high-resolution camera to monitor the whole clearing process. And after the clearing task is finished, the unmanned aerial vehicle performs inspection shooting and detection on the target area again to verify whether the bird nest is thoroughly cleared. According to the method, image acquisition, deep learning detection, intelligent discrimination and diversified clearing technologies are combined, and the bird nest on the high-voltage power transmission facility can be quickly and accurately identified and efficiently cleared.

The invention provides a path optimization method for cleaning an unmanned aerial vehicle swarm and application. The method comprises the steps that position initialization is conducted on multiple unmanned aerial vehicles in a task area, a fitness function is set, and a variable Trials is recorded; the stages of employed bees, observation bees and reconnaissance bees are executed in sequence through an artificial bee colony algorithm, and path searching, probability selection, boundary processing and position updating are carried out; a reverse learning strategy, disturbance control frequency and a global factor are introduced into track initialization to improve search performance, and meanwhile, a nectar source regeneration mechanism in a scout bee stage is set. Through dynamic task allocation optimization, global collaborative path planning and a distributed elastic control architecture, the cleaning resource utilization rate, the operation efficiency and the energy consumption balance are improved, the fault-tolerant capability of the system is enhanced, and the cleaning requirement of the complex building facade is met.

The invention relates to the technical field of peanut phenotype intelligent analysis scheme design based on an unmanned aerial vehicle, in particular to a peanut phenotype intelligent analysis method and system based on the unmanned aerial vehicle. The method comprises the following steps: acquiring a multi-modal image through a visible light, multispectral and thermal infrared sensor carried by an unmanned aerial vehicle, separating a plant area through illumination normalization and soil mask treatment, aligning data in different growth stages by using a time sequence registration algorithm, and extracting morphological, spectral and thermal characteristics to construct a fusion matrix; inputting the matrix into a dynamic growth model fusing LSTM and a crop physiological model, and outputting the number of pods, biomass and disease resistance parameters; and generating fertilization and irrigation decision suggestions in combination with a preset threshold. The system comprises a multi-modal data acquisition module, an image processing module, a feature fusion module, a model calculation module and a decision generation module. According to the method, multi-source data collaborative analysis, time sequence growth precise modeling and field management dynamic optimization are realized, and the peanut phenotype analysis efficiency and decision scientificity are remarkably improved.

The utility model discloses a grassland biomass comprehensive non-damage measuring device, and belongs to the technical field of grassland biomass measurement. Comprising a remote sensing unmanned aerial vehicle, a power supply control device and an I-shaped base are detachably installed on a vehicle body of the remote sensing unmanned aerial vehicle, azimuth cameras are fixedly arranged at the front end and the rear end of the remote sensing unmanned aerial vehicle, four Z-shaped supporting frames are fixedly arranged on the I-shaped base, and a sample measuring frame is installed between the Z-shaped supporting frames and located at the bottom in an inserted mode; the unmanned aerial vehicle hovering technology is utilized, a grassland measurement area is laid in cooperation with the sample measurement frame, non-damage measurement work can be achieved, illumination treatment can be conducted through an illumination lamp, the phenomenon that image shooting cannot be achieved in a dark area is avoided, and four transparent wind shielding belts are arranged on the sample measurement frame and form a conical three-dimensional structure; the strong wind generated by the remote sensing unmanned aerial vehicle does not cause the bending phenomenon of organisms on the grassland enclosed by the sample measurement frame, so that the measurement data accuracy is ensured, and the practicability is high.

The invention provides a zizania aquatica open-space cooperative pest and disease damage pesticide application operation method and device and a readable storage medium thereof, and belongs to the field of agricultural intelligent plant protection. The problems that existing zizania aquatica plant protection depends on manpower (large in intensity and poor in safety), a traditional unmanned aerial vehicle does not have accurate perception and closed-loop control, and air-ground cooperation is disjointed are solved. According to the scheme, ground intelligent sensing nodes (bimodal imaging and edge calculation for identifying diseases and insect pests), a central cooperative control module (scheduling and reinspection triggering) and an aerial autonomous pesticide application module (RTK positioning and variable pesticide application) are arranged in a zizania aquatica field, and a'monitoring-alarming-pesticide application-reinspection 'closed loop is formed. Real-time pest and disease damage identification (the accuracy rate is 93.5%) can be achieved, pesticide application is started within 2.8 minutes, the pesticide dosage is reduced by 53.6% or above compared with that of a traditional unmanned aerial vehicle, the prevention and control accuracy and safety are improved, and intelligent water bamboo plant protection is promoted.

The invention relates to the technical field of unmanned aerial vehicles, and discloses a method for displaying the relative position between an unmanned aerial vehicle and unmanned aerial vehicle detection countering equipment, and the method comprises the steps: obtaining the first position information of a target unmanned aerial vehicle and the second position information of the unmanned aerial vehicle detection countering equipment; determining a horizontal distance and a vertical distance between the target unmanned aerial vehicle and the unmanned aerial vehicle detection countering device according to the first position information and the second position information; a first coordinate system is established with the position of the unmanned aerial vehicle detection countering device as a reference position, the first position of the schematic diagram of the target unmanned aerial vehicle in the first coordinate system is determined, and the abscissa and the ordinate of the first position are the horizontal distance and the vertical distance respectively; and displaying the first coordinate system and displaying the schematic diagram of the target unmanned aerial vehicle at a first position in the first coordinate system. According to the invention, the relative position between the target unmanned aerial vehicle and the unmanned aerial vehicle detection countering device can be visually reflected through the two-dimensional view.

This invention provides a method and apparatus for detecting termite nests in earthen embankments. The method involves mounting a high-definition camera and / or an infrared thermal imager and / or an infrared gas imager and / or a plant spectrometer on a drone. The high-definition camera captures images of the embankment surface and its surroundings. An AI model trained using artificial intelligence training methods identifies natural phenomena in the high-definition images that reflect the living habits of termites. The infrared thermal imager utilizes the temperature difference between the inside of the termite nest and the surface of the embankment to locate anomalies in the embankment surface temperature. The infrared gas imager detects the CO2 distribution on the embankment surface to identify areas with abnormal carbon dioxide levels. The plant spectrometer searches for changes in the spectral composition of plants on the embankment surface to locate possible fungi growing above termite nests. By mounting two, three, or four of these devices on the drone, the same area is scanned simultaneously using multiple devices to screen for abnormal areas and determine the termite nest area. This is then combined with manual and physical detection methods to pinpoint the termite nest.

The unmanned aerial vehicle underwater virus detection and concentration device comprises an unmanned aerial vehicle body, a lighting device is arranged in the middle of the upper end of the unmanned aerial vehicle body, wings are arranged on the four faces of the upper end of the unmanned aerial vehicle body, a first motor is arranged in the unmanned aerial vehicle body, and a rotating shaft is arranged on a driving shaft of the first motor; a cable fixed to the shell is wound on the rotating shaft, a groove matched with the shell is formed in the bottom of the unmanned aerial vehicle body, the lower end of the shell is connected with an assembling structure, and the lower end of the assembling structure is fixedly connected with a water taking structure; the assembly structure is used for accommodating a water sample and simultaneously implementing a virus concentration process; the water taking structure is used for taking water and sampling the water body. The device has the characteristics of safety and high convenience, and can realize quick fixed-point sampling and virus concentration in a water area.

The invention relates to the field of unmanned aerial vehicle path adjustment, and discloses an unmanned aerial vehicle pest monitoring method based on intelligent path planning, which is used for solving the problem that the pest condition cannot be monitored in time when the pest distribution is dynamically changed, and comprises the following steps: averagely dividing a region needing pest monitoring into n regions which are recorded as monitoring sub-regions, detecting meteorological data of each monitoring sub-region, calculating to obtain a meteorological influence coefficient, obtaining illumination condition data of each monitoring sub-region, calculating to obtain an illumination influence coefficient, obtaining historical insect disaster data of each monitoring sub-region, evaluating to obtain a historical disaster coefficient, and comprehensively evaluating to obtain a disaster index. The monitoring sub-areas are divided in real time according to the disaster indexes, path planning is performed in real time according to the divided areas by using the algorithm A, and the unmanned aerial vehicle performs pest monitoring according to the real-time path planning, so that the effectiveness of unmanned aerial vehicle path planning is effectively improved, and the damage rate of crops is reduced.

This utility model discloses a drone-based drag chain spraying device, relating to the field of agricultural spraying equipment technology. Its main objective is to provide a drone-based drag chain spraying device capable of adjusting the distance between the nozzle and crops as needed. The main technical solution of this utility model is as follows: a drone-based drag chain spraying device, comprising: a drone component, including a drone body and a loading box, the loading box being located on the upper part of the drone body; a liquid supply component, including a liquid supply pipeline, a housing, an adapter, a roller, a motor, an output pipeline, and a nozzle. The housing is installed on the lower part of the drone body. One end of the liquid supply pipeline is connected to the loading box, and the other end passes through the housing and is connected to the adapter. The output pipeline is wound around the outside of the roller. The motor is connected to the roller to drive the roller to rotate around its axis. One end of the output pipeline is connected to the adapter, and the other end is connected to the nozzle. This utility model is mainly used for spraying pesticides.

The utility model discloses a biological prevention and control releasing device based on a four-axis unmanned aerial vehicle, which belongs to the technical field of biological prevention and control and comprises an unmanned aerial vehicle body. The throwing mechanism comprises a connecting assembly and a storage shell, the top of the connecting assembly is fixedly connected to the bottom of the unmanned aerial vehicle body, the bottom of the connecting assembly is clamped to the two sides of the top of the storage shell in a limiting mode, the storage shell comprises a storage bin, a protection cover is connected to the bottom of the storage bin, and a discharging bottom cylinder is connected to the bottom of the protection cover; a discharging buffer pipe is arranged among the storage bin, the protection cover and the discharging bottom cylinder in a communicating mode. And a synchronous putting assembly is arranged in the storage shell and comprises a material shifting rod and a discharging ratchet wheel, the material shifting rod and the discharging ratchet wheel are arranged at the two ends of the discharging temporary storage pipe in a transmission mode correspondingly, and the synchronous putting assembly is used for stably putting trichogramma eggs, so that the situation that the trichogramma eggs are put too much or too little on one side is avoided.

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 relates to the technical field of agriculture and forestry pest control, and provides an agriculture and forestry pest intelligent early warning system based on a monitoring image, comprising: a region monitoring module connected with a plurality of groups of local devices such as a humidity sensor, a temperature sensor, a monitoring camera probe and the like, the area monitoring modules are arranged in an agriculture and forestry area at equal intervals, and acquire environment data of the arranged area through local equipment; and the unmanned aerial vehicle base station control module is carried on an unmanned aerial vehicle base station. Environmental data and image information of agriculture and forestry areas can be obtained in real time, pest and disease damage risks can be rapidly judged, early warning signals can be sent out in time, pest and disease damage monitoring efficiency is improved, the types and positions of pest and disease damage can be accurately recognized, and by collecting relevant data of all areas and proofreading through intelligent entries, the risk of pest and disease damage can be accurately recognized. Therefore, agriculture and forestry related personnel can better trace the root of diseases and pests of agriculture and forestry.

This invention relates to the field of unmanned aerial vehicle (UAV) interception technology, specifically to a UAV swarm for nuclear missile interception and its interception method, comprising: UAV modules, a communication module, and a coordination module; multiple UAV modules constitute a UAV swarm; the communication module is connected to each UAV module to achieve encrypted information transmission; the coordination module is constructed based on multi-agent reinforcement learning, and is connected to the UAV modules through the communication module, and is used to control the UAV swarm to cooperate in interception; this application achieves stable operation in a nuclear environment through the UAV modules, and constructs a protective shield to intercept nuclear missiles through the coordination module.

A device for distributing pollen attachable to a drone, characterized in that it comprises: - a hopper (1) that stores a quantity of pollen, equipped with an agitator that ensures the homogeneity of the pollen, - at least one outlet orifice (8), located at the bottom of the hopper (1), - a metering lever (2) located below the outlet orifice (8), equipped with a displacement mechanism that regulates the degree of opening of the outlet orifice (8) and therefore the quantity of pollen that comes out through it, - a Venturi tube (3) provided with a central narrowing corresponding to the outlet orifice (8), - a blower (5) connected to one end of the Venturi tube (3), equipped with a motor, which provides pressure along the Venturi tube (3) to facilitate the exit of the pollen, - at least one adjustable nozzle (4) connected to the other end of the Venturi tube (3), which allows the dispersion of the pollen, - a supporting structure (7) linked to the hopper (1) which is equipped with a plurality of couplings (6) intended to be coupled to a drone, - a control unit connected to the displacement mechanism of the metering lever (2) that regulates the pollen output and connected to the blower motor (5), - a power supply or a connecting element intended to connect the device to a power supply of the drone.