3603 results about "Fruit tree" patented technology

The invention relates to the technical field of agricultural intelligent detection, in particular to a fruit tree disease and pest detection method based on machine vision, which comprises the following steps: acquiring visible light, infrared light and ultraviolet light images of a fruit tree at multiple angles through a multispectral camera to obtain complete fruit tree surface information, and preprocessing the acquired images to obtain a fruit tree disease and pest detection result; comprising the steps of denoising, illumination correction and image alignment, extracting feature vectors, including the color, shape and size of scabs, pest distribution and other features, of pest and disease damage from preprocessed images through a convolutional neural network, fusing the pest and disease damage features with environmental data by using a multi-modal data fusion algorithm, and enhancing the accuracy of pest and disease damage classification. Classifying and identifying diseases and pests based on a support vector machine classification algorithm; according to the dynamically adjusted threshold value and suggestions provided by the expert system, a disease and pest diagnosis report is generated, and prevention and control measure suggestions are provided; according to the invention, efficient and accurate detection and management of fruit tree diseases and insect pests can be realized, and the intelligent level of agricultural management is improved.

The invention relates to the technical field of intelligent agriculture, in particular to a growth situation monitoring method and system for fruit trees, and the method comprises the following steps: collecting the image information of an orchard through an image collection device based on the position information of the orchard, analyzing and recognizing the position information of a plurality of fruit trees and obstacles in the orchard, and generating a three-dimensional model of the orchard. According to the invention, through image acquisition and three-dimensional model construction, the positions and surrounding environment information of a plurality of fruit trees are accurately identified, blind spots are reduced, resource allocation is optimized, a routing inspection path is optimized, the utilization rate of manpower and time resources is improved, and through real-time analysis of fruit tree state images and marking of key growth areas, the quality of the fruit trees is improved. The monitoring capability of the fruit tree health condition is improved, the early recognition capability of diseases and insect pests is enhanced, the timeliness and effectiveness of prevention and control measures are improved, the growth condition of the fruit tree is comprehensively evaluated by analyzing image information and physiological parameters, the diseases and insect pests are recognized in the early stage and intervened in time, and the quality and yield of fruits are improved.

The invention relates to the field of green orange detection, in particular to a green orange detection method based on self-supervised contrast learning and a computer device. The feature extraction capability of the model is enhanced by adopting a self-supervised comparative learning method, collected green orange fruit image data without a green orange fruit tree background and green orange fruit tree image data without a fruit background are constructed into a positive and negative sample pair, and through comparative learning of the positive and negative sample pair, redistribution of model weights can be realized, and the accuracy of the model is improved. According to the method, the detection capability of the model in the camouflage environment is enhanced, finally, a loss function, the number of iterations of the model, the batch size and the initial learning rate are set, iterative training is carried out through marked data, finally, green orange detection is carried out through the trained YOLOv11-go network model, and the green orange detection capability in the camouflage environment is greatly improved. The method is suitable for detecting the green orange fruits.

The invention relates to the technical field of agricultural informatization, in particular to an orchard three-dimensional reconstruction and fruit tree semantic segmentation method based on a neural radiation field, and the method comprises the following steps: carrying out the multi-view shooting of an orchard through an unmanned plane, and obtaining an original image data set for three-dimensional reconstruction; and preprocessing the original image data set, including camera pose estimation and distortion removal, image down-sampling, monocular depth estimation and semantic annotation, and generating image data of RGB images, depth maps and semantic maps suitable for neural radiation field network training. According to the method, under the condition of processing a large-scale outdoor environment, especially complex illumination conditions and variable terrains, through optimized unmanned aerial vehicle multi-view reconstruction, an improved NeRF algorithm and an introduced semantic field, the limitation of a traditional method can be overcome when the method faces a large-scale orchard scene, and the method is suitable for large-scale orchard scenes. And faster, finer and more accurate three-dimensional reconstruction is realized.

The invention discloses an unmanned aerial vehicle double-unmanned aerial vehicle cooperative intelligent pesticide spraying method for fruit trees, and relates to the technical field of plant protection unmanned aerial vehicles, and the method comprises the steps: deploying a laser radar and a high-precision GPS on an unmanned aerial vehicle, collecting orchard three-dimensional point cloud data, and constructing an orchard three-dimensional point cloud data set; performing data preprocessing on data in the constructed orchard three-dimensional point cloud data set, cleaning the data and performing position correction; an orchard three-dimensional grid model is constructed, orchard areas are divided based on the height of fruit trees, and tasks are allocated to the unmanned aerial vehicle; planning a pesticide spraying path of the unmanned aerial vehicle to execute pesticide spraying operation, and monitoring pesticide sedimentation and spraying effects in real time; the comprehensive score YL of the orchard is obtained according to pesticide spraying data collected in real time, the comprehensive score YL and a preset pesticide spraying evaluation threshold value YZ are compared and analyzed, the current pesticide spraying condition of the orchard is evaluated, and supplementary operation is conducted; pesticide waste caused by height difference of fruit trees is effectively reduced, the coverage rate and uniformity of pesticide spraying are improved, and the working efficiency of the unmanned aerial vehicle is improved.

The invention discloses an intelligent orchard inspection robot and a control system and method thereof, and relates to the technical field of robots, and the method comprises the steps: customizing an inspection task at an APP client according to an orchard demand, and remotely controlling the operation state and parameter information of the robot; after the robot is started, the robot starts to walk, collects multi-view images and point cloud data of the surrounding environment, and transmits the multi-view images and the point cloud data to the data processing module, the inspection navigation subsystem and the cloud control subsystem; an Alpha Shape algorithm is combined with a grid integration method, and the fruit tree canopy volume is accurately measured and calculated according to the point cloud data; receiving and processing the image and the point cloud data to obtain environment monitoring data and crop growth information, and generating current real-time inspection information, a pest and disease identification result and an equipment laser cloud picture; and processing the point cloud data to obtain a motion track of the robot, and constructing a map to realize autonomous inspection of the robot. According to the invention, accurate path planning can be realized, and autonomous routing inspection is realized.

An intelligent air-assisted spraying device for orchards includes a traction mechanism, a spaying system, a collecting system, and a processing-controlling system. The traction mechanism is provided with a spray tank. The spaying system includes a main body connected to the spray tank and multiple spraying tubes, first ends of the spraying tube are communicated with the main body, and second ends of the spraying tubes are capable of rotating. The collecting system is configured to collect a contour and a volume of the canopy, and branch and leaf density of a fruit tree. The processing-controlling system is configured to obtain multiple spraying areas, calculate an air outlet angle of each spraying tube, and control a rotation of the second end of each spraying tube according to the air outlet angle. The device can adjust the spraying direction of each spraying tube, and the quality of pesticide application can be improved.

The invention provides an anti-premature senility management method for closely-planted double-layer truncated cone macadamia nut trees, and belongs to the technical field of fruit tree cultivation. The premature senility prevention management method comprises layered nutrition supply and flower and fruit protection management. The layered nutrition supply comprises the following steps: deeply applying a calcium magnesium phosphate fertilizer to the lower layer of soil greater than 60cm, and covering a root controller; organic fertilizers and biological agents are applied to 30-60 cm soil of the middle layer; a macroelement, humic acid and seaweed extract water-soluble fertilizer is applied to the 0-30 cm soil of the upper layer; the flower and fruit protection management comprises the steps that ABA and paclobutrazol are sprayed in the flower bud differentiation period; brassinolide and fluid boron are sprayed in the full-bloom stage; spraying forchlorfenuron and amino acid in a young fruit stage; nAA and monopotassium phosphate are sprayed in the fruit swelling period. Layered nutrition supply and flower and fruit protection management are carried out on the densely-planted double-layer truncated cone macadamia nut trees, premature senility of the densely-planted macadamia nut trees is prevented, branch development of the lower layer is promoted, the tree vigor is balanced, and the high-yield level is maintained.

A method for managing an agricultural vehicle during harvesting process in a plantation of fruit trees, such as an orange orchard, the vehicle being shaped as a portal capable of moving over a crop row and provided with a couple of rotors (R) arranged to work simultaneously on both opposite sides of each plant of the crop row, the method including process of recognition of the trunks of the trees of a plantation to be worked by means of a first 3D sensor (3DS) system interfaced with processing means (CPU) of an agricultural vehicle (V), wherein the first 3D sensor (3DS) system includes a couple of 3D sensors arranged in a low portion of the agricultural vehicle at opposite sides of the vehicle oriented such that to converge in common point (P) circa on a vehicle center line axis (VC) in front of the vehicle, the process including fitting of pseudo-ellipsoids in a horizontal slice of a merged point cloud generated by the two 3D sensors, in order to identify trunks of the crop row.

The invention discloses a fruit tree disease and pest detection method and device based on improved YOLOv11, relates to the technical field of automatic machine learning, and aims to solve the problem that YOLOv11 in the prior art is still difficult in apple disease and pest detection. Taking the fruit tree shot image as input, and outputting based on the fruit tree pest detection model to obtain a fruit tree processing graph; and judging whether the fruit tree has diseases and pests and the types of the diseases and pests according to the fruit tree processing graph, wherein the fruit tree disease and pest detection model is obtained based on improvement of a YOLOv11 model. According to the method, advanced feature extraction, layered feature fusion and enhanced spatial perception functions are integrated, efficient and accurate detection of fruit tree diseases and insect pests is realized, the robustness and generalization ability of a detection model are improved, and meanwhile, relatively high performance is kept in a complex environment.

The invention discloses a multi-agent collaborative global coverage path planning method and system, and the method comprises the steps: carrying out the real-time data collection of a litchi orchard environment, and constructing a visual orchard real scene three-dimensional map; constructing a virtual orchard environment based on an orchard real scene three-dimensional map and an OpenAI Gym framework, and obtaining litchi fruit tree position information, a number point set, the number of robots and an initial position; based on a DDQN algorithm, an MA-DDQN algorithm is constructed; the MA-DDQN algorithm is trained, the shortest total path is selected, and path coordinate points of all robots are output; and based on the output global shortest path and the path coordinate point of each robot, driving each robot to complete a picking task along the respective picking path. According to the method, the learning efficiency of the algorithm and the precision of path planning are improved, and meanwhile, by designing the virtual orchard environment and dynamic data input, a plurality of intelligent agents can be trained and cooperatively work in the simulation environment, so that efficient path coverage and picking tasks are realized.

The invention relates to a variable-depth, variable-position and variable-quantity intelligent fertilizer applicator based on the size of a tree crown, and belongs to the technical field of agricultural and forestry machinery. The machine comprises a power device, a fertilizing device, a spiral ditching device, an intelligent identification device and an intelligent control system which are supported on a rack, wherein the rack is arranged on a walking device. The fertilizer applicator can realize integrated operation of intelligent ditching, fertilizer application and soil and fertilizer stirring in agricultural planting environments such as orchards and the like, and meanwhile, can intelligently find an optimal root zone position and adjust the ditching depth, the fertilizer application position and the fertilizer application amount according to the size of a fruit tree crown and the distance of a tree machine, so that the ditching efficiency and the fertilizer utilization rate are improved.

The invention provides a fruit tree canopy three-dimensional model reconstruction method and a fruit tree canopy three-dimensional model reconstruction system based on multi-modal data, and relates to the technical field of fruit tree canopy three-dimensional model reconstruction based on multi-modal data. Sub-pixel-level space registration is achieved by means of ground control points, voxelization division is carried out based on the range of a point cloud bounding box, and the number of point clouds is screened to construct an initial seed set; and adopting a structure-spectrum collaborative growth strategy, taking the adjacent voxels which are not accessed as candidate voxels, calculating a spectrum similarity mean value of the candidate voxels and directly adjacent voxels in the seed set, stopping growth when no new voxels are added in continuous three times of iteration, and outputting a final seed set. Meanwhile, the set is converted into a three-dimensional canopy model fusing space coordinates and full-wave-band spectral reflectivity, voxel-level pathological labeling is achieved based on the chlorophyll sensitivity index and the blue-red slope, and the whole process of reconstruction and analysis is completed.

The invention relates to the technical field of fruit tree pruning, in particular to an automatic fruit tree pruning system based on machine vision, which comprises a camera used for capturing fruit tree images and determining specific positions of fruits, branches and leaves. And the image processing module is used for identifying branch structures through an image processing technology and identifying branches and leaves needing to be pruned. And the decision making module is used for calculating a cutting path and a driving path of the trimmer and the driving vehicle from the current position to the target position according to a result of the image processing module. And the intelligent control module is used for controlling the trimmer and the driving vehicle to move to a target position to trim branches and leaves according to the information provided by the decision making module, and avoiding fruits and branches and leaves which do not need to be trimmed in the trimming process. By accurately identifying the branch structure of the fruit tree, the angle and force of the trimmer are adjusted in real time, fruits of the fruit tree and branches and leaves which do not need to be trimmed are avoided, trimming is more accurate, and the healthy growth state of the fruit tree can be maintained.

The invention relates to the technical field of intelligent agriculture, and particularly discloses an orchard intelligent water and fertilizer integration-based edge end fruit tree fertilizer demand prediction method, which comprises the following steps of: constructing each fruit tree as a graph node, and fusing canopy spatial characteristics extracted by unmanned aerial vehicle multispectral remote sensing and nutrient time sequence characteristics monitored by a soil sensor; space influence weights among nodes are calculated according to the terrain, the soil texture and the space distance, and a fruit tree individual relation graph is established; then, through a space-time diagram convolutional network, aggregating space neighborhood features and capturing time dynamic changes, and outputting target state features subjected to multi-scale enhancement; when the canopy conflicts with the soil characteristic indication, combined diagnosis is carried out based on a neighborhood consistency index and an adjacent node state, systematic nutrient stress and local non-nutrient factors are distinguished, and final fertilizer demand prediction is generated after the conflict is eliminated; a digital fertilization prescription map is generated through spatial interpolation, soil characteristics and slope correction, and the water and fertilizer integrated equipment is driven to execute precise operation.

The invention provides a forest-grain intercropping area fruit tree density extraction method based on remote sensing, and relates to the field of fruit tree density extraction. The method comprises the following main steps: firstly, based on an instance segmentation thought, utilizing a deep learning model to replace a traditional segmentation mode to extract a land parcel from an image with ultrahigh spatial resolution as a basic calculation unit; then, on the basis of a time sequence model, features of Sentinel-2 time sequence images are extracted to distinguish common farmland plots and forest-grain intercropping plots, and plot units for subsequent fruit tree density extraction tasks are determined; then, extracting spatial information features of the fruit tree from the GF-2 image based on a semantic segmentation model; and finally, designing a template filling strategy based on a semantic segmentation result, and obtaining the planting density of the fruit trees in the forest-grain intercropping area under the constraint of a land parcel result. The extraction method is high in accuracy, high in processing speed and good in demonstration application effect, and can provide support for scientifically adjusting the planting structure of the vegetation in the forest-grain intercropping area.

The invention relates to a variable-depth, variable-position and variable-quantity intelligent ditching fertilizer applicator based on the diameter of a fruit tree, and belongs to the technical field of agricultural and forestry machinery. The intelligent ditching and fertilizing machine comprises a rack arranged on a walking device, and a power device, a fertilizing device, a spiral ditching device and a control device which are supported on the rack, the fertilizing device is used for conveying the fertilizer from the fertilizer box to a feeding mechanism of the fertilizer discharging pipe according to the control instruction; the spiral ditching device comprises a vertical moving pair and a horizontal moving pair; the spiral ditching device comprises a vertical spiral ditching cutter which is arranged at the lower part of the movable platform and is driven by a spiral ditching driving motor; a visual camera and a laser radar are arranged in the middle of the rack, signal output ends of the visual camera and the laser radar are respectively connected with corresponding input end pins of the intelligent chip, and control output ends of the intelligent chip are respectively connected with controlled ends of the fertilizing motor, the moving motor, the spiral ditching motor and the lifting motor. The device can work at the position of an optimal root zone, the depth, the position and the variable are controlled, and after furrowing is conducted in soil, fertilizer is evenly scattered into furrows.

The invention discloses a juicy peach yield prediction method based on comprehensive data analysis, and particularly relates to the technical field of agricultural intelligent perception. The method comprises the following steps: collecting multi-source data of an orchard, and constructing a data set containing meteorological, physiological, remote sensing and soil information; fruit tree physiological response parameters are extracted, and a bimodal diagram structure fusing the spatial adjacency relation and the physiological state similarity is established in combination with the dynamic climate anomaly index; inputting the graph structure into a graph neural network model, extracting spatial-temporal characteristics, dynamically adjusting an edge weight and a propagation layer number, and constructing an adaptive model; performing region division and weighted summarization according to a model output result, and finally obtaining a predicted value of the total yield of the orchard; the method improves the prediction accuracy under the conditions of complex climate and unstable data, and is suitable for refined orchard management.

The invention discloses an intelligent nutrient circulation regulation and control method for a morchella esculenta fruit forest intercropping system based on the Internet of Things, particularly relates to the technical field of intelligent agriculture, and is used for solving the problem of symbiosis imbalance of hyphae and fruit trees caused by nutrient regulation and control lag and static threshold setting in the prior art. Hypha density, electric signal dominant frequency and root system absorption rate data are collected in real time through a multispectral impedance sensor and an ion selective electrode array, and a space-time aligned dynamic matrix is constructed; dynamically updating a nutrient balance threshold value based on a real-time ratio of a hypha metabolism rate to an absorption rate, and generating a nutrient transfer priority by combining collaborative analysis of a hypha transfer direction included angle and an electric signal main frequency offset gradient; a continuous transfer path is identified through a region growing algorithm, water and fertilizer amounts are distributed according to priorities, and a local area instruction is generated; and inverting the equipment response delay by using the high-frequency pulse signal, synchronously adjusting the execution frequency and updating the dynamic matrix and the threshold parameter, thereby realizing the dynamic adaptation and autonomous optimization of the nutrient supply and demand of the intercropping system.

The invention discloses an orchard shortest path planning method based on a laser radar SLAM map. The method is used for autonomous navigation of an orchard operation robot. Firstly, orchard three-dimensional point cloud data are collected by using a laser radar SLAM technology, and a high-precision map is constructed. Then carrying out downsampling and noise removal on the point cloud data, extracting ground point cloud and calculating gradient information; thirdly, segmenting the point cloud through a random forest model, distinguishing passing and non-passing areas, extracting the center coordinates of the fruit tree through an improved DBSCAN algorithm, and judging the row direction of the fruit tree through kernel density estimation; then, a cost map containing a gradient cost layer is constructed, and the path length, gradient information and obstacle distribution are comprehensively considered; an improved A * algorithm is adopted to introduce cost map information, a 16 adjacent mode is used to optimize path smoothness, a Bezier curve is used to further carry out local smoothing processing on the path, and a shortest path is generated. The method integrates multiple technologies, is suitable for a complex orchard environment, and has high robustness and applicability.

The invention discloses a multi-sensor fusion accurate spraying control system and method, and relates to the technical field of fruit tree planting, and the system comprises a data collection module, a fruit tree analysis module, a leaf analysis module, a pesticide analysis module and a pesticide spraying module. The environmental factors comprise wind power data and light data during pesticide spraying, the fruit tree data comprise distribution data of fruit tree leaves and fruit tree leaf image data, and the spraying equipment data comprise an equipment inclination angle and a distance between equipment and a fruit tree; according to the technical key points, in the spraying fertilization process, leaves on fruit trees are analyzed, healthy leaf values are calculated, whether the fruit trees are healthy or not is judged according to the healthy leaf values, a fruit tree treatment scheme is analyzed when the fruit trees are abnormal, spraying fertilization and pesticide spraying are combined, the using effect is good, and the good using prospect is achieved.

The invention discloses an intelligent fruit tree cultivation and planting integrated device and a method thereof, and relates to the field of seedling cultivation, the intelligent fruit tree cultivation and planting integrated device comprises a cultivation box, a spherical cultivation frame and two groups of processing mechanisms, the top end of the cultivation box is provided with a square cultivation groove, and the spherical cultivation frame is formed by butting two hemispherical frames; each processing mechanism is composed of a lifting assembly, a clamping assembly and an unfolding assembly, the outer wall of each hemispherical frame is connected with the lifting assembly through a guide rod and two connecting sliding blocks, the lifting assembly is arranged in the cultivation tank and used for controlling lifting of the spherical cultivation frame, and the clamping assembly is arranged at the top of the cultivation box and located on one side of the cultivation tank. In the seedling transplanting process, separation and lifting operation of the spherical cultivation frame is stable, adhesion of soil to root systems when seedlings are taken out of a planting box is effectively reduced, the risk of damage to the root systems is reduced, and the survival rate of the seedlings is remarkably increased.

The invention discloses an apple recognition positioning and obstacle avoidance method based on zero samples and visual guidance. An improved YOLOV8 model is used for recognizing unknown category objects in a picking scene; semantic embedding of apples and branches is constructed by combining an external knowledge base Word2Vec, and unknown category objects in a picking scene are identified by using a zero sample learning method; constructing three-dimensional point cloud data of a fruit tree where an identification result is located, carrying out iterative convergence point cloud denoising by adopting a convex set constrained by an alpha norm, and updating point cloud coordinates; performing point cloud segmentation by using a potential energy function segmentation model; obtaining three-dimensional information of the apple and the obstacle through three-dimensional reconstruction of the point cloud feature information; extracting three-dimensional space position data of the branches at different time points to form a time sequence data set, and predicting the motion trail of the branches by using LSTM (Long Short Term Memory); through branch coordinate data of the point cloud and the LSTM, predicting a motion track of the branch, and utilizing a geometric collision detection method to evaluate and predict a collision risk between a robot end effector and the branch; and planning an optimal obstacle avoidance path by using an A * algorithm.

The invention discloses an intelligent drip irrigation system for high mountain fruit planting, and relates to the technical field of agricultural planting, the intelligent drip irrigation system comprises a self-adaptive position regulation and control assembly, the top end of the self-adaptive position regulation and control assembly is fixedly connected with an edge connecting plate, and under the cooperation of the self-adaptive position regulation and control assembly, the edge connecting plate is fixedly connected with the self-adaptive position regulation and control assembly; the fine adjustment driver correspondingly adjusts the drip irrigation pipeline and the automatic telescopic pipe, it is guaranteed that water is accurately supplied to a fruit tree root system absorption area, a self-adaptive position control and fixing mechanism is integrally formed, and the problem that accurate drip irrigation cannot be achieved due to drip irrigation position change caused by terrain and environmental factors in a high mountain orchard is solved; meanwhile, under the cooperation of the position positioning and adjusting assembly and the self-fixing stable anti-offset assembly, the spatial position of the drip irrigation pipeline can be flexibly adjusted so as to better adapt to topographic relief and changes of fruit tree growth positions, stable support is provided for the whole drip irrigation system, and the service life of the drip irrigation system is prolonged. And system deviation caused by external force factors such as wind power and rain wash is prevented.

The invention discloses a method for positioning and navigating a closed orchard operation robot based on semantic segmentation, and the method is characterized in that the method comprises the following steps: installing a camera on the operation robot, and carrying out the continuous collection of a current fruit tree inter-row navigation path image when the operation robot runs; establishing a semantic segmentation model based on the improved Fast-Unet network, and solving a path center line equation, namely an inter-row navigation line equation; obtaining fruit tree row endpoints based on a normal method; when the vertical distance D of the fruit tree row end point straight line is smaller than 1 / 2 of the wheelbase of the operation robot, current inter-row navigation is ended; during line changing, the course of the operation robot is controlled to rotate by 90 degrees firstly, then the operation robot is controlled to linearly run by the distance of the diameter of a crown, then the operation robot is controlled to rotate by 90 degrees to enter fruit tree lines, and finally the navigation operation of the operation robot is controlled by repeating the line-to-line navigation method.

The invention discloses a UWB navigation system and method based on visual feature fusion, the system comprises a UWB positioning module, a visual perception module, a data fusion processing module and a navigation control module, the UWB positioning module adaptively generates UWB anchor point layout, calculates UWB coordinates in real time, and then obtains UWB positioning data according to fruit tree coverage rate and soil humidity calibration; the visual perception module collects orchard images to recognize fruit trees and obstacles, obtains visual coordinates and corrects the visual coordinates to obtain visual positioning data; the data fusion processing module calculates weights of the UWB positioning data and the visual positioning data, fuses the UWB positioning data and the visual positioning data according to the weights to obtain fusion coordinates, and performs error correction to obtain optimal coordinates; and the navigation control module calculates a target path point according to the optimal coordinate, the obstacle recognition result and the weeding operation path, and controls the weeding robot to run to the target path point. According to the invention, high-precision navigation of the weeding robot in a complex orchard environment is realized.

The utility model discloses a fruit tree fertilizing device, which belongs to the technical field of fruit tree fertilizing devices and comprises a cart, and hub components are mounted at two ends of the bottom of the cart respectively. A bracket is mounted at one end of the trolley, and a positive and negative ball screw frame is arranged on the bracket; the two ends of the positive and negative ball screw frame are respectively connected with a fertilizer applying frame through a screw nut pair, and the fertilizer applying frame is semicircular; a plurality of groups of drainage holes are formed in the bottom end of the fertilizing frame at equal intervals. When the two fertilizing frames are close to each other and combined, the whole fertilizing frame is approximately circular, the purpose of fertilizing around the root of a fruit tree is achieved through uniform distribution of the liquid drainage holes, the situation that fertilizer is locally accumulated or intensively applied to a certain area is avoided, and an operator can complete the whole fertilizing operation without adjusting the fertilizing position or direction for many times; and time and labor cost are saved.

The invention relates to the technical field of agricultural machinery, in particular to a multifunctional fruit huller. Comprising a case, and an eccentric rolling and screening device, a spiral screening and conveying device, a drying device and a collecting device which are mounted in the case from top to bottom; the eccentric rolling and screening device is provided with an eccentric rolling roller and a screening barrel and is used for rolling and screening fruits, then the fruits are conveyed to the drying device through the spiral screening and conveying device, and the drying device is used for drying the seeds and conveying the seeds into the collecting device; the motor and the transmission device are installed in the machine box, and the motor drives the eccentric rolling and screening device and the spiral screening and conveying device to operate through the transmission device. The device can be conveniently carried to mountainous areas where fruit trees live, fruits can be hulled on site after being picked, transportation is convenient, and the hulled seeds can be dried.

The utility model discloses a plant protection unmanned aerial vehicle for spraying pesticide on fruit trees, which comprises a shell, a GPS (global positioning system) is connected onto the shell, the GPS is used for positioning the unmanned aerial vehicle, and propellers are connected onto the shell; the undercarriage is fixedly connected to the machine shell; the pesticide box is arranged at the bottom of the machine shell, the pesticide box is fixedly connected with the machine shell, and a liquid inlet is formed in one side of the pesticide box; the spraying assembly is arranged at the bottom of the pesticide box, the spraying assembly is used for spraying pesticide liquid in the pesticide box outwards, and the spraying assembly at the bottom of the pesticide box is aligned with the side faces and the bottom of a fruit tree to conduct pesticide liquid spraying operation in the process that the unmanned aerial vehicle lifts off and flies; in the using process, the spraying rod pump sprays pesticide liquid in the pesticide box outwards through the spraying head after passing through the spraying rod, the spraying head is arranged on the side wall of the spraying rod so that the pesticide liquid can be conveniently sprayed to the side wall of a fruit tree through the spraying head, and in the flying process of the unmanned aerial vehicle, the unmanned aerial vehicle is suspended, and spraying operation can be completed.

The invention relates to the technical field of agricultural mechanical equipment, in particular to an agricultural planting fertilizer mixing and fertilizing device which comprises a rack, a fixing plate and the like. Two grips are arranged on the rack; the rack is rotationally connected with a roller; a fixing plate is rotationally connected to the rack. According to the device, the mud scraping plate drives the cutting ring to rotate while the connecting pipe drives the mud scraping plate to rotate, so that root systems at the edge of a fertilization hole are cut off through the sawteeth, then drilling operation is performed through the spiral auger, the spiral auger is prevented from pulling the root systems of fruit trees, and the yield of the fruit trees is improved. After the soil and the fertilizer are completely pushed into the fertilization hole through the spiral auger, the cutting cylinder moves downwards, and the edge of the fertilization hole is cut through the sharp edge on the lower side of the cutting cylinder, so that the root system at the edge of the fertilization hole is further cut, the flatness of the section of the root system is ensured, and the healing and regeneration of the root system are facilitated.