2053 results about "Fruit tree" patented technology

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.

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 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 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 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 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 invention discloses a transplanting device and method for fruit tree cultivation, and belongs to the field of fruit tree transplanting. A transplanting device for fruit tree cultivation comprises a cart body and a loading slide way fixed to the top of the cart body, a driving shaft is rotationally connected to the cart body, a pit digging auger is fixedly connected to the bottom end of the driving shaft, the transplanting device further comprises two sets of semi-arc bins, and the two sets of semi-arc bins are rotationally connected to the two sides of the bottom of the cart body respectively; the opening and closing motor is used for controlling the two groups of semi-arc bins to be opened, in cooperation with the repulsion effect between the magnetic plate and the piston plate, firstly, rooting liquid in the liquid storage box is sprayed to the roots of fruit trees, so that the final survival rate of fruit tree transplanting is increased, and secondly, the fruit trees automatically fall into transplanting pits, so that the survival rate of fruit tree transplanting is increased. Meanwhile, the improved soil can automatically fall into the transplanting pit, so that automatic spraying of the rooting liquid and automatic backfilling of the soil are realized, the transplanting steps are simplified, and the transplanting efficiency is greatly improved.

The invention relates to the technical field of image processing, and discloses a pitaya fruit pose estimation method based on point cloud three-dimensional reconstruction. The pitaya fruit pose estimation method comprises the steps that RGB color images and depth images of pitaya fruits are collected, expanded and marked; the DeepLabV3 + network model is improved and trained, and then an RGB color image is segmented; segmenting the depth image by using the semantic segmentation mask image, then matching the depth image with the RGB color image to obtain a pitaya tree point cloud, and preprocessing the pitaya tree point cloud; registering the pitaya fruit tree point cloud by using an improved point cloud registration algorithm to obtain a fruit tree three-dimensional point cloud model, and segmenting fruit local point clouds from the fruit tree three-dimensional point cloud model; establishing a pitaya fruit point cloud coordinate system by using a PCA principal component analysis method; and performing spherical fitting and ellipsoid fitting on the pitaya fruit point cloud coordinate system to obtain an ellipsoid model with unique pose parameters. According to the invention, a fruit tree three-dimensional point cloud model and accurate fruit three-dimensional space pose information can be provided for a pitaya fruit picking system.

The invention discloses a lemon fruit tree semantic segmentation method and system based on unmanned aerial vehicle laser radar and transfer learning, a storage medium and a computer device, and the method comprises the following steps: carrying out the data collection of a lemon orchard through employing a laser radar scanning device based on an unmanned aerial vehicle; processing the acquired data by adopting an instant positioning and map building algorithm so as to build a real-time three-dimensional point cloud map of the lemon orchard; constructing a neural network model, optimizing the neural model by adopting a deep learning and transfer learning method, and performing real-time semantic segmentation on the three-dimensional point cloud map of the lemon orchard by using the optimized neural network model; based on the semantic segmentation result, lemon fruit tree point cloud and environment point cloud are separated, and corresponding geometric parameters of the lemon fruit tree are calculated and extracted. According to the method, the accurate three-dimensional point cloud map can be reconstructed, the lemon fruit tree point cloud and the three-dimensional geometric parameters thereof in the three-dimensional point cloud map can be extracted, and accurate data support is provided for various subsequent agricultural researches.

According to the forest and fruit tree body profiling flapping fruit harvester based on the special-shaped crankshaft structure, transmission is achieved through special-shaped crankshafts with different crank throw eccentricity degrees, and the movement amplitude of flapping strips is limited through the inner side edges of amplitude limiting plates; the purpose that the flapping strips are arranged in a manner of imitating the contour of a tree crown can be achieved while the flapping strips are flapped, the flapping strips are arranged in a shape of imitating a tree shape, missed flapping of the top of the tree crown and the pole position of the symmetry axis is avoided, and the net picking rate of different positions is increased. The device comprises a chassis vehicle body and a tree-like flapping device, in the tree-like flapping device, a crankshaft with a plurality of crank throws is rotationally arranged on a base, and the eccentric distances of the crank throws are different; the lower end of each beating strip is hinged to a crank throw of the crankshaft; opposite amplitude limiting plates are arranged on the higher parts of the two sides of the base, and an amplitude limiting hole is formed between the opposite inner side edges of the two amplitude limiting plates; the distance between the inner side edges of the two amplitude limiting plates close to the center of the amplitude limiting plate in the direction parallel to the axial direction of the crankshaft is smaller than the distance between the inner side edges of the two amplitude limiting plates close to the two ends of the amplitude limiting plate.

The invention relates to the technical field of navel orange tree cultivation and physiological regulation and control, and discloses an ethylene regulation and control-based low-temperature dry-water prevention and control method for late-maturing navel oranges before harvest. According to the method, the dry-water rate of the navel oranges in a complex environment is reduced through multi-dimensional accurate regulation and control, and the ethylene inhibitor spraying time is flexibly adjusted according to the climate of a planting field and the fruit development process; the ethylene synthesis can be intervened at the optimal opportunity; the method comprises the following steps: preparing a solution according to a formula, scientifically grouping navel orange trees with consistent growth vigor, accurately spraying inhibitors with two concentrations, ensuring that the agent uniformly acts on fruits and leaves, dynamically adjusting the spraying frequency according to rainfall conditions, avoiding agent loss, and after spraying, performing fertilization and watering in combination with a fruit tree phenological period fertilizer requirement rule and soil moisture content. A good growth environment is created for navel orange trees, the control effect is verified and the scheme is optimized through data detection and record analysis of the system after fruits are picked, and the low water rate of the navel oranges under different severe conditions can be reduced by combining the steps, so that the fruit quality and yield are guaranteed.

According to the forest and fruit tree body profiling flapping fruit harvester based on tree form recognition, the flapping action of the flapping strips is achieved through rotation of the crankshaft and push-pull of the piston rod of the air cylinder, different portions of a tree crown can be effectively beaten, missed flapping of the top of the tree crown and the pole portion of the symmetry axis can be avoided, the net picking rate is uniform, and the automation degree is high. The device comprises a chassis vehicle body and a tree-like flapping device, the tree-like flapping device comprises a base arranged on the chassis vehicle body, at least two bearing seats are arranged on the base, a crankshaft with a plurality of crank throws is connected to the bearing seats, a driving device for driving the crankshaft to rotate relative to the bearing seats is arranged on the base, and the lower end of each flapping strip is hinged to one crank throw of the crankshaft. Each sleeve is arranged on the corresponding beating strip in a sliding mode, a plurality of air cylinders arranged in the axial direction of the crankshaft are arranged on the base, and the end of a piston rod of each air cylinder is hinged to the corresponding sleeve.

The invention relates to an Orah fruit tree plot classification method in a multi-age mixed planting mode, and belongs to the technical field of agricultural remote sensing and image recognition. In order to solve the technical problem of difficulty in classification of citrus reiculata Blanco fruit tree plots in a multi-age mixed planting mode, remote sensing images of citrus reiculata Blanco planting areas are acquired through remote sensing of an unmanned aerial vehicle, and a multi-category data set including seedling stages, different tree ages and lime pesticide sprayed plots is constructed after preprocessing; a channel perception lightweight attention module (CLA) and a gating axial space attention module (GASA) are designed, a GACL-DeepLabV3 + model is constructed, the CLA is utilized to guide a key area to respond, spatial feature fusion is enhanced through the GASA, and fine classification of land parcels is realized. The method is mainly used for fine management of the citrus reiculata orchard, provides tree age distribution information for fruit farmers, assists in formulating differentiated management strategies such as water and fertilizer regulation and disease and pest early warning, and improves orchard monitoring efficiency and yield quality.

The invention discloses a soil moisture content prediction method and device based on deep learning, and the method comprises the steps: carrying out the seasonal trend decomposition of historical soil moisture content time sequence data, and obtaining a trend term, a seasonal term and a residual term; secondly, analyzing a causal relationship between each decomposition item and a meteorological variable in combination with a plurality of causal test methods, and constructing a dynamic causal adjacency matrix to obtain a causal tree structure; for the trend item, the season item and the residual item, designing a causal LSTM sub-model according to the fruit tree structure, inputting a historical decomposition sequence and the screened causal meteorological variables, and carrying out independent prediction; and finally, fusing prediction results of the sub-models through a full connection layer, and outputting a final prediction value of the soil moisture content in a future period. The innovation point of the method is that causal information and time dependence characteristics of decomposition items are fused, and the interpretability of the model and the response capability of the model to long-term trend and short-term mutation are enhanced.

The invention relates to the technical field of agricultural intellectualization, and discloses a fruit tree growth monitoring system based on multi-sensor fusion, which comprises a multi-mode sensor array, an integrated soil humidity sensor, a three-dimensional ultrasonic ranging sensor, a hyperspectral imaging sensor and a microenvironment meteorological station, and realizes multi-parameter synchronous acquisition; a nonlinear data calibration model adopts a layered architecture, and cross interference between sensors is eliminated; a dynamic weight distribution algorithm is built in the edge computing node, multi-source heterogeneous data are fused in real time, a fruit tree growth situation map is generated, and a low-power-consumption processor is adopted to support lightweight AI model deployment; the self-adaptive power supply module adopts a photovoltaic-temperature difference composite power generation structure to realize cable-free deployment; the wireless ad hoc network communication unit constructs an inter-tree relay network based on a LoRa protocol, supports low-power-consumption remote data return, and integrates a relay node health degree evaluation mechanism. The real-time performance of fruit tree growth monitoring, the data reliability and the situation analysis accuracy are improved.

The invention relates to a fruit tree growth phenotype dynamic acquisition method and system based on multi-modal perception fusion. Belongs to the field of fruit tree growth detection, and establishes a fruit tree whole growth period growth dynamic knowledge graph by fusing structure and growth phenotype data of fruit tree individuals and groups collected by laser radar, machine vision, remote sensing and other multi-source sensors to realize space-time modeling and automatic identification of tree body structures, growth states and key growth periods. Knowledge reasoning and decision-making algorithms are further combined, and intelligent management services such as fruit tree nutrition diagnosis, pruning suggestion and yield prediction are provided. The fruit tree phenotype data acquisition, analysis and utilization efficiency is improved, refined, intelligent and digital management of fruit trees is realized, and the method can be widely applied to the field of modern orchard digital twinning and intelligent agriculture.

The invention discloses a pruning analysis method and system based on deep learning, and the method comprises the following steps: S1, collecting images of fruit tree branches through an image collection device, uploading the image data to a cloud end, and constructing three-dimensional positions between the fruit tree branches at the cloud end; s2, compensating image offset caused in the shooting process, and correcting image blurring; s3, extracting key features in the images according to the obtained fruit tree branch images, comparing the key features with historical data, and matching the key features with the historical data to obtain historical data with the same conditions; S4, predicting branch development conditions after different pruning modes, predicting subsequent fruit generation conditions, and determining a pruning strategy. According to the pruning analysis method and system based on deep learning provided by the invention, the problems existing in the fruit tree branches in the image can be found, and the corresponding pruning mode is given, so that the pruning difficulty is reduced, and according to the pruning mode given by the system, people lacking experience can also achieve the pruning effect of professional fruit farmers.

Provided are a method, apparatus, electronic device, and storage medium for monitoring a nitrogen demand of a fruit tree. The method includes acquiring plant input parameters of a fruit tree to be monitored, where the plant input parameters at least include a phenological development accumulated temperature parameter, a green leaf parameter, a photosynthesis parameter, a respiration parameter, a dry matter accumulation parameter, a partitioning parameter, and a nitrogen transport parameter. A target monitoring model is constructed, where the target monitoring model is a WOrld FOod Studies (WOFOST) model integrating nitrogen monitoring. The plant input parameters of the fruit tree to be monitored are input into the target monitoring model to obtain a nitrogen demand of the fruit tree to be monitored that is output by the target monitoring model.

The invention relates to the technical field of fruit tree disease detection, in particular to a fruit tree disease detection method based on image recognition, and the method comprises the steps: collecting images of leaves, fruits and branches of a fruit tree, and corresponding environment and physiological data; image features are extracted through a convolutional neural network, and time sequence modeling is carried out on environment and physiological data; inputting the multi-source features into a fusion network for weighted fusion to generate a fusion feature vector; inputting a disease recognition model, and outputting a multi-disease recognition result; when the confidence coefficient is low or feature conflicts exist, auxiliary judgment and correction are carried out in combination with environment and physiological data; outputting the corrected disease type, the disease spot position and the confidence coefficient, and generating a distribution diagram and a detection report; and marking a low-confidence or unidentified sample as an incremental sample, checking and storing the incremental sample, and performing online fine adjustment or transfer learning on the model based on the incremental sample to realize adaptive optimization of the model.

The invention relates to the technical field of picking machines, and provides a fruit cutting device which comprises an arc-shaped supporting body, an arc-shaped track, a cutting mechanism and a driving mechanism. An arc-shaped receding groove communicating with the arc-shaped movable channel is formed in the inner side wall of the arc-shaped supporting body in the circumferential direction, and a clamping opening is formed between the two ends of the arc-shaped supporting body. The arc-shaped track is assembled in the arc-shaped movable channel in a sliding manner; cutting mechanisms are arranged above the two ends of the arc-shaped supporting body and connected with the arc-shaped track through first connecting pieces. And the driving mechanism is arranged between the arc-shaped track and the arc-shaped supporting body. The fruit tree picking and cutting device has the advantages that the driving mechanism can be used for driving the arc-shaped track to drive the two cutting mechanisms to move in the circumferential direction, and by means of mutual cooperation of the two cutting mechanisms, it can be ensured that the periphery of a fruit tree can be covered by 360 degrees to achieve dead-corner-free picking and cutting.

The utility model relates to the technical field of plant grafting, and discloses a fruit tree grafting auxiliary device which comprises a semi-ring plate and an auxiliary plate, pressure plates are arranged in the semi-ring plate and the auxiliary plate, first springs are fixedly connected between the semi-ring plate and the pressure plates and between the auxiliary plate and the pressure plates, first clamping grooves are formed in the semi-ring plate and the auxiliary plate, and the first clamping grooves and the second clamping grooves are formed in the semi-ring plate and the auxiliary plate. Son buckles are arranged in the semi-ring plate and the auxiliary plate, the side walls of the son buckles are fixedly connected into the first clamping grooves, and second clamping grooves are formed in the semi-ring plate and the auxiliary plate. According to the device, the son buckle and the mother buckle are clamped through pressure, the two semi-ring plates are closed, the internal pressure plate extrudes and fixes branches at the grafting position, the first spring has elasticity and can adapt to branches with different thicknesses, the auxiliary plate further fixes the branches outside the grafting position, the grafted branches are conveniently fixed, and the stability and the tree survival rate are improved.

A picking robot, a fruit positioning method and apparatus therefor, an electronic device, and a medium are provided. The robot includes a robot body including a processor and a plurality of robotic arms. A plurality of first image collection modules and one second image collection module are mounted on the robot body. Each robotic arm has one corresponding first image collection module mounted nearby. Each first image collection module does not interfere with the corresponding robotic arm. The second image collection module is mounted at a position of a base of the robot body. The processor is configured to determine, based on fruit tree images of respective sub-areas in an operation area that are collected by respective first image collection modules and the base coordinate system, global fruit positioning distribution information of the operation area, and control the robotic arms to perform collaborative operation.

The invention provides an evaluation method for multi-angle telemetry of fruit tree canopy fruit quality, and solves the important problem of multi-scale in-situ precise calibration. Non-coherent light far-field partial coherence characteristics and different-angle telemetering spectrums are greatly different. The typical environment of an orchard in a to-be-measured area is selected by combining satellite remote sensing images and a ground interactive verification experiment, illumination and a temperature field are gridded in a spatial orientation, the reflectivity change rule of the same fruit in different angle polarization directions through multispectral and multi-angle polarization is utilized, and the multi-angle polarization spectrum morphological parameters are utilized to determine the orchard in the to-be-measured area. And fitting a multi-angle polarization reflectivity curve model. Spectrum quantitative detection and multi-angle unmanned aerial vehicle spectrum image area evaluation are combined through multiple times of calibration of a spectrograph detection angle and a spectrum camera field angle. A spectrograph detects and reconstructs a fruit canopy spectral image from multiple angles, and in-situ calibration and regional quality monitoring and evaluation of fruit tree canopy fruit quality are achieved.

The invention relates to the technical field of state recognition, in particular to an apple tree growth state intelligent recognition system which comprises a branch structure recognition module, a direction sequence calculation module, a growth stability judgment module, a fruit trend steering module and a vector trajectory offset module. According to the method, the continuous change characteristics of the branch direction in the time sequence dimension can be identified by constructing the inter-node main axis direction vector sequence and quantifying the change trend of the included angle of the inter-node main axis direction vector sequence, synchronous detection of the fruit diameter growth rate and the color difference change trend is combined, and the reflectivity and morphological symmetry linkage change is introduced as the judgment condition of the trend turning point; according to the method, fruit growth mutation nodes are effectively positioned, a growth trajectory sequence is constructed through multi-period branch angle amplification, diameter growth rate and leaf area index three-dimensional vectors, an individual growth direction offset region is accurately identified, and the precision and timeliness of growth anomaly identification are enhanced. And intelligent fruit tree management implementation based on the linkage relationship between the structure and the fruit parameters is effectively supported.

The invention discloses beneficial fusarium oxysporum and application thereof in relieving apple continuous cropping obstacles, and belongs to the technical field of agricultural microorganisms. The biological preservation number of the fusarium oxysporum FoB1 disclosed by the invention is CGMCC (China General Microbiological Culture Collection Center) NO.42195. The fusarium oxysporum FoB1 disclosed by the invention is beneficial fusarium oxysporum, and has the advantages of excellent biocontrol function, strong environmental adaptability and high affinity with a host. By optimizing the preparation process of the microbial inoculant, the activity and stability of the inoculant are remarkably improved, and a high-quality bacterium source is provided for large-scale production. On the basis, a simple and convenient bacterium-bearing nursery stock application technology is developed, a novel efficient biological prevention and control way is provided for relieving apple continuous cropping obstacles, and important technical support is provided for promoting green and sustainable development of the fruit tree industry.

The invention discloses a fruit tree natural vibration characteristic identification and excitation regulation and control method based on vision. Belongs to the technical field of mechanical fruit picking. The method comprises the four steps of video acquisition, fruit natural vibration frequency information extraction based on machine vision, fruit tree structure parameter extraction based on machine vision, and optimization of the optimal force bearing point of the fruit tree based on space finite element analysis and a physical information deep neural network. Wherein the extraction of the fruit tree structure parameters based on the machine vision is to perform target detection and instance segmentation by using a Mask R-CNN algorithm, reinforce image details in combination with a Canny edge detection technology, and accurately extract geometric parameters such as the crown area, the slenderness ratio of branches and a trunk. According to the method, multiple independent modes of the fruit tree can be accurately separated, and the natural vibration frequency of the fruit can be accurately extracted. According to the innovative scheme, the limitation of a traditional method is broken through, and an efficient, low-cost and high-adaptability technical path is provided for intelligent agriculture and precise fruit tree management.

The invention discloses a dual-mode orchard gas explosion subsoiler, and belongs to the technical field of agricultural mechanical devices. Comprising a moving platform, an electric telescopic arm device, an air generating device, an air conveying device, a handheld operating device, a pedal device, an angle adjusting device, a self-closed double-layer pneumatic deep scarification device and a self-closed soil penetrating device. The electric telescopic arm can move up and down and stretch out and draw back to adapt to plant spacing, and automatic deep scarification operation is achieved at a proper position; in an area where the terrain is complex and the platform is difficult to enter, the handheld operating device can be detached from the mobile platform and manually operated by an operator, and the subsoiling operation position is flexibly selected; the self-closed double-layer pneumatic deep scarification device enables the air holes to coincide by adjusting the angle of the inner-layer air rod, soil can be prevented from entering the air channels in the soil penetrating process, and blockage caused by the fact that the soil enters the air channels is effectively prevented; the fruit tree deep scarification device has portability and high efficiency, can cover the deep scarification requirement around fruit trees, and prevents soil from entering an air passage.

The utility model provides a picking machine for driving fruit trees to do elliptical reciprocating motion, and belongs to the field of agricultural machinery. The picking machine comprises a machine body, a reversing eccentric rotation mechanism and a clamping mechanism; the reversing deflection rotation mechanism is fixed to the machine body and provided with a first input part and a first output part, the input force direction of the first input part is perpendicular to the output force direction of the first output part, and the first input part is connected with an external driving device. One end of the clamping mechanism is rotationally connected with the first output part and does circular motion with the rotating center of the first output part as the circle center, and the other end of the clamping mechanism is connected with an operation fruit tree. According to the picking machine, rigid excitation is achieved through the simplified reversing deflection rotating mechanism and the clamping mechanism, the operation fruit trees are driven by the deflection rotating vibration mechanism to do elliptical reciprocating motion, the fruit falling rate of the operation fruit trees, especially transverse branches, is remarkably increased, the optimal operation efficiency can be achieved with the minimum energy consumption, and the labor intensity of workers is lowered. The device has the advantages of compact structure, low cost, reliable operation, low failure rate and the like.