1540 results about "Orchard" patented technology

The invention relates to the technical field of orchard unmanned vehicles, in particular to an orchard unmanned vehicle intelligent navigation and control method based on multi-sensor fusion, and the method comprises the following steps: S1, global path planning: generating discrete plane path points, and then pre-fitting a prior navigation global path; s2, three-dimensional environment perception: extracting static obstacle and dynamic obstacle information in a working path in real time, detecting and identifying a target obstacle at the same time, and performing sensor information fusion; s3, path tracking control: controlling the orchard unmanned vehicle to run according to a priori navigation global path; and S4, dynamic path optimization: realizing dynamic planning of an obstacle avoidance trajectory, synchronously establishing a trajectory overlap ratio evaluation mechanism, and adjusting a driving mode to avoid the obstacle. According to the invention, the reliability of environment perception and path dynamic adjustment is improved, and the three-dimensional motion control model is constructed, so that the unmanned vehicle can safely and accurately complete autonomous navigation work.

The invention discloses a multi-target fruit tracking detection and counting method based on a complex orchard. The method comprises the following steps: acquiring a to-be-detected video sequence; the to-be-detected video sequence is input to a fruit detection model, a detection result is obtained, the fruit detection model is constructed through an improved YOLOv8n network model and is obtained through training of a training set, and the training set is fruit image data; inputting the fruit image data and the detection result into a trajectory prediction model to obtain a prediction result of the fruit, the trajectory prediction model being obtained by introducing a dynamic Kalman filtering algorithm of a variable forgetting factor; and performing data association on the detection result and the prediction result to obtain fruit position information and a counting result. According to the invention, the prediction precision can be further improved, noise accumulation and prediction errors are reduced, and continuous tracking of fruits in a video sequence is realized.

The invention relates to the technical field of orchard automatic navigation deviation correction, in particular to an automatic navigation deviation correction system for an orchard track power platform. Comprising a chassis, a vehicle control unit, a navigation system vehicle-mounted computer, a laser radar, a navigation control box, a navigation antenna, a steering motor, a differential planetary mechanism, a rotating speed sensor, an edge architecture control box, a driving wheel assembly, a servo valve and a steering pump. The mode that the whole vehicle controller regulates and controls the servo valve to conduct deviation rectification is adopted, the deviation rectification response speed is high, the control precision is high, the high-speed operation working condition of the orchard power platform can be adapted, and the operation efficiency is improved; the swash plate plunger steering pump is adopted to drive the steering motor, the driving power is small, the heat balance stability is good, and the cost is low; the differential steering mechanism is adopted as a deviation rectifying executing mechanism, the transmission efficiency of the power platform is not affected, the steering line type is controllable, steering is stable, the overcorrection phenomenon is effectively avoided, the automatic navigation operation precision is effectively improved, and the automatic navigation deviation rectifying stability of the orchard power platform is improved.

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 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 discloses a bird identification method based on sound-image multi-modal fusion. The bird identification method comprises the following steps: S1, carrying out standardized frame-level preprocessing on bird audio signals; s2, acoustic features are extracted and enhanced, and an acoustic high-level feature vector which highlights birdsong discrimination information and suppresses environmental noise is obtained; s3, visual image standardization preprocessing; s4, performing visual feature extraction and multi-scale fusion to obtain a visual high-level feature vector which enhances correspondence to the bird key form area and inhibits background interference; s5, performing dynamic weighted fusion on the decision-making layer to obtain a bird existence probability; and S6, comparing the bird existence probability with a preset threshold value of the corresponding bird, and judging whether the bird exists or not and the type of the existing bird. Through cross-modal feature enhancement and adaptive fusion, the precision, robustness and real-time performance of bird recognition in a complex orchard environment are significantly improved, and a core technical support is provided for green intelligent bird repelling.

The invention provides an artificial intelligence network model-based early warning and monitoring method for the occurrence amount of agricultural pests, and particularly relates to a time sequence modeling method by combining a variable structure bus module VSB with a bidirectional long short-term memory network BiLSTM, which is used for predicting the occurrence dynamic state of important pests in a field and an orchard with high precision. Comprising the following steps: selecting three monitoring sites in a main crop producing area; and establishing a time sequence data set of the corresponding relationship between the average daily temperature, the rainfall and the effective accumulated temperature and the number of pests in ten days. According to the method, a hybrid neural network prediction model is constructed, the model comprises five function modules, and the model can accurately early warn annual dynamic changes of main crop main pest populations and judge peak values, and helps farmers establish efficient pest prevention and control measures.

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 YOLOv5-assisted vision-laser integrated efficient bird repelling method and device, and the method comprises the steps: obtaining the video stream data of a monitoring region in real time, inputting the video data into a multi-modal target detection model, extracting a key frame containing a target bird through the pre-trained multi-modal target detection model, and carrying out the recognition of the key frame, wherein the multi-modal target detection model is constructed by a YOLOv5 network structure; performing species feature analysis on the key frame; acquiring position coordinates after accurately identifying bird types; generating a dynamic control instruction based on the positioning data; the laser bird repelling module is driven to emit green light with the wavelength of 532 nm to optically repel birds; the system is integrated with a closed-loop feedback mechanism, and the bird repelling effect is verified through secondary image acquisition. The method can be applied to key fields such as airports, orchards, photovoltaic power plants and wind power plants, can also be expanded to the fields of ecological monitoring and agriculture, and has a good application prospect.

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 a tangelo ecological orchard data management platform, relates to the technical field of agricultural informatization and intelligent management, is used for solving the problem that decision-making efficiency is affected by data delay, and realizes real-time acquisition and classified storage of orchard environment data by arranging a sensor node network and a distributed storage system. Through data preprocessing and feature extraction, the data redundancy is reduced, and the analysis efficiency is improved; an orchard environment change trend is predicted by using a time sequence analysis algorithm, and a scientific basis is provided for resource allocation; the generation of a dynamic resource allocation scheme is realized by introducing an adaptive weight allocation mechanism and a resource priority index; and an interaction module is deployed at the orchard management terminal, so that a user can check data and adjust resource allocation in real time, and the intelligent level of orchard management is improved.

The invention discloses a path planning and dynamic scheduling method for multiple plant protection vehicles in an orchard. The method comprises the following steps: S1, generating an operation base map from aerial photography data of an unmanned aerial vehicle through an operation grid base map generation and operation point drawing module; s2, monitoring insect pests in real time through an insect pest monitoring module, and transmitting monitored insect pest point coordinates to a dispatching center; s3, calling an operation grid base map generation and operation point drawing module to draw operation points; s4, a path planning module performs path planning prediction on the N plant protection vehicles; s5, the N plant protection vehicles receive the operation path information and start to execute operation; s6, in the operation execution process, an obstacle avoidance scheme is provided through a vehicle-mounted local path planning module; and S7, new tasks are allocated to the plant protection vehicles completing the operation through the dynamic scheduling module, and when fault information occurs, the uncompleted tasks are allocated to the nearby plant protection vehicles until all the tasks are completed. According to the invention, path planning and dynamic scheduling of multiple plant protection vehicles in an orchard can be accurately and efficiently completed.

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 fruit target detection method based on a lightweight multi-scale attention mechanism, and relates to the field of computer vision, deep learning and intelligent agriculture fusion. The method comprises the following steps: firstly, by taking RetinaNet as a framework, constructing an LMSA-ReNet model by adopting a lightweight backbone network ShuffleNetV2 in combination with an improved multi-scale attention mechanism; and training the constructed LMSA-ReNet model by introducing a knowledge distillation mechanism and a structure sparse regular strategy, and finally performing fruit target detection in an orchard by using the trained model. Compared with other methods, the method can reduce the calculation cost of the model, improves the detection precision of the fruit target, and achieves the rapid target detection in a complex actual environment of an orchard.

The invention discloses a fertilization method for promoting sugar increase and acid reduction of ponkan and a water and fertilizer integrated system, and relates to the technical field of ponkan planting, and the fertilization method comprises the following steps: installing the water and fertilizer integrated system, arranging a 100m < 3 > water pool and a 30m < 2 >-50m < 2 > water and fertilizer pressure pump room at the highest position, close to a water source, of each 100mu of orchard, and controlling the annual fertilization frequency to be one time of base fertilizer and three times of topdressing, the application time of the base fertilizer is from late February to early March, two strip-shaped application ditches with the width of 0.4 m, the depth of 0.4 m and the length of 1 m are dug along a water drip of a tree crown, 1.5 kg of bio-organic fertilizer is used for each plant, the content of organic matter in the bio-organic fertilizer is not smaller than 40%, the content of N + P2O5 + K2O is not smaller than 5%, 1 kg of compound fertilizer is used for each plant, the content of N + P2O5 + K2O in the compound fertilizer is not smaller than 45%, and the content of N is 15%, the content of P2O5 is 15%, and the content of K2O is 15%. The quality of fruits in the marketing period is improved, the price of the fruits can be remarkably increased, the sales pressure can be remarkably reduced, and better benefits are brought to fruit farmers.

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 relates to a smart orchard water and fertilizer intelligent management method and system, and the method comprises the steps: obtaining orchard environment monitoring data with a collection timestamp; performing space-time alignment on the precipitation and the soil humidity to obtain precipitation monitoring data and soil humidity monitoring data; acquiring target soil humidity of crops and determining irrigation volume, and distributing irrigation time according to parameters of irrigation equipment; and determining the fertilization amount according to the target yield, the target unit yield nutrient absorption amount and the soil available nutrients. The problems of resource waste and soil degradation caused by extensive traditional water and fertilizer management can be solved.

The invention relates to the technical field of orchard meteorological disaster prevention and reduction frost prevention, and provides a surface layer disturbance temperature inversion frost prevention method. According to the method, the frost rate of the orchard can be effectively reduced and the frost loss can be reduced through the processes of constructing the orchard air temperature prediction model, determining the frost disaster threshold value, installing fan pipe tower equipment in the orchard, collecting temperature data in real time based on a sensor network of an Internet of Things monitoring system and optimizing fan parameters through the heat balance model.

The invention relates to the technical field of agricultural microorganisms, in particular to a microbial agent containing paenibacillus love KF-B6, a compound microbial fertilizer and application of the microbial agent and the compound microbial fertilizer. The KF-B6 Paenibacillus lovuenyuan as well as the microbial agent and the compound microbial fertilizer thereof provided by the invention can promote the formation of soil aggregates, improve soil organic matters, improve soil enzyme activity, effectively improve soil and increase the proportion of soil bacteria and fungi. Therefore, the method can be applied to prevention and treatment of apple continuous cropping obstacles, and the survival rate of field planting of apple seedling stocks and field planting of young trees in a rebuilt orchard is guaranteed. By applying the KF-B6 compound microbial fertilizer and the microbial agent, the survival rate of the M9T337 rootstock seedlings planted in the continuous cropping soil can be increased, the highest survival rate reaches 96.67%, and the seedling roots are high in activity and good in growth vigor. The survival rate of the Daphne red apple seedlings planted in the rebuilding of the continuous cropping obstacle old orchard is as high as 94.00%, and the effect of effectively preventing and controlling the apple continuous cropping obstacle is achieved.

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 present invention provides a fruit picking method based on a picking robot, a picking robot and an electronic device, which relate to the field of fruit picking, and include S1, obtaining map information of an orchard; S2, establishing a spatial model of an orchard map and a picking robot according to the map information and depth information obtained by a depth camera, and obtaining a coordinate set of fruit distribution; S3, performing preliminary path planning covering the coordinate set according to a preset path planning algorithm according to the spatial model and the coordinate set; S4, establishing a kinematic model of the robot; S5, determining a kinematic inverse solution of different picking postures of the robot according to the coordinate set and the kinematic model; S6, optimizing the preliminary path planning into a navigation path according to the kinematic inverse solution; S7, executing the navigation path through a mobile chassis to realize fruit picking, wherein the mobile chassis is connected to the robot arm and drives the robot arm to move, and can complete the picking of fruits in the orchard with high efficiency without collision, thereby saving energy consumption.

The invention provides an apple orchard pest and disease damage intelligent monitoring and early warning robot, and relates to the technical field of artificial intelligence. The apple orchard pest and disease damage intelligent monitoring and early warning robot comprises a power supply module, a pest luring and capturing module, a data processing and analyzing module, a data transmission and communication module, a walking and navigation module, a user interface and control module and an image acquisition and processing module. Identification, counting, monitoring and early warning and knowledge questioning and answering of diseases and insect pests are realized. The apple orchard pest and disease damage intelligent monitoring and early warning robot provided by the invention realizes efficient, accurate and real-time pest and disease damage monitoring and early warning of the apple orchard, and improves the automation level and the intelligent degree of apple orchard pest and disease damage management.

The invention relates to the technical field of agricultural fertilization, in particular to an orchard fertilization and pesticide spraying system which comprises an irrigation pipe network, a pesticide spraying device and a fertilizer spraying device. The moving spraying assembly comprises a moving track, a spraying trolley, a spraying mechanism, a stirring mechanism and a height adjusting mechanism; the fertilizer conveying assembly comprises a detection mechanism and a release mechanism; the pesticide conveying assembly comprises a telescopic pipe, a height synchronization mechanism, a butt joint opening mechanism and a butt joint output mechanism, the pesticide conveying pipeline is provided with a plurality of pesticide output ports, and the butt joint opening mechanism is used for communicating the telescopic pipe with the pesticide output ports when the liquid inlet end of the telescopic pipe is in butt joint with the pesticide output ports; the butt joint output mechanism is used for communicating the telescopic pipe with the spraying mechanism when the liquid outlet end of the telescopic pipe and the spraying mechanism are in butt joint. According to the system, the automation level of orchard management is improved, resources are saved, and the working efficiency is also improved.

The invention relates to the technical field of ecological orchard management. According to the orchard management data platform and the control method thereof, the method comprises the steps that environmental data are collected through a soil sensor, a meteorological station and an unmanned aerial vehicle, space-time alignment and standardization processing are carried out on the environmental data, and a structured data set is generated; based on the structured data set, a farming control instruction set is generated through water demand prediction, pest and disease risk assessment and economical efficiency optimization; according to the farming control instruction set, the irrigation equipment, the fertilization equipment and the unmanned aerial vehicle are driven to execute tasks through priority arbitration and dynamic threshold adjustment; execution results of irrigation equipment, fertilization equipment and an unmanned aerial vehicle are collected, orchard output data are combined, and model parameters generated by dynamic decision instructions are optimized, so that sudden meteorological changes and production demand fluctuations are responded in real time, resource waste is reduced, the system adapts to a multi-equipment collaborative scene, and instruction conflicts are avoided.

The invention discloses an orchard dense map construction method and system based on an adaptive ORB threshold and point-line fusion, and the method comprises the steps: employing a FAST corner extraction method of an adaptive threshold in a tracking thread of ORB-SLAM3, solving a corresponding threshold according to the average pixel of a current image under different illumination, carrying out the ORB feature extraction of an image, and carrying out the point-line fusion. The number of feature points extracted under different conditions is increased, and the robustness of the algorithm is improved; aiming at obvious line features of branches and branches in the mulberry field environment, adding a line feature extraction method in a tracking thread at the same time, performing point-line fusion feature tracking, and performing BA optimization on key frame map points generated by the tracking thread to complete local map construction; the method comprises the steps of obtaining a key frame and depth information, performing depth optimization on the obtained key frame and depth information to obtain a camera pose, performing local point cloud construction and splicing according to the camera pose, finally performing global BA optimization on an obtained point cloud map, and constructing an orchard three-dimensional dense map to solve the problem of sparse point cloud information of an original algorithm.

The invention discloses an apple identification method suitable for a dwarf, close-planting and high-shielding orchard environment, and the method is characterized in that the method comprises the following steps: collecting an apple image; collected apple images are preprocessed, marked and input into a target detection model to be processed, and the processing steps comprise the steps that a bounding box regression gradient is optimized through a loss function; an ODConv module dynamically adjusts the weight of a convolution kernel in a Backbone feature extraction stage, and gives a higher weight to the target feature of a non-uniform illumination region in the image through an attention mechanism; the SPPELAN structure further performs multi-scale fusion on the enhanced Backbone features, provides multi-scale features for a loss function, and improves the small target detection capability; and outputting the image drawn with the prediction frame. Through cooperation and combination of an ODConv module embedded in a C2f module in the target detection model, an SPPELAN structure and an attention mechanism, apple detection performance in a dwarf close planting scene is significantly improved.

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.

The invention discloses a path tracking control method for a crawler-type unmanned plant protection machine in an orchard environment. The method comprises the following steps: acquiring a to-be-tracked path; continuously moving the unmanned plant protection machine, traversing path points in the path to obtain a path point closest to the current point, and recording the path point as a closest point; traversing remaining path points except the nearest point in the path, presetting a fixed distance, obtaining a path point with the distance being the fixed distance from the nearest point, and recording the path point as a target point; fitting the nearest point and the target point to obtain a navigation line; calculating transverse deviation and course deviation of the current point and the navigation line; and taking the transverse deviation and the course deviation as two input quantities of a controller, fuzzifying the input quantities, and outputting an angular velocity by the controller according to a fuzzy control rule table. According to the method, the multiple path points are collected, the navigation line is dynamically fitted in the path tracking process of the unmanned plant protection machine, and the problem that the curve tracking effect is not ideal is solved.

The invention discloses a double-arm citrus picking robot and a picking method, and belongs to the technical field of agricultural automation equipment. The robot comprises a crawler-type moving chassis, a symmetrically-arranged double-mechanical-arm system, a machine vision system, a citrus collecting bin and a control system. The double-mechanical-arm system adopts a main and auxiliary collaborative framework, the tail end of a main mechanical arm is integrated with a clamping jaw and vacuum suction disc parallel mechanism, an auxiliary mechanical arm is provided with a rotary circular saw blade, and lossless clamping of fruits and precise cutting of fruit stems are achieved. The machine vision system realizes high-precision citrus identification under complex illumination by improving a YOLOv5 network and fusing an HSV dynamic threshold value and Focal Loss optimization; graded path planning is combined with an A * algorithm and an RRT * algorithm, and motion safety of the mechanical arm is ensured through bounding box collision detection. The picking method comprises the steps of three-dimensional semantic map construction, collaborative grabbing and cutting, lossless transfer and the like. The problems that a single-arm robot is low in efficiency, fruits are prone to being damaged, fruit stems are not thoroughly treated and the like are solved, and the robot is suitable for automatic orchard harvesting in hilly areas.

The invention discloses a forest fruit pose estimation method and system suitable for a depth information loss scene, and belongs to the field of picking robot environment awareness, and the method comprises the steps: carrying out the forest fruit pose estimation of a forest fruit image of an orchard through employing a trained model, and enabling the model to comprise a target detection network, a feature enhancement module and a pose prediction head, using the forest fruit image sample training model and the target detection network to extract a multi-scale feature map from the forest fruit image sample, and generating a target detection frame; a feature enhancement module extracts global semantic Token from the multi-scale feature map, constructs a feature sequence in combination with target features in a target detection frame, and extracts global features from the feature sequence; the pose prediction head predicts the target pose by using the global features, and trains the model to convergence to obtain a trained model. According to the method, the pose estimation precision is improved in the absence of depth information, and the generalization ability is high, so that the operation robustness and reliability of the picking robot in a complex outdoor orchard environment are enhanced.