1253 results about "Orchard" patented technology

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.

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 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 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 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 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 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.

The invention relates to the technical field of orchard planting, in particular to an orchard intelligent gas explosion deep scarification fertilizer applicator which comprises a supporting table used for supporting a device body, a protective frame is mounted at the top of the supporting table, and a gas pressure controller used for soil gas explosion deep scarification and an automatic driving control controller are mounted in the protective frame. By arranging the fertilizing mechanism, the device can sow seeds while loosening soil through gas explosion, the gas pressure of a gas storage tank is controlled through a gas pressure controller, when the seeds move into a through pipe from a feeding pipe, the seeds slide directionally due to the arrangement of a wedge-shaped surface, and the position of a gas outlet plate is adjusted through an electric push rod; the multiple sealing blocks form a closed space for the partition plate area, the multiple limiting blocks divide the seeds in the multiple through pipes in a limited mode, and the multiple air outlets correspond to the multiple feeding holes, so that air impact acting force is released to the seeds, the seeds can be released through the discharging holes, and meanwhile the loosening effect can be generated on internal soil.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

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 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 relates to the technical field of target tracking, and discloses a target fruit dynamic tracking method and system based on visual perception and deep learning. The method comprises the following steps: collecting original image data, carrying out image enhancement through an encoder-decoder architecture adaptive regulation and control model, carrying out deep learning target detection on an enhanced large-vision-field image to extract fruit position information, guiding a small-vision-field sensor to a picking preparation point, and carrying out deep learning target detection on the fruit position information; the method comprises the following steps: acquiring fruit shielding rate and growth attitude information through a multi-target segmentation network, performing dynamic tracking in combination with depth information to obtain motion trail prediction data, calculating a picking suitability score and an optimal picking point according to the shielding rate, the growth attitude and the motion trail, and generating a picking execution instruction. The fruit detection coverage rate and the positioning precision in a complex orchard environment are improved, and the limitation of single picking point selection in the prior art is overcome.

The invention relates to the technical field of mobile robot autonomous navigation path planning, and discloses a complex orchard scene-oriented hybrid sampling global path planning method, which comprises the steps of adopting a Gaussian hybrid sampling strategy, switching between global uniform sampling and Gaussian offset sampling through a probability threshold, and considering global exploration and key area guidance; a dynamic node expansion strategy is adopted, the expansion step length is adaptively adjusted according to the distance between the expansion step length and a target point, and the expansion direction is optimized by fusing vectors facing a sampling point and the target point and introducing an obstacle influence factor; a layered collision detection strategy is adopted, and step-by-step filtering is carried out through rough detection based on a quadtree, secondary screening based on an axis alignment bounding box and accurate geometric judgment in sequence so as to improve the efficiency; and a dynamic iteration termination mechanism is adopted, and the minimum number of iterations and the path quality convergence state are combined as composite termination conditions. The method can quickly generate a smooth and safe path, and is especially suitable for structured scenes such as orchards.

The invention relates to the technical field of data processing, and discloses an orchard nitrogen and phosphorus interception method and system based on intelligent monitoring. The method comprises the steps of collecting orchard multi-sensor data and performing standardization processing to generate a monitoring data set; learning a nitrogen and phosphorus distribution rule by using a variational auto-encoder to generate an interception parameter relation model; calculating regulation and control instructions of the groove, the PRB and the sedimentation tank through a multi-objective optimization algorithm; all the intercepting devices are controlled to operate cooperatively to collect nitrogen-phosphorus-rich sediments; and recycling sediment to prepare slow-release fertilizer to be applied to the orchard again. Through data enhancement of the variational auto-encoder and a multi-objective optimization algorithm, the problems of insufficient training samples and single parameter optimization in orchard nitrogen and phosphorus interception are solved, and the prediction precision of intelligent monitoring and the cooperative control efficiency of an interception system are remarkably improved.

The invention provides a fruit quality detection method, a robot and terminal equipment, and the method is applied to the fruit quality detection robot, and specifically, a plant canopy is scanned to obtain a canopy image so as to recognize a target detection fruit and adjust the collection posture of a mechanical arm, so that target recognition and positioning of the fruit are realized; fruit appearance images of target detection fruits are collected through the visual system, fruit external appearance characteristic detection is achieved, fruit spectrum data of the target detection fruits are collected through the spectrum collection system, fruit internal quality detection is achieved, and detection surrounds the whole growth stage of the whole cherry tomato plant and is continuous; in addition, fruit quality evaluation is carried out based on a deep learning model, and it is ensured that a quality detection task can be rapidly responded and completed in an orchard environment. Therefore, the fruit growth stage can be efficiently, accurately and automatically monitored in the orchard, the overall quality and market competitiveness of the fruits are improved, and agricultural mechanization and automation development is promoted.

The invention belongs to the technical field of agricultural robots, and particularly relates to a multi-degree-of-freedom double-arm pear fruit picking robot based on an agricultural machinery and agricultural technology fusion idea, which comprises a walking chassis, a picking system, a fruit collecting system, a visual system, a control system and an endurance system, the walking chassis is mainly used for bearing mechanical parts and picked fruits, walking in a field and providing power for the whole picking robot, the picking system is carried on a picking robot body and mainly used for grabbing recognized pears capable of being picked, and the fruit collecting system is carried on the picking robot body and used for collecting the fruit collected by the fruit collecting system. The picking robot body is used for conveying the picked pears into the fruit collecting box, and the visual system is carried on the picking robot body. The device can adapt to the picking operation of standardized pear garden planting agriculture in different planting modes, is highly integrated and automatic, can effectively improve the pear fruit picking operation efficiency, greatly reduces the dependence on the labor cost, and achieves the intelligent upgrading of agricultural production.

The invention relates to the technical field of computer vision, in particular to an improved YOLOv8n-based unmanned aerial vehicle litchi image detection method, which comprises the following steps of: acquiring a litchi image data set of a litchi orchard; a YOLOv8-litchi model based on the YOLOv8n model is constructed; training set data are input into the model for training, and detection and classification of the litchi fruits are completed; and inputting test set data into the model to realize accurate detection of the litchi fruits. According to the invention, an RCS-OSA module is introduced to improve a YOLOv8 network model, the feature extraction capability of a feature acquisition module is enhanced, the calculation complexity is reduced, effective information of the module is successfully fused by introducing a BiFPN network structure, the detection capability of a detection model is enhanced by using a DynamicHead detection head, and the detection efficiency is improved. And finally, the detection precision, the generalization ability and the robustness of the model are enhanced, so that relatively high accuracy is obtained in a natural environment.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The invention discloses an orchard transportation robot path tracking system, and relates to the technical field of robot programmes, and the system comprises the steps: collecting an alignment data set, extracting a stability feature, a terrain trafficability feature and initial trajectory deviation data based on the alignment data set, generating a deviation scoring result based on the initial trajectory deviation data and the trafficability feature, and carrying out the tracking of an orchard transportation robot path. According to the method, autonomous path tracking and avoidance control of the orchard transportation robot are realized through multi-source perception and intelligent decision, positioning, attitude and environment structure data are fused, the advancing stability and terrain trafficability are analyzed in real time, and the accuracy and the reliability of the orchard transportation robot are improved. The path deviation trend is predicted and adaptively corrected, high-precision navigation in a complex orchard environment is realized, the robot can keep stable operation in a multi-machine cooperation and edge-of-field steering scene through a dynamic channel regulation and control and feedback learning mechanism, and automation and operation efficiency of orchard transportation operation are remarkably improved.

The invention provides a method for identifying and picking flat peaches in a growth period in a natural orchard environment, and belongs to the technical field of computer vision and intelligent agriculture. The problems that branches and leaves in a natural orchard are seriously shielded, characteristics of flat peaches in different growth periods are different, identification is difficult, and positioning accuracy is low are solved. In order to solve the problem, the invention constructs an identification model OGD-Net for the growth period of the flat peach in the natural orchard environment; the method comprises the following steps: firstly, enhancing data through Cutout, and improving the detection robustness of a model under the shielding of branches and leaves; lightweight context is introduced into the backbone network to guide down-sampling and gate control frequency domain dynamic convolution, and perception of texture and color is enhanced while the calculation amount is reduced; an orchard adaptive feature fusion pyramid is designed, and a space-channel dual attention mechanism is combined, so that the positioning error is effectively reduced; the detection head introduces space to enhance attention, and the attention degree of a key area is improved. Precise recognition and center point positioning of the flat peach growing period are achieved, automatic operation from recognition to picking is achieved in combination with coordinate transformation and mechanical arm control, and the picking success rate is remarkably increased.

A method and system provide the ability to manage an orchard. Sensor data that represents a first state of the orchard is captured via one or more sensors. The sensor data is captured as the one or more sensors are traveling through the orchard. An almanac is maintained. The almanac provides a state library of sequential states of a representative orchard and a task library for one or more tasks to be performed to transition between the sequential states. A task manager queries the almanac to identify a first task of the one or more tasks and allocates the first task to one or more robots that perform the first task.

The invention discloses an orchard unmanned aerial vehicle multispectral image noise restoration method based on a generative adversarial network, and relates to the technical field of multispectral image noise restoration, and the method comprises the steps: synthesizing a noisy sample containing stripes, photon particles, thermal noise, Rayleigh, shielding, compression artifacts, overexposure and waveband crosstalk on a noise-free reference image through a conditional generative adversarial network; a crosstalk matrix T obtained through optical calibration is introduced; based on the canopy / soil / sky semantic partition, main-cooperation-subdivision level superposition is implemented; generating hierarchical labels by taking energy conservation consistency and thermal noise correlation as thresholds; a branch and region self-adaptive repair module corresponding to classification is arranged in the image-to-image generative adversarial network, and joint loss training containing T regularization is adopted; and outputting the repaired image subjected to physical consistency checking. The method gives consideration to visual quality and spectral fidelity, inhibits cross-band pollution, improves the stability of indexes such as NDVI and NDRE, and is suitable for precision agricultural monitoring.

The invention discloses a cherry maturity and surface defect detection method and a related device, and the method comprises the steps: collecting and preprocessing cherry image data, and constructing a cherry image data set; constructing a reference target detection model Cherry-YOLO, performing response-structure double-guide pruning operation on the reference target detection model Cherry-YOLO, introducing a structure perception cross-layer distillation mechanism to obtain an SEDDP-YOLO model, and performing training by using a cherry image data set to obtain a lightweight optimized cherry detection model; and the maturity and surface defects of the cherries are detected by using the lightweight optimized cherry detection model, so that automatic detection and grading tasks of the cherries are completed. According to the method, efficient and accurate detection of the maturity and the surface defects of the cherries is realized, the real-time deployment requirements on orchard sites, sorting assembly lines and low-power-consumption equipment are met, and the method can be widely applied to the field of small target identification and grading detection of other agricultural products.

The invention discloses a self-adaptive apple maturity lossless picking flexible claw and a picking method. The device comprises a base assembly, and a visual recognition module, a flexible clamping jaw assembly, a distributed pressure sensing module, a transmission execution module and a control unit are integrated on the base assembly. And the visual module adopts a depth camera and a YOLO11n model, judges the maturity grade in real time based on the apple red area proportion and is matched with the corresponding grabbing threshold force. The pressure sensing module is a thin film pressure sensor, and multi-point pressure feedback is achieved through a plurality of pressure detection points. The transmission module adopts a single motor to drive a lead screw nut lifting table, opening and closing of clamping jaws are synchronously controlled, the structure is light and simple, and cost is low. The picking method follows the process of recognition, matching, force control and picking, a cutter is not needed, lossless and high-success-rate self-adaptive picking is achieved, the problem of damage or failure caused by traditional fixed force control is effectively solved, the method is suitable for dense orchards, and the intelligent and lossless levels of apple picking are improved. The control unit is low in response delay and small in overshoot, and it is ensured that the grabbing force is accurately matched with the fruit state.

The invention discloses an orchard acidified soil improvement effect evaluation system based on a knowledge graph, and relates to the technical field of agricultural science, and the system comprises a data collection monitoring module which is used for collecting multi-source data in an orchard acidified soil improvement process, and carrying out the preprocessing of the multi-source data to form a multi-source data set; and the dynamic knowledge graph construction module is used for constructing a dynamic knowledge graph in which the time dimension is introduced based on the preprocessed multi-source data. According to the method, the hysteresis effect and the cumulative effect of the orchard acidified soil improvement measure can be accurately captured by constructing the dynamic knowledge graph introducing the time dimension, a traditional evaluation method mostly depends on static cross-section data and is difficult to comprehensively reflect the dynamic change of the improvement process, and the system can accurately evaluate the dynamic change of the improvement process through time sequence data analysis. The dynamic relation among the elements in the soil improvement process is visually displayed, a more accurate basis is provided for evaluating the improvement effect, the improvement strategy can be adjusted in time, and the improvement efficiency is improved.

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 invention discloses an orchard crawler differential robot trajectory tracking method based on adaptive incremental control, and relates to the technical field of agricultural robot control. The method comprises the following steps: receiving environmental data collected by a robot sensor, constructing a probability grid map of an orchard environment according to the environmental data, and aligning the probability grid map with a pre-constructed map inertial coordinate system; the course angle error and the distance error between the current position and a target point are calculated based on the pose of the robot, an error vector is formed, and the target point is dynamically planned according to the fruit maturity and reachability priority detected by a visual system and a laser radar point cloud. According to the method, coordinate transformation is not needed, and different from traditional tracking control, a control instruction is generated directly based on pose increment, the course and distance error of a target point is calculated directly based on an inertial coordinate system, and real-time matrix projection operation is avoided.