2153 results about "Orchard" patented technology

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

The invention provides an orchard agricultural robot navigation method, and belongs to the technical field of agricultural robots. The method comprises the following steps: constructing a priori map of an orchard; carrying out odometer initialization and rear end correction on the robot; carrying out ground segmentation and obstacle identification; constructing a cost map: constructing the cost map through static obstacles of a prior map and dynamic obstacles after ground segmentation, and realizing visualization of the obstacles; and path planning: assigning an initial point and an end point on the cost map, firstly realizing global path planning, bypassing an obstacle by using local path planning to realize an obstacle avoidance function when a dynamic obstacle appears in the advancing process, and performing real-time trajectory optimization and smooth control. According to the method, accurate positioning and autonomous navigation of the robot in a complex orchard environment can be realized; meanwhile, through the combination of global path planning and local path planning, the robot can flexibly process static and dynamic obstacles, and the robustness of path planning and the effectiveness of a navigation task are ensured.

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 provides an innovative apple tree fruit nutritional ingredient dynamic monitoring and precise management system, and aims to solve the problem that the change of fruit nutritional ingredients cannot be mastered in real time in traditional orchard management. The system integrates spectral data, environmental data and image data, and comprehensively reflects the change of fruit nutrients and the relationship between the change and environmental factors through multi-source data fusion analysis. According to the invention, a standardized model between spectral data and nutritional ingredients is established, and a deep learning algorithm is introduced to improve the accuracy and efficiency of image recognition. The system can analyze fruit nutrients and environmental parameters in real time, send early warning information and generate personalized fertilization and health management suggestions. In addition, the system adopts a modular and extensible design, and is convenient to customize and integrate. The orchard management accuracy and efficiency can be remarkably improved, the yield and quality of fruits can be improved, the production cost is reduced, and the economic benefits are increased.

The invention relates to the technical field of agricultural machinery, in particular to an orchard intelligent all-dimensional mechanical weeding machine which comprises a frame, a controller is fixedly connected to the top of the frame, a camera is fixedly connected to one side of the frame, the controller is used for receiving image information collected by the camera, and an energy supply assembly for supplying energy to the mechanical weeding machine is arranged at the top of the frame. A driving assembly used for driving the mechanical weeding machine to move and a reversing assembly used for controlling the mechanical weeding machine to reverse are arranged at the bottom of the frame, and a monitoring assembly used for monitoring the current environment condition and an adjusting assembly used for adjusting the weeding height are arranged on one side of the frame; and a control system for controlling the mechanical weeding machine to operate is arranged in the controller. Orchard images are collected in real time through the camera and the recognition module, crops and weeds are accurately distinguished on the basis of the database established through data over the years, the weeding accuracy is improved, and meanwhile the possibility that the crops are accidentally damaged by the mechanical weeding machine is greatly reduced.

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

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

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

The invention discloses a method for collecting orchard ecological environment information, and the method comprises the steps: collecting soil parameters, space environment parameters, insect pest parameters and fruit parameters of an orchard in real time through deploying a plurality of collection devices, and carrying out the collection to form an orchard ecological basic data set; a backstepping model is constructed to indirectly calculate key ecological parameters including soil nutrients, fruit quality and insect pest diffusion trend, and an optimization result is corrected through experimental verification and the model; comparing a backstepping result with an orchard target value to analyze a deviation source, and establishing a macroscopic cause chain through data matching and a visual tool; according to the deviation analysis and the cause chain result, determining an executable regulation and control measure matched with the regulation and control direction; and monitoring the effect after the implementation of the regulation and control measures, verifying the regulation and control effect by comparing the data before and after the implementation of the regulation and control measures, and applying feedback data to iterative optimization of the backstepping model. The method is scientific and efficient and can be used for improving orchard production benefits and ecological environment quality.

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

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

The invention relates to the field of intelligent breeding, in particular to a breeding monitoring method and system for an intelligent agricultural orchard, and the method comprises the steps: obtaining poultry breeding data in an orchard range in real time; judging whether abnormal conditions are met or not according to the poultry breeding data; when the poultry breeding data is not abnormal, acquiring environmental data and behavior data of poultry; judging whether the behavior data meets abnormal behaviors or not according to the environment data; when the behavior data meets the abnormal behavior, marking a target poultry corresponding to the abnormal behavior; quantifying the behavior data according to the judgment result, and speculating the reason of the current abnormal behavior in combination with the quantified behavior data and the environment data; and generating a coping strategy according to the reason of the current abnormal behavior based on the speculation result. And the multi-dimensional data fusion capability is realized, and the adaptability of the system to complex conditions is remarkably improved. Misjudgment possibly caused by a single data source is avoided, and the accuracy of an abnormal behavior analysis result is ensured.

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 provides an annual intercropping green manure planting method suitable for orchards in plateau areas. According to the method, tartary buckwheat and vicia villosa are mainly adopted as green manure plants, and a plurality of key steps of land parcel selection, soil preparation before sowing, variety selection, seed treatment, green manure sowing, plant management, cutting and covering and the like are clearly stipulated. The method can improve the utilization rate of nutrients in the soil, increase the content of organic matters in the soil, improve the soil quality, effectively inhibit growth of weeds between rows and on ridges of the organic apple orchard, reduce the weeding cost, and solve the problem of high labor cost caused by long-term ploughing and weeding of the organic apple orchard in the plateau mountain area of Yunnan province. Excessive dependence on organic fertilizer is effectively relieved, and the method is an annual intercropping mode for comprehensively improving ecological benefits and economic benefits of the orchard.

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 pesticide residue prediction method based on an LSTM variant, and relates to the technical field of pesticide residue detection and prediction.The method comprises the steps that time sequence data such as temperature, humidity, soil pH value, rainfall and pesticide application concentration are collected through a sensor, and normalization, missing value filling and sliding window slicing treatment are conducted; an optimized LSTM variant model is adopted for modeling, and an output activation function is removed to enhance the capacity of capturing nonlinear changes of pesticide residues. The model is combined with a dynamic threshold adjustment mechanism, adapts to characteristics and residue standards of different pesticides, and can be deployed on edge equipment in real time to realize real-time alarm when residues exceed the standard. Compared with the prior art, the method has the remarkable advantages in the aspects of prediction precision, response speed and adaptability, and is suitable for pesticide residue monitoring in various agricultural scenes such as rice fields and orchards.

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 relates to the technical field of automatic agricultural equipment, in particular to an orchard plant protection robot cluster operation system which comprises a central scheduling module, a cluster communication module, an environment modeling module and an updating module. The central scheduling module communicates with a robot through a communication technology, and the cluster communication module constructs a robot cluster communication network by using a communication network configuration strategy; the environment modeling module evaluates the complexity of a local environment through a sensor assembly, and generates a local map containing orchard terrain, vegetation density and obstacle information. The updating module splices the local maps to generate an operation map, updates a communication network according to the environmental complexity, and optimizes task allocation and path planning of the robot cluster; the system has the functions of task scheduling, communication network optimization and map updating, and efficient collaborative operation of a robot cluster in a complex orchard environment is achieved.

The invention discloses an automatic pear picking detection system based on SRSMama. According to the system, three-dimensional selective scanning SS3D is provided, the original scanning mechanism of Vama is improved, the scanning range is expanded from two dimensions to three dimensions, semantic information between channels is efficiently processed, and the capability of a model in a complex orchard environment is enhanced; in order to eliminate redundant background information in the pear garden, the system provides a reward and punishment mechanism (RPM), enhances useful information, weakens redundant information and retains important feature information in a neural network; the invention further provides a stacked feature pyramid network (SFPN), the scale information of the feature fusion section is enhanced, the features of different scales are effectively integrated, and the capability of positioning the dense pear clusters is enhanced. The invention further discloses detection equipment for automatic pear picking.

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.