204 results about "Row crop" patented technology

The invention provides a crop whole growth cycle identification method and system based on deep learning, and the method comprises the steps: obtaining a multispectral image sequence of a target crop in a continuous time period through an image collection device, and carrying out the standardized illumination adjustment processing of each image frame in the multispectral image sequence, obtaining a standard illumination image set corresponding to the multispectral image sequence; carrying out crop region segmentation processing on each image, extracting a local feature region related to a target crop, generating a standardized crop image set containing the local feature region, inputting the standardized crop image set into a pre-trained multi-task deep learning model, extracting combined features through parallel convolution branches, and carrying out image segmentation processing on the combined features; and executing cross-stage correlation analysis in the full connection layer, outputting a multi-task classification result corresponding to the target crop growth cycle, and generating a stage identification report corresponding to the target crop full growth cycle. According to the invention, the crop whole growth cycle identification precision and the agricultural management efficiency can be improved.

The invention relates to the technical field of intelligent decision making, in particular to an accurate fertilization intelligent decision making method and system based on multi-source remote sensing, and the method comprises the steps: obtaining a multi-source remote sensing image which comprises an unmanned aerial vehicle image and a stationary satellite image; acquiring soil nutrient data and fertility indexes of the sample field parcels; acquiring meteorological data and soil data; carrying out image preprocessing and feature extraction; constructing a crop identification and classification model; carrying out crop type identification and block division on the multi-source remote sensing image; extracting crop growth features based on the multi-source remote sensing image and the result of the crop identification and classification model; based on the crop growth characteristics and the soil nutrient data, analyzing the relationship between the crop growth stage and the nutrient demand; constructing a nutrient utilization efficiency maximization model; based on a nutrient utilization efficiency maximization model, generating a precise fertilization decision scheme; and outputting a precise fertilization decision scheme, and carrying out precise analysis and intelligent decision making on the growth state and nutrient demand of farmland crops.

The invention discloses an AI-based digital twinborn irrigation area automatic irrigation method and system, and relates to the technical field of artificial intelligence, and the method comprises the following steps: collecting data, and constructing a digital twinborn model; irrigation demand prediction is carried out according to the digital twinborn model; making an irrigation control decision according to the irrigation demand prediction result; performing automatic irrigation based on the irrigation control decision content; crop growth evaluation is carried out according to the irrigation execution result; performing irrigation strategy optimization according to a growth evaluation result; maintaining and updating the irrigation system according to the irrigation strategy; and performing water resource management based on the planning result. According to the method, the digital twin model can be updated in real time and adapt to environmental changes by introducing the data stream based on the Internet of Things and a reinforcement learning mechanism. The feedback mechanism based on AI and reinforcement learning enables the digital twinborn model to more accurately simulate an irrigation environment and provide an optimal irrigation strategy under different environmental conditions.

The invention discloses a multi-modal deep neural network model, system and method based on continuous learning. The multi-modal deep neural network model comprises a data acquisition and preprocessing module used for acquiring multi-modal data of a crop growth environment; the feature extraction module is used for extracting key agricultural features; the multi-modal information fusion module is used for effectively fusing the extracted key agricultural features; the knowledge continuous learning module is used for memorizing and storing the crop growth mode to a crop growth mode library and applying a model parameter self-adaptive updating strategy; the intelligent decision-making module is used for performing crop management decision-making based on the fusion features; and the effect evaluation and feedback module is used for evaluating the decision effect. According to the invention, the problem of knowledge forgetting of the existing AI system is solved, and the adaptability and decision accuracy of the intelligent agricultural system are improved.

The invention relates to a field water and fertilizer intelligent control method based on the Internet of Things, and the method comprises the steps: carrying out the crop type recognition and growth stage recognition through collecting crop images, soil water and fertilizer data and meteorological prediction data, and generating an initial water and fertilizer control scheme of a current time node; a soil water and fertilizer prediction model is created to predict the soil water and fertilizer change trend in a specified time period, the initial water and fertilizer control scheme of the current time node is adjusted according to the predicted soil water and fertilizer change trend, growth cycle characteristics corresponding to crop types are obtained based on big data, and a dynamic water and fertilizer control scheme is formulated. A water and fertilizer adjusting control scheme is executed at a current time node, a dynamic water and fertilizer control scheme is executed at a specified time node, the field water and fertilizer intelligent control method integrating Internet of Things sensing, intelligent identification, trend prediction, dynamic planning and closed-loop feedback is realized, the whole process intelligence from data acquisition to scheme execution is realized, and the water and fertilizer utilization efficiency is improved. The agricultural production cost is reduced.

The invention discloses a vision-based agricultural unmanned aerial vehicle terrain following method and device. The method comprises the steps of S1, performing off-line adversarial training on a GAN network; s2, fusing infrared and visible light images obtained in real time by using a trained GAN network generator to enhance terrain information; s3, estimating the crop canopy distance by using the depth estimation model; s4, taking the obtained crop canopy depth map Dp as a constraint of an objective function, and comprehensively considering other constraint conditions to construct the objective function so as to optimize trajectory planning; s5, optimizing the objective function by adopting a genetic algorithm, and taking an optimized curve as a flight path of the unmanned aerial vehicle to realize terrain following flight; the system can be applied to crop monitoring, pesticide spraying and the like, is particularly suitable for agricultural operation in complex terrains such as hills and mountainous regions, improves the production efficiency and the operation quality, and promotes informatization and intelligentization development of agricultural machinery equipment in China.

The invention relates to the technical field of water conservancy monitoring, and provides an intelligent water conservancy monitoring method for an irrigation area and related equipment thereof. Water body boundary extraction is carried out on remote sensing image data to obtain an initial water body boundary data set, time sequence interpolation processing is carried out on the initial water body boundary data set to obtain a water body change trend data set, and soil moisture content prediction deduction is carried out according to a satellite cloud picture and the water body change trend data set to generate a soil moisture content prediction data set. Performing water level and area fitting on the regional DEM according to the water body change trend data set to construct a water storage capacity model, and performing crop water demand calculation according to regional data and crop data to obtain a crop water demand matrix data set; and performing constrained programming according to the soil moisture content prediction data set, the crop water demand matrix data set and the water storage capacity model to obtain a multi-target irrigation scheduling decision set. According to the application, dynamic balance between irrigation requirements and water resource supply is realized through multi-level and multi-source data fusion and intelligent scheduling.

The invention relates to a resource calling control system and method for a water conservancy irrigation area project, and relates to the field of intelligent control, the request water supply amount of a request node is received, the sum of the request water supply amount is updated, and when the sum is greater than or equal to the redundant water storage amount, the crop type, the crop planting duration, the meteorological information and the soil information of the request node are extracted, and a water supply portrait is configured; retrieving water demand time sequence information of the sample group meeting the water supply portrait, and performing water deficit calculation in combination with the supplied water time sequence information to obtain water deficit time sequence information; performing crop growth data statistics according to the water deficit time sequence information to obtain a crop damage probability; the request nodes are ranked according to the crop damage probability from large to small, the first node ranking result is obtained for water supply resource calling, and the technical problems that the water supply quality is not matched with the demand and the irrigation effect and the agricultural production benefit are affected due to the fact that accurate scheduling for comprehensively considering the actual water use demand of each demand point and the irrigation priority is lacked are solved.

A rear module, as illustrated with tanks, seed blowers, and transfer pump for fertilizer is mounted on the existing design frame, axle, and steering system; but it is a separate, distinct module. The planting module and the harvesting module must be disconnected from the common front module power unit and switched for planting row crops or harvesting. The harvesting configuration can have an attachment to the header to seed non row crops, such as, a cover crop like rye or canola Such rear module can be the rear unit of an articulated vehicle, such as a harvester.

The invention discloses an inter-ridge autonomous navigation method and system for an asparagus picking robot based on machine vision, and belongs to the technical field of agricultural automation. The method comprises the following steps: processing a field image acquired by a robot in real time by using a pre-trained lightweight inter-ridge path segmentation network to generate a path mask; extracting and smoothing left and right boundary points of the path based on the mask; fitting a middle navigation line; and finally, a control error is calculated according to the navigation line and the robot pose, and a steering instruction is generated to drive the robot to autonomously travel. Through lightweight model design and a robust path fitting algorithm, the problems that an existing visual navigation method is large in calculation amount, poor in real-time performance and insufficient in precision are solved, an efficient, accurate and easy-to-deploy autonomous navigation solution is provided for an agricultural robot, and the method is particularly suitable for complex field environments of ridge crops such as asparagus.

The invention relates to the field of agricultural planting, and discloses a water and fertilizer pesticide spraying integrated high-altitude spraying system which comprises a sensing unit used for obtaining crop physiological and morphological information, dynamic environment field information and physical property information of mixed liquid; the decision-making and optimizing unit is used for comprehensively diagnosing the crop growth condition, generating a water-fertilizer-pesticide mixed formula, predicting a spraying track and optimizing spraying parameters according to the information acquired by the sensing unit; the execution unit is used for executing spraying operation according to the spraying parameter optimization result; and the feedback and calibration unit is used for monitoring the spraying operation effect in real time and providing calibration information. According to the invention, the density and the dynamic viscosity of the mixed liquid are integrated into the three-dimensional motion physical model of the fog drops, so that the system can dynamically adjust the actual physical characteristics of the water-fertilizer-pesticide mixed liquid in different proportions and at different temperatures, the accuracy of mist spraying track prediction is further improved, and invalid spraying is reduced.

The invention provides a greenhouse environment control method and system based on an AI algorithm, and the method comprises the following steps: collecting environment data in a greenhouse through environment collection equipment, and carrying out the preprocessing of the environment data, and generating primary data; transmitting the primary data set to a cloud computing platform, randomly splitting the primary data set into a plurality of contrast data sets through the cloud computing platform, and operating a crop growth model on the cloud computing platform in combination with weather forecast and the plurality of contrast groups so as to respectively obtain plant growth prediction data; inputting the plant growth prediction data into an adjustment model for analysis so as to output a plurality of corresponding pre-adjustment parameters; and performing overall calculation on the plurality of pre-adjustment parameters to obtain a target adjustment parameter, and adjusting the regulation and control equipment in the greenhouse according to the target adjustment parameter. According to the invention, the environment in the greenhouse can be automatically controlled, and the growth efficiency of plants in the greenhouse is improved.

The invention discloses a farmland segmentation and crop identification method based on cross-granularity remote sensing fusion, and belongs to the technical field of agricultural remote sensing and intelligent farmland management. The method comprises the following steps: firstly, acquiring and preprocessing a high-resolution fine-grained image and a multi-temporal medium-low-resolution coarse-grained image of a target area; performing over-segmentation by using the fine-grained image to generate a super-pixel object and constructing a graph structure model; extracting a core region through morphological corrosion to form a core object, and distinguishing a farmland region from a non-farmland region; taking the kernel object as a unit, and extracting a multi-dimensional feature set covering a crop growth cycle from the coarse-grained image; carrying out crop type identification by adopting a machine learning classifier based on the kernel object features; and finally, mapping the identification tag back to the super-pixel object, and generating a plot level classification map with accurate plot boundary and crop category information after boundary optimization. According to the method, the advantages of remote sensing data with different spatial resolutions are fused, so that the comprehensive precision and practicability of plot segmentation and crop identification in a complex farmland area are effectively improved.

The invention provides an open environment crop chlorophyll fluorescence parameter imaging detection method and device, and the method comprises the steps: carrying out the crop canopy segmentation of a crop canopy hyperspectral image based on a crop canopy mask image, and obtaining a crop canopy image; performing hierarchical segmentation based on the crop canopy image according to a clustering algorithm to obtain an upper-layer leaf image and a lower-layer leaf image; determining upper-layer chlorophyll fluorescence intensity of the upper-layer leaf image and lower-layer chlorophyll fluorescence intensity of the lower-layer leaf image; respectively compensating the upper chlorophyll fluorescence intensity and the lower chlorophyll fluorescence intensity based on the balanced brightness and crop absorption photosynthetically active radiation to obtain an upper chlorophyll fluorescence signal correction value and a lower chlorophyll fluorescence signal correction value; and constructing a visual chlorophyll fluorescence parameter spatial distribution image based on the upper-layer chlorophyll fluorescence signal correction value and the lower-layer chlorophyll fluorescence signal correction value. The method can realize rapid and accurate imaging detection of the chlorophyll fluorescence parameters of the crop canopies in the open environment.

The invention discloses a crop planting scheme generation method and device based on a large language model, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the natural language understanding of to-be-processed agricultural information, obtaining language understanding information, and converting the language understanding information into an information vector; constructing an agricultural knowledge graph based on the to-be-processed agricultural information; fusing the information vector and the agricultural knowledge graph to obtain a fusion result, and determining a retrieval result which corresponds to the agricultural knowledge graph and comprises a target entity and entity relationship path in the agricultural knowledge graph by utilizing a knowledge retrieval enhancement technology and based on the fusion result; determining a target context based on the retrieval result and an initial context corresponding to the large language model, and generating a crop planting scheme for the target context by using the large language model to obtain a target crop planting scheme; the target crop planting scheme comprises the explanation information corresponding to the planting suggestions, so that the efficiency of generating the crop planting scheme is improved.

The invention relates to the technical field of agricultural machinery, and provides an open field vegetable swing type intelligent inter-plant weeding device and a control method thereof, and the device comprises a plurality of weeding monomers, a sensor mechanism and a control mechanism. The sensor mechanism comprises depth cameras, laser sensors and absolute value encoders, the depth cameras are installed on the front portions of the multiple weeding single bodies, the visual field covers multiple rows of crops, and the laser sensors and the absolute value encoders are arranged on the weeding single bodies; the control mechanism is connected with the depth camera, the laser sensor and the absolute value encoder and used for processing data of the sensor mechanism, calculating the fusion height through a fusion algorithm and controlling and driving the lifting mechanism, the transverse moving mechanism and the weeding knife opening and closing mechanism to act based on the error between the fusion height and the target depth so as to achieve accurate weeding and seedling avoiding. The device can quickly avoid crops while effectively removing weeds between rows and plants, and finally realizes mechanical weed removal of open field vegetable crops which are large in bare soil proportion and easy to generate weeds.

The invention discloses a crop point cloud instance segmentation method, device and equipment, a medium and a product, and relates to the technical field of agricultural informatics. The method comprises the following steps: acquiring a crop point cloud point set; the crop point cloud point set comprises the number of points of the crop point cloud and real number coordinates of each point in a three-dimensional space; performing data enhancement processing on the crop point cloud according to the crop point cloud point set by adopting a plant data enhancement method to obtain enhanced point cloud data; based on a point cloud instance segmentation model, performing organ-level instance segmentation of the crop point cloud according to the enhanced point cloud data to obtain segmentation information data; the segmentation information data comprises leaf organs and stalk organs; the point cloud instance segmentation model is obtained by adopting a soft grouping algorithm and performing deep learning based on a crop point cloud point set with label data; the label data is the segmentation information data. The objective of the invention is to realize organ-level accurate instance segmentation of crop point clouds.

The invention provides an unmanned aerial vehicle variable fertilization method and system fused with multi-modal data, which is applied to the technical field of smart agriculture and comprises the following steps: acquiring time sequence multi-modal data of a target crop area; constructing a multi-scale three-dimensional growth model of the target crop region based on the time sequence multi-modal data; according to multi-modal deep learning, crop core leaf positioning is carried out based on a multi-scale three-dimensional growth model, and core leaf center point coordinates and crop ridge core leaf center lines are obtained; according to a fluid mechanics-discrete element-finite element coupling mode, a multi-scale three-dimensional growth model and the coordinates of the center points of the core leaves of each crop are used for simulating the movement track of fertilizer particles, and the fertilization safety distance is determined; based on the crop ridge core leaf center line of each crop, the fertilization safety distance in different growth periods and a preset fertilization amount, generating fertilization safety area thermodynamic diagrams of the crops in the target crop area in different growth periods, and planning an unmanned aerial vehicle path; and precise fertilization operation is carried out. According to the invention, the core-burning-free precise fertilization of the unmanned aerial vehicle can be realized.

Automated precision applicator for row crops with dual sided applicators that can be used to direct pollen to multiple areas of the plant, as well as to measure phenotypic characteristics of plant fruiting bodies and detect successful pollination.

An agricultural machine for arrangement on a vehicle, wherein during movement of the machine in the travel direction an area of soil at least between plants arranged parallel to the travel direction in row crops can be tilled by the machine, wherein the machine comprises a soil tillage tool, which is pivotable back, counter to the travel direction, about an axis of rotation running in a height direction of the machine out of a transverse direction arranged perpendicular to the travel direction and perpendicular to the height direction and / or is movable towards the machine in the transverse direction, an obstacle sensor by which obstacles can be detected, and comprises a control device, wherein when an obstacle is detected the control device can pivot the soil tillage tool back out of the transverse direction counter to the travel direction and / or move it in the transverse direction towards the machine.

This invention relates to a spraying device for defoliants on edible sunflowers and its usage method. The device includes a drone body with a liquid tank in the center. An annular plate is vertically movable along the outer wall of the drone body, and a windbreak plate is slidably connected to the side wall of the annular plate. A first spray pipe is fixedly mounted at the center of the bottom of the drone body. Second spray pipes are rotatably connected to both ends of the first spray pipe, and the other end of each second spray pipe is rotatably connected to vertical plates on both sides of the bottom of the drone body. The rotation of the second spray pipes is synchronized with the raising and lowering of the windbreak plate. Atomizing nozzles are fixedly mounted at the bottom of both the first and second spray pipes. This invention forms a vertical air curtain through the windbreak plate, providing a stable falling environment for the droplets and effectively preventing drift. The inclined spraying of the atomizing nozzles on both sides makes the liquid distribution more uniform and widens the effective coverage area, ensuring that the entire row of crops receives sufficient liquid from the center to the edge, eliminating missed spraying along the row edges, and improving the spraying effect.

The invention discloses a crop early recognition method and system based on genetic programming customization characteristics and a storage medium, and the method comprises the steps: obtaining an initial image of a target region and a ground sample collected on site, generating the initial characteristics of a target crop through the spectral band of the initial image, and generating an initial population according to the initial characteristics of the target crop; calculating a feature value of the initial feature, and performing binary classification on the feature value according to a preset feature threshold value; based on the classification label and the real label of the ground sample, obtaining an accuracy rate of binary classification, and obtaining a fitness value of the initial feature according to the accuracy rate; iterating the initial population through a selection method and a genetic operator to obtain customized features of the target crop; and performing crop classification on the target crops based on the customized features, and obtaining a classification drawing result of the early crops or seasonal crops according to a classification result. According to the method, the required sample size can be reduced, and meanwhile, accurate early-stage and season crop classification and mapping results can be obtained.

The utility model provides a final drive assembly for increasing ground clearance and a high ground clearance tractor, the final drive assembly for increasing ground clearance comprises a gear box, two opposite sides of the gear box are provided with an output disc and a half shaft shell, the lower part of the gear box is provided with an output shaft, and the output shaft is connected with the output disc; one end of the half shaft shell is fixed to the upper portion of the gearbox, an input half shaft and a transmission shaft are arranged in the half shaft shell, the input half shaft is in transmission connection with the opposite end of the transmission shaft, the other end of the transmission shaft extends into the gearbox and is in transmission connection with an output shaft of the gearbox, and the other end of the input half shaft extends out of the half shaft shell. According to the utility model, the power of the input half shaft is transmitted to the output disc through the gear box, so that the transmission of the walking power of the tractor is realized; the output disc is lower than the input half shaft, so that the ground clearance of the tractor is increased, the tractor can cross higher crops to perform crop management operation, and the chassis of the tractor is prevented from damaging the crops.

According to an aspect, there is provided a systems and methods for assessing crops. The crop assessment includes, for each given plant of a plurality of plants, assigning a unique identifier to the given plant, emitting light toward the given plant using at least one light emitter, capturing an image using at least one imaging sensor, detecting the given plant and a predictor of a crop output of the plant in the image, estimating a location of the given plant based on a signal from a geo-positioning subsystem, and generating at least one crop metric predictive of yield of the given plant based on the crop output, and generating a crop output data structure with data defining the at least one crop metric and the location, in association with the unique identifier, for each of the plurality of plants.

A method for farmland path recognition and planning based on aerial imagery is proposed. First, a data set (DOM) is constructed, cropped, and scale-normalized to form a dataset for recognition and planning. Next, multi-scale identification of the inter-row crop passage area is performed to obtain passable areas. Then, path planning is performed based on the inter-row structure: the centerline of the passable area is extracted to obtain a skeleton; principal direction constraints and structural consistency analysis are applied to the skeleton; branch removal and break repair are performed to obtain continuous, directional, and consistent inter-row centerline paths. The paths are clustered, grouped, and sorted to automatically distinguish different ridges and determine the walking order within the plot. Based on the endpoint relationships of adjacent inter-row paths, cross-row connection paths are generated at the ridge ends, forming a global navigation path combining straight and turning routes, and determining the entrance and exit points. Finally, the results are converted into standard path data for output, providing navigation for ground-based equipment. This invention effectively improves the stability and consistency of planning.

The invention discloses a seedling condition information determination method and device, equipment and a storage medium. The method comprises the steps of performing conventional segmentation on an acquired unmanned aerial vehicle image to obtain a conventional segmented image set, and performing dislocation segmentation on the acquired unmanned aerial vehicle image to obtain a dislocation segmented image set; determining a first image set according to the conventional segmentation image set, and determining a second image set according to the error segmentation image set; according to a pre-trained crop seedling identification model, identifying the first image set to obtain a first seedling identification result, and identifying the second image set to obtain a second seedling identification result; and determining seedling condition information according to the first seedling identification result and the second seedling identification result. According to the technical scheme of the embodiment, the crop seedling identification model is pre-trained, the two segmented images of the unmanned aerial vehicle image data are identified, the seedling condition information of the target crop field row crops is determined, and crop field seedling condition monitoring based on the unmanned aerial vehicle image is realized.

A row crop header for harvesting crop in a field comprises a header frame having a center section and a pair of side wing sections operatively coupled to the center section. An upper link is pivotally coupled between the center section and each one of the side wing sections adjacent the top portion thereof. A lower link is pivotally coupled between the center section and each one of the side wing sections adjacent the bottom portion thereof. The upper links and lower links provide independent pivotal movement of the side wing sections relative to the center section to contour to the surface of the field of crops to be harvested. An automatic float system is operatively coupled between the center section and each one of the side wing sections to adjust the weight of the side wing sections on the surface of the field and allow the side wing sections to float relative to the center section.

The invention discloses a method for improving the quality of low-yield slope cropland in mountainous and hilly areas, and belongs to the technical field of cropland quality improvement and improvement. (2) plot planning: performing plot planning according to topographic elements; (3) digging, filling and leveling: performing slope reduction, soil improvement and leveling on the slope cropland according to plot planning; and (4) soil fertilization: after the land parcel treatment is completed, soil fertilization is performed, crop planting is started, the combined operation of organic fertilizer, straw returning to the field and crop planting is adopted for soil fertilization for more than or equal to 3 years, the quality improvement of the low-yield slope cropland in the mountainous and hilly areas is completed, and the problems that at present, the slope cropland is poor in soil physicochemical property, the improvement effect is repeated, and the high-quality land parcel is difficult to continue are solved.

The invention relates to a crop row detection and row control method for a high-ground-clearance plant protection machine, and the method comprises the following steps: S1, collecting a crop row image in a working scene of the high-ground-clearance plant protection machine, and carrying out the data enhancement processing and marking; s2, constructing a crop row detection model and performing training; in the crop row detection model, a C2fCIBPConv module integrating partial convolution PConv and point-by-point convolution PWConv is used for replacing a C2fCIB module in the original YOLOv10n model, and an EMA attention mechanism module is embedded between a backbone network and a neck network; s3, deploying the trained crop row detection model on a high-ground-clearance plant protection machine, and carrying out crop row identification on images collected in real time; and S4, calculating a transverse deviation and a course angle deviation between the plant protection machine and the crop row based on an identification result, generating a row control instruction through a control algorithm combining adaptive PID with feedforward compensation, and driving a steering system to realize dynamic row control. According to the invention, high-speed and high-precision detection of crop rows can be realized, and row alignment deviation is stabilized within + / -5cm.

The present invention provides a method, device, system and electronic device for crop irrigation in saline-alkali farmland, relating to the technical field of intelligent agriculture. The method includes: when it is determined that crop irrigation needs to be carried out on the target saline-alkali farmland based on the actual value and the target value of the soil salt storage in the target saline-alkali farmland, obtaining the irrigation amount of the target saline-alkali farmland based on the environmental data in the target saline-alkali farmland, and then carrying out crop irrigation on the target saline-alkali farmland based on the above irrigation amount. The method, device, system and electronic device for crop irrigation in saline-alkali farmland provided by the present invention can irrigate the target saline-alkali farmland more scientifically and reasonably based on the soil salt storage in the target saline-alkali farmland, not only meeting the demand-based supply of crop water in the target saline-alkali farmland, but also ensuring the relative stability of the salt concentration in the soil, effectively solving problems such as salt stress caused by salt migration during the production process of crops in saline-alkali farmland.