894 results about "Growing region" patented technology

The invention relates to the technical field of agricultural datamation, in particular to an agricultural meteorology intelligent decision support method and system combined with multi-source heterogeneous data, and the method comprises the following steps: S1, obtaining and processing the multi-source heterogeneous data: obtaining agricultural multi-source heterogeneous data through official and monitoring equipment, and preprocessing the multi-source heterogeneous data; s2, data extraction and fusion: extracting multi-source heterogeneous data according to relevance; the method integrates meteorological, soil and crop multi-dimensional data through the space-time diagram neural network, combines the dynamic adjacency matrix and the attention mechanism, effectively captures the space-time correlation characteristics of the farmland area, solves the problem of data island of a traditional method, improves the disaster recognition precision, and improves the disaster recognition accuracy through introducing a transfer learning mechanism and a federated learning framework. Rapid migration of a main production area model to a new planting area is achieved, meanwhile, farm node data are encrypted and aggregated through a parameter server, collaborative modeling is completed on the premise of guaranteeing privacy of agricultural enterprises, and the risk of sensitive data leakage is avoided.

The invention relates to the technical field of inspection unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle inspection method and system for a tobacco field, and the method comprises the steps: obtaining multi-source remote sensing image data of a preset tobacco planting region; detecting tobacco plants in the remote sensing image data, and performing spatial clustering on detection results to obtain a plurality of tobacco plant cluster targets; and generating an initial inspection path according to the position coordinate and the vegetation index of the tobacco plant cluster target. And in the flight process, the flight pose and environmental parameters of the target unmanned aerial vehicle and the deviation angle between the unmanned aerial vehicle and the current path section are collected in real time. And when the adjustment condition is satisfied, automatically generating an improved path, and adjusting the flight attitude and the camera angle of the target unmanned aerial vehicle. And identifying whether an abnormal condition exists in the image data acquired in the flight process. The tobacco planting scene-oriented inspection method integrating multi-source remote sensing perception, adaptive path planning and intelligent identification is realized.

The invention discloses a rose planting medication scheme decision-making method based on multi-modal feature fusion. The method comprises the following steps: firstly, collecting multi-modal data of a rose planting area, and inputting the obtained multi-modal data into a feature fusion module for multi-modal feature fusion; a double-branch decision network is used for identifying the types of diseases and pests and judging the types and dosage of pesticides, and a pesticide proportioning rule is introduced through a pesticide liquid mixing control module to correct the types and dosage of the pesticides. And finally, through a user interaction optimization mechanism, closed-loop control is achieved, and a medication scheme decision is optimized. According to the method, dynamic closed-loop decision making is realized in combination with user feedback, so that individual requirements of different farmer households are met, and the pesticide application problem caused by data isolation, decision making staticization and the like in traditional rose planting is solved. An artificial intelligence technology and agronomic knowledge are deeply coupled, and traditional agriculture is promoted to be transformed to intelligence and automation.

The invention relates to the technical field of agricultural informatization and intelligent decision making, and discloses a vegetable growth cycle pest control decision making system and method based on a knowledge graph. According to the method, an unstructured agricultural data stream including vegetable varieties, growth stages, environmental parameters, pest and disease records and geographic information is collected from an original database. A data tuple set with a time stamp is generated by performing scanning identification and content extraction on a data stream. Then, clustering is carried out according to the growth stages to which the tuples belong, and stage feature clusters sorted according to the growth cycle are formed; and performing multi-dimensional feature correlation analysis on the stage feature cluster, and establishing a dynamic coupling relationship among the environmental parameters, the disease and pest history and the geographic space features under the growth cycle dimension. And based on the dynamic coupling relationship, generating disease and pest control decision parameters of the vegetables in the specific planting area in each growth cycle stage. According to the invention, accurate matching between the control decision and the crop physiological time sequence and regional characteristics is realized.

The invention discloses a rice yield estimation method based on unmanned aerial vehicle remote sensing and rice spike feature extraction, which comprises the following steps: firstly, assuming a ground section RTK base station and combining an unmanned aerial vehicle remote sensing platform to determine a coverage range and a spatial position of a rice region, and then collecting a rice image and canopy height and performing deblurring processing; then using a rice ear detection model to obtain a rice ear segmentation mask, determining a pixel scaling factor to construct a rice ear feature index, and carrying out statistical analysis and identification on key features to form a feature data set; performing yield estimation by using a rice yield estimation model, and optimizing the model in combination with the actual yield; and finally, transmitting the data to a ground end to generate a thermodynamic diagram and a statistical diagram, and carrying out regional comparison and time sequence trend analysis. According to the method, the influence of the flight height on yield estimation is eliminated based on the rice ear segmentation result in combination with high-precision depth information, and the accuracy and efficiency of rice yield estimation are effectively improved.

The invention provides a rice planting area identification method and device based on multi-temporal remote sensing data, equipment and a storage medium. The method comprises the following steps: acquiring the multi-temporal remote sensing data according to a rice phenological period; determining a spectral index sequence of the multi-temporal remote sensing data to construct a spectral index time sequence curve, and generating planting characteristic data in the target area according to a spectral change curve; determining a spectrum time sequence template matched with the rice growth rule according to the planting characteristic data, performing matching analysis on the planting mode of each land block, and marking a land block set of rice planting mode characteristics; performing cross comparison on the plot set and the rice planting reference sample to generate a rice planting mode sample set, and constructing a rice planting area identification model based on the rice planting mode sample set; and analyzing the remote sensing data of the land parcels to be identified in the research area according to the rice planting area identification model, and determining a rice planting area identification result. According to the scheme, the accuracy of rice identification can be effectively improved.

The invention relates to the technical field of intelligent agricultural planting and breeding, and discloses a digital intelligent agricultural planting and breeding combined system which comprises a multi-source sensing module used for collecting environment data, biological sign data and equipment operation data through a planting area sensor, a breeding area sensor and a circular processing equipment sensor; the data verification module is used for dynamically verifying the collected data based on a preset agronomic mechanism model; the hierarchical digital twinning module is used for carrying out collaborative decision making in a three-level architecture of the edge computing node, the local server and the cloud platform and generating an optimization instruction; a virtual-real decision engine module; and a closed loop execution network module. According to the invention, a lightweight twinborn model is deployed through the edge layer to realize millisecond-level equipment control and guarantee timely response of extreme working conditions, the field area layer coordinates planting and breeding material circulation through a dynamic optimization algorithm, and the cloud layer integrates climate data to generate a carbon sink strategy of block chain evidence storage. And the real-time decision-making precision, the resource utilization efficiency and the carbon economic value are improved through three-level cooperation.

The invention belongs to the field of crop yield estimation, and discloses a corn yield remote sensing estimation method and system based on multispectral data coupling radiation transmission and a crop growth model, and the method comprises the steps: obtaining and preprocessing a Sentinel-2 multispectral remote sensing image, and obtaining the reflectivity data of a corn planting region; constructing a PROSAIL forward simulation spectrum library, and performing domain correction on the simulation spectrum by using an auto-encoder and a residual error alignment network; establishing a machine learning inversion model based on the corrected simulated spectrum and the actually measured spectrum, and generating regional leaf area index (LAI) distribution; key agronomic parameters are obtained, a localized WOFOST crop growth model is constructed, the LAI state of the model is assimilated by adopting ensemble Kalman filtering at a remote sensing observation moment, and the crop growth process is dynamically corrected; and advancing the model to a mature period, outputting the dry weight of the corn kernels, and realizing remote sensing estimation of the regional corn yield. The method can effectively improve the LAI inversion precision and yield prediction reliability, and is suitable for the fields of agricultural monitoring, grain evaluation, agricultural condition management and the like.

The invention provides a corn planting area corn identification method based on spatial-temporal feature selection, and belongs to the technical field of remote sensing. The method comprises the following steps: acquiring a remote sensing image of a corn planting area, determining an NDVI characteristic curve of each pixel in the corn planting area, and screening out corn pixels conforming to key growth period planting characteristics as corn sample points; based on the NDVI characteristic curves of the corn sample points and the non-corn sample points, determining the importance of different vegetation characteristics to corn identification; removing redundant features by integrating importance and feature correlation to obtain classification features; calculating the time sequence-spectrum joint similarity between the to-be-classified pixels and the corn sample points and the non-corn sample points; and classifying the to-be-classified pixels into a category corresponding to the minimum hour sequence-spectrum joint similarity. By using the method, the problems of insufficient separability and feature redundancy in corn identification of a traditional classification method and the problem of agricultural statistical data threshold dependence of a TWDTW method can be solved.

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 the technical field of data processing, and particularly discloses a Huanglongguo meteorological disaster monitoring method and system based on the Internet of Things, and the method comprises the steps: firstly, obtaining a target environment meteorological risk parameter, determining a risk level through model analysis, and synchronously monitoring the image attributes of Huanglongguo fruits, according to a preset threshold value, the planting areas are divided into a qualified type, a critical qualified type and an unqualified type; on the basis, fusing the meteorological risk and a region classification result, and implementing hierarchical adjustment; meanwhile, fruit burn characteristic parameters are continuously monitored, and whether secondary adjustment is needed or not is dynamically judged by means of real-time analysis of the Internet of Things; after adjustment, images are collected again for contrastive analysis, and once burn risks or quality decline trends are found, corresponding disaster early warning is triggered immediately; according to the scheme, the intelligent level of management of the growth environment of the Huanglongguo fruits can be effectively improved, early warning of meteorological disasters can be achieved, burn and disease losses are reduced, the fruit quality and yield are improved through accurate regulation and control, and scientific and efficient planting decision support is provided for farmers.

The invention relates to the technical field of mushroom planting, in particular to a mushroom planting system and a control method thereof.The mushroom planting system comprises a mushroom planting chamber, and a sealing door is arranged on one side of the mushroom planting chamber; the cultivation frame is located in the mushroom planting chamber, the cultivation frame is of a latticed frame structure, and multiple rows of cultivation frames are arranged in the mushroom planting chamber; the monitoring system is composed of a walking mechanism capable of moving on the cultivation frame and an image acquisition device; and the temperature control system consists of a plurality of temperature sensors and a central refrigerator which are uniformly distributed. By the adoption of the technical scheme, the monitoring unit can monitor the mushroom growth condition of each planting area on the cultivation frame, then the temperature sensor, located in the area, of the temperature control system feeds the mushroom growth condition back to the central refrigerator, the temperature of the area is adjusted, and the temperature meets the mushroom growth requirement.

The application discloses a river ecological slope protection, and relates to the technical field of new energy building engineering.The river ecological slope protection comprises a slope protection body and a slope protection device, the slope protection body is provided with a water storage tank, a nozzle pipe, a watering nozzle, a watering pipe, a pump, a solar panel and a planting area, the water storage tank is communicated with a river channel through an anti-overflow pipe, water level is dynamically adjusted, and rainwater is collected; the watering system is driven by the pump, and vegetation is irrigated by solar power supply; the slope protection device comprises a self-adaptive protection structure and an aquatic breeding module; after water is stored in a pressure water storage tank, a pressing plate is driven to be pressed down, a linkage movable rod is extended and retracted to protect a plate, water flow erosion is resisted, and soil and water loss of the planting area is avoided; a servo motor controls the lifting of a breeding net cage through a cable and a pulley block, water level change is adapted, and economic value is improved.The river ecological slope protection realizes self-adaptive adjustment of the slope protection structure, efficient utilization of water resources and collaborative optimization of ecological and economic benefits through intelligent control, new energy driving and modular design, and is suitable for river channel management and ecological restoration engineering.

The invention discloses a knowledge graph construction method for alternative planting of poppy, which comprises the following steps: acquiring soil humidity, illumination intensity, air temperature and rainfall data from Internet of Things sensing nodes deployed in an alternative planting area, analyzing a crop growth state in combination with a remote sensing image, and generating a structured environment observation tuple; entity recognition and relation extraction are carried out on policy and regulation texts, agricultural technology manual and historical planting records, and an initial triple set is constructed; inputting the structured environment observation tuple and the initial triple set into a graph neural network encoder to generate a node embedding vector; on the basis of the node embedding vectors, calculating semantic association weights among entities by adopting a graph attention mechanism, and constructing a dynamic adjacency matrix; the objective of the invention is to solve the problems of lack of semantic association, fragmentation of knowledge expression, weak reasoning ability and insufficient cross-domain collaboration of multi-source heterogeneous data in existing alternative planting management.

The invention provides a high-production planting method and system for realizing agricultural multi-dimensional elements based on geographic information, and relates to the technical field of geographic information, and the method comprises the steps: S1, obtaining multi-source geographic information data of a target planting region, comprising soil type spatial distribution data, climate condition statistical data, landform three-dimensional feature data and pest and disease damage occurrence records; s2, carrying out spatial superposition and correlation analysis on the multi-source geographic information data, and generating a multi-dimensional geographic element comprehensive layer integrating the soil fertility grade, the moisture infiltration capacity and the accumulated temperature illumination adaptation degree; and S3, presetting two target detection points in the multi-dimensional geographic element comprehensive layer, wherein the detection points are respectively located at a geographic feature turning point and a region center point of the planting region boundary. By integrating multi-source geographic information, accurate division of planting areas, intelligent matching of varieties and parameters and dynamic regulation and control of growth cycles are realized.

The invention discloses a tobacco planting area suitability evaluation method and device, electronic equipment and a storage medium. Comprising the steps of obtaining multi-dimensional detection data of a target tobacco planting area, and determining at least one piece of ecological factor data of each grid unit of the target tobacco planting area based on the multi-dimensional detection data; for each grid unit, determining a target dynamic weight of each ecological factor based on the at least one ecological factor data of the grid unit; obtaining a plurality of pre-constructed preset levels and membership function parameters corresponding to the preset levels, and determining target membership data of each ecological factor based on the membership function parameters corresponding to the preset levels and the at least one ecological factor data; determining a suitability evaluation result of the grid unit based on the target dynamic weight and the target membership data of each ecological factor in the grid unit; and generating a suitability spatial distribution diagram of the target tobacco planting area based on the suitability evaluation result of each grid unit. According to the scheme, the accuracy of the suitability evaluation result is improved.

The invention is suitable for the technical field of three-dimensional planting management, and particularly relates to a three-dimensional planting field management method and system, and the method comprises the steps: dividing a three-dimensional agricultural planting region, cutting the planting region into a plurality of blocks, collecting the growth data of all agricultural plants through video monitoring equipment disposed in the planting region in advance, and storing the collected growth data in a database; identifying a risk point location, selecting an infrared anchor point, starting a laser generator integrated in the video monitoring equipment in advance, and projecting infrared laser to the infrared anchor point; sending a working instruction to the inspection equipment, shooting a snapshot of the planting area, reading the projection positions of the infrared anchor points, integrating all the infrared anchor points, generating an anchor point group, and positioning the real-time position of the inspection equipment when the real-time position coincides with the projection positions. By determining the management report, defective plants in three-dimensional planting can be found in time, health management of agricultural plants is enhanced, and intelligence and refinement of agricultural production are effectively improved.

The invention provides a coastal mud flat suaeda salsa restoration suitable area evaluation method and system, and the method comprises the steps: constructing evaluation indexes and suitability grading standards comprising water environment, local vegetation, substrate environment and threat factors, and forming a basic vector pattern spot based on water environment data; assigning scores to the substrate environment data, the local vegetation condition and the threat factor condition, superposing the scores to the basic vector pattern spots, calculating suitability scores of the substrate environment vector pattern spots, the local vegetation vector pattern spots and the threat factor vector pattern spots respectively corresponding to weights, and grading the pattern spots based on the suitability grading standard to obtain a suitability grading result; and combining the pattern spots of the same grade, generating a spatial distribution diagram of the suaeda salsa restoration suitable area, visually dividing a planting area suitable for suaeda salsa, providing a spatial basis for suaeda salsa restoration, and significantly improving the success rate of suaeda salsa restoration.

The invention provides a soybean growth monitoring method based on satellite remote sensing, and relates to the technical field of crop growth monitoring, and the method specifically comprises the steps: setting two monitoring nodes according to a soybean growth period, and collecting multispectral and thermal infrared remote sensing images and normal growth leaf area density and stem diameter sample data; a soybean area and a non-soybean area are distinguished by using a vegetation index threshold, cloud / shadow pixels are eliminated by using a multiband combination-threshold method, effective pixels are screened, and two types of remote sensing data are matched. Extracting leaf area density, canopy temperature and collaborative inversion stem diameter from the effective data; calculating the relative growth rate of the two types of indexes based on samples, and constructing a collaborative growth reference equation and a residual standard deviation; and substituting the growth rate of the planting area into an equation to obtain a theoretical value, calculating a deviation value, and judging the soybean growth state by combining the comparison between the deviation and a normal fluctuation interval and the positive and negative characteristics of the growth rate.

The invention relates to a facility crop transplanter which comprises a rack, a front walking mechanism arranged at the front end of the rack and a rear walking mechanism arranged at the rear end of the rack, a seedling taking mechanism, a duckbilled planting device and a seedling feeding mechanism are sequentially arranged at the upper end of the rack at intervals from front to back in the length direction of the rack, and the seedling feeding mechanism is used for conveying hole trays in the length direction of the rack; a transverse moving mechanism is arranged at the lower end of the seedling taking mechanism, the transverse moving mechanism is used for driving the seedling taking mechanism to move in the width direction of the rack, and the seedling taking mechanism is used for taking out pot seedlings from the hole tray and can drive the pot seedlings to reciprocate between the transverse moving mechanism and the seedling conveying mechanism; a seedling blocking piece is arranged above the duckbilled planting device, and when the seedling taking mechanism takes out the pot seedlings from the hole tray and moves towards the transverse moving mechanism, the seedling blocking piece is used for blocking the pot seedlings and enabling the pot seedlings to fall into the duckbilled planting device. According to the transplanting machine, flexible operation and efficient transplanting in scattered small planting areas can be achieved, and the applicability problem that a large field transplanting machine cannot be applied in facilities is solved.

The invention relates to the technical field of aquaculture and ecological environment management, and discloses a novel pond ecological circulation culture method which comprises the steps that a pond is divided into four vertical functional areas including an upper-layer floating bed planting area, a middle-layer net cage culture area, a bottom-layer benthic purification area and a microbial conversion area, a water circulation system is arranged in a matched mode, the area needed by pond site selection is 3000-4000 m, and the water depth is 3.0-3.5 m. The water quality is automatically controlled through a sensor containing dissolved oxygen and the like, and the biological monitoring module is automatically controlled through a high-definition camera and an AI to acquire data in real time, trigger oxygenation, move the net cage and the like; mixed feeding of'basal feed and insect protein ', cross-season crop rotation of floating bed crops and batch harvesting of benthic organisms are performed daily, equipment is maintained regularly, and ecological circulating breeding is achieved.

The invention relates to the technical field of intelligent agriculture, in particular to an agricultural seeding machinery automatic control system based on a ground environment, and the system comprises a data collection module which is used for collecting the environment data of a target planting area, the soil data of a plurality of collection points, and the previous crop data of a plurality of previous crops; the soil analysis module is used for dividing the target planting area into a plurality of planting sub-areas; the environment analysis module is used for determining an environment influence index; the crop analysis module is used for determining crop distribution characteristics of a single planting sub-region and determining an edge influence index corresponding to each adjacent planting sub-region; and the seeding analysis module is used for determining an initial seeding parameter of a single planting sub-region, and determining whether to adjust the initial seeding parameter of the single planting sub-region or not based on the environment influence index and the edge influence index corresponding to each adjacent planting sub-region. According to the invention, multi-dimensional data sensing can be realized, the seeding parameters are accurately determined, and the seeding quality is improved.

The invention discloses an intelligent wheat disease diagnosis method and system based on a multi-modal large model. The method comprises the following steps: constructing a knowledge graph; obtaining a geographic position and current time of a user, positioning a wheat planting area based on the geographic position, calculating a wheat growth period in combination with the current time and the wheat planting area, and selecting a corresponding knowledge graph sub-graph according to the wheat growth period and the planting area; collecting texts and pictures input by a user through guiding type interaction; extracting text keywords, and identifying disease sites, disease types and disease symptoms in the pictures by using a deep learning model; fusing the text analysis result and the image analysis result to form a multi-modal feature; the optimized features are screened from the multi-modal features; a keyword set is decoded from the optimized features; generating a query statement based on the keyword set; and retrieving answers from the knowledge graph sub-graphs based on the query statements, and generating response contents based on the answers and feeding back the response contents to the user. The accuracy and efficiency of wheat disease diagnosis are improved.

The invention relates to the technical field of agricultural data analysis, in particular to a crop disease and insect pest data prediction method and system, and the method comprises the following steps: obtaining environment parameters of a planting area, positioning a mutation point, determining a species diffusion risk range, carrying out the statistics of temperature and humidity change frequencies, obtaining species diffusion frequency data, and recognizing environment factor correlation indexes. Screening a deviation node set, analyzing the response time difference, carrying out clustering analysis, and generating a dynamic change prediction data set. According to the method, by accurately capturing environmental parameter changes and monitoring key nodes of a propagation path in real time, the species diffusion range and intensity can be efficiently identified, pest and disease damage occurrence can be accurately predicted, pest and disease damage trends in different areas can be effectively predicted, and the limitation that a traditional method depends on meteorological data and historical records is overcome; the node density change is evaluated and normalized through a clustering analysis method, the dynamic change prediction precision of the planting area is improved, and more effective decision support is provided for agricultural managers.

The invention relates to the technical field of agricultural data visualization, in particular to a salinized cotton field visual multi-point salinity collection and analysis method and system.The method comprises the following steps that soil conductivity distribution data of a salinized cotton field area is obtained, a high-salinity accumulation area and a low-salinity dilution area are recognized, particle gap distribution characteristics are collected, and a salinized cotton field is obtained; and screening areas with loose or compact particle structures, executing temperature gradient analysis, screening surface thermal environment abnormal subareas, detecting wind speed distribution, removing wind field disturbance areas, and obtaining a recommended planting area list. According to the method, high and low salt areas are accurately identified through salt space distribution analysis, distribution precision is improved, humidity dynamic expression is refined, structural abnormal blocks are screened by using a particle gap change trend, soil management is optimized, surface temperature and wind speed data analysis is superposed, thermal disturbance and wind field abnormity are eliminated, and agricultural management precision is improved through multi-dimensional data fusion. The capability of coping with a complex environment is enhanced, efficient decision making is effectively supported, and the overall agricultural production benefit is improved.

The invention relates to the technical field of agricultural planting, and discloses a winter wheat rain-suitable planting configuration optimization method and system based on drought year identification, and the method comprises the steps: constructing a drought year identification index system, collecting the data of each drought year identification index in a target planting region in real time, carrying out the preprocessing of the collected data, and carrying out the calculation of the data; pre-processed data is obtained; inputting the preprocessed data into a drought year type identification model, and identifying the current drought year type of the target planting area through the drought year type identification model; determining planting configuration parameters of winter wheat suitable for rain according to the identified drought type in combination with historical rainfall data and soil characteristics of the target planting area; monitoring parameter information in real time through a sensor to obtain monitoring data, and adjusting planting configuration parameters according to the monitoring data so as to enable winter wheat to obtain optimal growth conditions in different drought years; according to the method, different drought years can be accurately identified, and a reliable basis is provided for winter wheat rain-suitable planting configuration optimization.

The invention discloses a smart agriculture management method based on ecological agriculture, and relates to the technical field of smart agriculture management, in a soil information processing link, through calculating a soil suitability index, scientifically evaluating the suitability of a specified area to a specific crop, assisting in reasonable planning of planting, and in a land parcel division process, determining the suitability of the specified area to the specific crop; a geographic information system and a clustering algorithm are utilized to realize dynamic land parcel division, high-value planting areas are marked according to soil suitability indexes, unmatched land parcels are improved, land utilization efficiency is improved, healthy growth of crops is guaranteed by calculating irrigation amount and prevention and treatment probability, optimizing planting strategies, and ecological benefit evaluation is realized by calculating ecological benefit scores in the aspect of ecological benefit evaluation. Whether the planting strategy reaches the standard or not is judged, agricultural sustainable development is promoted, planting decision accuracy is effectively improved, resource waste is reduced, economic benefits and ecological benefits are improved, and powerful support is provided for ecological agriculture development.

A modular ecological floating island with a plant-microbe combination, characterized in that it comprises the following: a floating shell body which is provided with a cavity extending through a thickness direction of the floating shell body; a planting trough that has a tubular structure with an open top and a closed bottom and is removablely arranged within the cavity, where the underside of the planting trough is lower than the lower surface of the floating bowl body; a filter screen that is attached horizontally inside the planting trough and divides the planting trough into an upper planting area and a lower bioreaction area; where aquatic plants are planted in the planting area; wherein the floating shell body is provided on one side with a tenon connection structure, wherein the adjacent floating shell bodies are joined horizontally and vertically by the tenon connection structure.

The utility model provides a soil sampling device for detecting a plant planting area. The soil sampling device comprises a positioning plate, a rotary drum arranged on the lower side of the positioning plate, a plurality of groups of sliding parts and a synchronous driving assembly, the rotary drum is rotationally connected with the positioning plate and is in transmission connection with a first rotation driving component; a plurality of through holes are formed in the rotary drum, the plurality of groups of sliding parts are inserted into the plurality of through holes in a one-to-one correspondence manner, each group of sliding parts comprises two sliding parts which are arranged at an interval along the axial direction of the through hole, and each sliding part is detachably connected with a sampling cup which is opened towards the circumferential direction of the rotary drum; the synchronous driving assembly is arranged on the rotary drum and is in transmission connection with the multiple groups of sliding parts, so that the two corresponding sliding parts move towards each other or back to back, and the sampling cups extend out of the rotary drum or retract into the rotary drum. According to the soil sampling device provided by the invention, multiple groups of soil samples at different depths can be collected at a time through the multiple sampling cups, so that the accuracy of soil sample collection is ensured, and the reliability of subsequent soil detection results is further ensured.

The embodiment of the invention provides a forestry planting device capable of automatically pulling out seedlings, and relates to the field of seedling equipment. Comprising a seedling raising cylinder, an independent planting area, a spreading mechanism and a driving mechanism, the six independent planting areas are arranged in the seedling raising cylinder at equal intervals in the circumferential direction; the spreading mechanism is rotationally mounted at the bottom end of an inner cavity of the seedling raising cylinder; the driving mechanism is mounted in the seedling raising cylinder; the driving mechanism is controlled to drive the whole sowing mechanism to rotate in a reciprocating mode, and soil in the seedling raising cylinder is loosened; the spreading mechanism comprises a rotating ring, a fixed arm, a first shifting rod, a fixed ring, a swinging arm, a second shifting rod, a through groove and a sliding rod. The equipment can loosen soil in the seedling raising cylinder, meanwhile, the situation that seedling roots are broken due to the too large swing distance is avoided, and the transplanting survival rate can be increased; meanwhile, seedling branches can be clamped, the seedlings can be automatically pulled out, and then transplanting work is facilitated.