1201 results about "Growing region" patented technology

The present invention relates to the technical field of agricultural irrigation. Disclosed are an intelligent irrigation strategy formulation method and system. The method comprises: determining the current irrigation strategy on the basis of a preset irrigation decision-making model and a Q-learning intelligent agent and according to the current agricultural state; on the basis of the current irrigation strategy, performing irrigation simulation on a target growing region to obtain a corresponding irrigation reward value and the next agricultural state, wherein the irrigation reward value is determined on the basis of the crop yield, the crop water demand, and the annual economic cost after irrigation, the current agricultural state, the current irrigation strategy, the irrigation reward value, and the next agricultural state form an empirical four-tuple, and a plurality of empirical four-tuples form an experience pool; and randomly sampling the empirical four-tuple from the experience pool as a training sample to train the Q-learning intelligent agent so as to obtain an optimal irrigation Q-value table which is used for determining a corresponding optimal irrigation strategy on the basis of any agricultural state of the target growing region. The present invention can efficiently obtain an accurate irrigation strategy.

The invention discloses a melon vegetable virus disease identification method and system constructed based on a sensor array. The method comprises the following steps: generating a dynamic thermodynamic diagram by collecting insect activity data of a target planting area, constructing a virus disease propagation distribution diagram in combination with an entomophilous propagation probability model, and deploying an initial sensor array according to the virus disease propagation distribution diagram; plant physiological parameters and environmental data are obtained in real time through multiple sensors, a virus disease recognition model fused with entomophilous activity characteristics is constructed, and early diagnosis of diseases is achieved; and evaluating the efficiency of the sensor array based on a recognition result, and dynamically adjusting the array layout by adopting a self-adaptive optimization algorithm to form an optimized network with balanced monitoring precision and resource efficiency. The virus disease detection sensitivity and accuracy are remarkably improved, the monitoring cost is reduced, and a digital solution is provided for prevention and control of diseases of melon crops.

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 remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention provides a crop planting environment data acquisition and real-time optimization device integrated with the Internet of Things technology, which comprises a data acquisition module, a data cleaning and feature extraction module, an environment optimization strategy generation module, a decision support and execution module, an intelligent learning and optimization module and a monitoring and data visualization module, relates to the field of communication and data processing. The environment data is collected in real time through the distributed Internet of Things equipment, the planting area is fully covered, the problem of insufficient data acquisition in the prior art is solved, and a reliable data source is provided for subsequent optimization. The real-time performance and comprehensiveness of data acquisition are improved, and the accuracy of basic data optimization is ensured. An adaptive signal enhancement model is introduced, complex interference is effectively identified and eliminated, the intensity of weak signals is enhanced, and the problem that the optimization effect is reduced due to data noise in the prior art is solved. The precision and efficiency of data processing are improved, and high-quality input is provided for an optimization model.

The invention provides an intelligent irrigation control method and system for a high-standard farmland. The method comprises the following steps: acquiring crop canopy image data and farmland terrain elevation data of different crop planting areas in the high-standard farmland; determining a crop growth stage based on morphological characteristic data in the crop canopy image data, and constructing a soil hydrodynamic simulation model according to a root distribution depth interval and a theoretical water absorption rate coefficient corresponding to the crop growth stage; and based on the farmland terrain elevation data and the soil porosity parameter, generating a soil moisture migration parameter by using a soil hydrodynamic simulation model, generating a pipe network pressure regulation and control instruction in combination with the soil moisture migration parameter and a preset irrigation strategy, and adjusting the water outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation and control instruction. Generating an irrigation control instruction; accurate space-time matching of irrigation of the multi-crop rotation area is achieved, the water utilization efficiency is improved, and the problem of irrigation uniformity at the terrain difference and the rotation boundary is solved.

The invention discloses a farmland soil data digital analysis method and system, and the method comprises the steps: obtaining a farmland image, and dividing the image into different planting regions according to plants; based on the images of different planting areas, dividing the images into a plurality of sub-areas, obtaining soil samples in each sub-area, and analyzing the samples to obtain soil fertility in the sub-areas; based on the land fertility of different time nodes in each sub-region, obtaining a land fertility change condition; and comprehensively evaluating and analyzing the soil fertility change condition of the planting area based on the soil fertility change condition in each sub-area. The farmland soil fertility monitoring system has the advantages that accurate monitoring and dynamic tracking of farmland soil fertility are realized through image analysis and soil sample collection, and scientific decision support is provided for agricultural production. Through analysis of the soil fertility change trend, optimization of a fertilization scheme is facilitated, the land utilization efficiency is improved, and sustainable agricultural development is promoted.

The invention is suitable for the field of crop fertilization control, and provides a fertilization control method and system based on corn protective farming, and the method comprises the steps: synchronously collecting time-space multi-dimensional data of a target planting region, and constructing a multi-modal data cube; generating a seed fertilizer ratio decision vector; according to the seed-fertilizer ratio decision vector, controlling the multi-channel ratio of mechanical precision seed-fertilizer simultaneous sowing equipment, so as to realize the dynamic ratio and variable fertilization of the seed fertilizer; a multi-spectral image and soil moisture content data are collected through an unmanned aerial vehicle group, and a space-time continuous crop growth monitoring network is constructed; dynamically adjusting the topdressing amount and the water and fertilizer concentration of the unmanned aerial vehicle group based on the growth monitoring network data; according to the method, by constructing a multi-modal data cube, the dynamic proportioning and variable fertilization of seeds and fertilizers are realized, the proportioning precision is improved by utilizing fuzzy PID and machine vision, a space-time monitoring network is constructed by virtue of an unmanned aerial vehicle group, the dynamic adjustment of topdressing is realized, and a data-driven closed-loop fertilization system is formed.

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 provides an intelligent agricultural irrigation optimization method and system, and relates to the technical field of agricultural management. The method comprises the following steps: acquiring first irrigation monitoring data of a plurality of sub-irrigation areas in a target planting area, calculating a temperature influence parameter and a humidity influence parameter of each sub-irrigation area, and constructing an irrigation feature vector of each sub-irrigation area; clustering the plurality of sub-irrigation areas to construct a plurality of irrigation area sets, calculating a water demand urgency factor of each irrigation area set and optimizing an initial irrigation scheme to obtain a first target irrigation scheme, and collecting second irrigation monitoring data of the plurality of sub-irrigation areas, and calculating an irrigation gain parameter of each irrigation area set, and optimizing the first target irrigation scheme to generate a second target irrigation scheme. According to the invention, accurate and efficient irrigation water distribution is realized.

The invention relates to the technical field of intelligent agricultural information processing, and provides a crop variety suitable planting area recommendation method and device based on deep reinforcement learning, and the method comprises the steps: obtaining crop variety features, environment data of candidate planting areas, and a pre-trained crop variety suitable planting area recommendation model; the recommendation model is trained through the following mode: training a pre-constructed intelligent agent based on variety planting data in different environments and environment data of planting sites and non-planting sites, and obtaining a trained deep reinforcement learning model. And adjusting the trained deep reinforcement learning model based on the variety promotion condition data to obtain a crop variety suitable planting area recommendation model. The recommendation model can accurately match the crop variety and the planting environment according to the input crop variety characteristics and the environment data of the candidate planting area, and accurate recommendation of the crop variety suitable planting area 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 discloses a radix ranunculi ternati planting monitoring method and a radix ranunculi ternati planting monitoring system based on the Internet, and relates to the technical field of planting management. Environment state parameters and radix ranunculi ternati state parameters in the radix ranunculi ternati planting area are collected in real time through installed environment monitoring Internet of Things equipment, and meanwhile the collected environment state parameters and the collected radix ranunculi ternati state parameters in the radix ranunculi ternati planting area are processed through a data processing method; the method comprises the following steps: processing environment state parameters of environment regulation and control Internet of Things equipment, setting catclaw buttercup root planting standard environment parameters after processing is completed, analyzing the processed environment state parameters and catclaw buttercup root state parameters based on the set standard environment parameters, finally collecting regulation and control parameters of the environment regulation and control Internet of Things equipment and the analyzed environment state parameters and catclaw buttercup root state parameters, predicting the regulation and control parameters in real time, and obtaining the regulation and control parameters of the environment regulation and control Internet of Things equipment. And the operation of the environment regulation and control Internet of Things equipment is controlled based on the regulation and control parameters predicted in real time, so that the real-time performance of planting monitoring of the radix ranunculi ternati is improved.

An integrated water and fertilizer spraying device for modern agriculture, provided with a water and fertilizer irrigation machine (1) localized and assembled on the ground, wherein a primary pipe (2) is mounted on the front side of the water and fertilizer irrigation machine on the outer surface thereof, and secondary pipes (3) are mounted on the sides of the primary pipe on the outer surface thereof. The device comprises clamping assemblies (4), each nested and connected to the upper side of the outer surface of the corresponding secondary pipe, wherein a moving assembly (5) is mounted on the rear side of each clamping assembly on the outer surface thereof. The integrated water and fertilizer spraying device for modern agriculture is provided with the clamping assemblies that facilitate localization of the secondary pipes, thereby improving the use stability of spraying heads assembled with the secondary pipes; the provided clamping assemblies are used in cooperation with protective pads and clips and clamps work in combination, so as to prevent damage of the secondary pipes; integral assembling of the clamping assemblies and the moving assemblies enables efficient movement along a localization rod and a suspension rod, allowing control of the heights of the spraying heads; and by means of a base mounted on the lower side of the localization rod, the spraying head can be localized within a planting area.

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 present invention belongs to the technical field of saline-alkali land improvement, and specifically relates to a method for improving saline-alkali land based on an efficient cultivation mode of alfalfa, oats and okra, comprising the following steps: S1. Plant alfalfa on the saline-alkali land, and let the alfalfa overwinter after being mowed; S2. Interplant oats and alfalfa in rows, and harvest the oats after they mature; S3. After harvesting the oats, plant okra in the planting area of the oats. The okra and alfalfa are interplanted in rows. After the okra matures, pick the okra, then cut the above-ground part of the okra and spread it flat between the planting rows; S4. During the planting period of oats and okra, mow the alfalfa multiple times, and let the alfalfa overwinter after the last mowing. By means of the efficient cultivation mode of interplanting alfalfa and oats, interplanting alfalfa and okra, and rotating oats and okra, the present invention improves the yield and quality of alfalfa and enhances the improvement effect of alfalfa on saline-alkali land.

The invention belongs to the technical field of sand shrub planting, particularly relates to a shrub planting device used under a sand photovoltaic panel, and aims to solve the problems that an existing device is low in planting efficiency and has large limitation in shrub planting, the following scheme is provided, and the shrub planting device comprises a driving walking assembly, a pit digging assembly, a planting assembly and a soil filling assembly; the planting device can advance in the set direction under a sand photovoltaic panel by driving the walking assembly, shrub planting is conducted on the two sides of the device after the walking assembly is driven to advance by a certain distance, the one-time planting hole site digging process and the planting process are synchronously conducted, and the planting efficiency is improved. The planting efficiency of the shrubs under the sand photovoltaic panel is improved, after the shrub saplings are planted in the holes, the dug planting holes can be filled and leveled up through the soil filling assembly, the whole planting process of the shrubs is achieved, and due to the fact that the planting areas are located on the two sides of the driving walking assembly, planting cannot be affected by the heights of the shrub saplings.

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 discloses a leaf vegetable disease detection method based on few sample learning and prototype attention. The method comprises the following steps: S1, preprocessing and enhancing data; s2, performing multi-scale feature extraction on the image; s3, global feature modeling is carried out; s4, target detection and semantic segmentation; and S5, carrying out few-sample optimization and fine tuning. According to the leaf vegetable disease detection method based on few-sample learning and prototype attention, a few-sample learning network architecture is introduced for solving the problem of agricultural disease data sample scarcity, rapid and accurate disease area identification and segmentation can be realized under a limited data condition, target detection and semantic segmentation tasks are combined, and the detection efficiency is improved. Disease positioning and fine-grained segmentation are carried out, the detection precision and segmentation performance are improved, prototype extraction and an attention mechanism are fused, the learning ability of the model under the low-sample condition is effectively improved, the method can be applied to disease detection and control of the vegetable planting area in real time, and efficient disease management of farmers is supported.

The utility model discloses an epiphytic plant planting plate capable of temporarily storing water, which comprises an injection-molded plate body, a plurality of regular hexagonal through holes which penetrate through the plate body are regularly arranged on the plate body, hollow tower-shaped structures are injection-molded on the regular hexagonal through holes on the front side of the plate body, the tower-shaped structures protrude upwards at the tops of the regular hexagonal through holes, and the top of the plate body is provided with a plurality of through holes. The tower-shaped structure comprises a plurality of uniformly distributed tower edges, a plurality of tower holes are uniformly formed among the plurality of tower edges, water can be attached to the tower holes and the tower edges under the action of surface tension, each tower hole is communicated with the regular hexagonal through hole, and the lower ends of the plurality of tower edges are correspondingly fixed with a plurality of uniformly distributed inner angles of the regular hexagonal through hole; the upper ends of the multiple tower edges extend upwards and then are fixed to the center line of the regular hexagon through hole, planting areas are formed between the adjacent tower-shaped structures, and the multiple planting areas are evenly distributed on the plate body; the high-strength plastic film has the mechanical properties of high strength, fatigue resistance and the like, can increase the humidity environment around plants, and is good in air permeability.

The invention discloses a regional soil monitoring method, which comprises the following steps: dividing a planting region and a growth cycle, acquiring remote sensing image data of the planting region, and acquiring real-time soil moisture content data; correcting the soil moisture content data by adopting multiple vegetation indexes, identifying soil texture by adopting a convolutional neural network random detection model, and obtaining soil data features; dividing soil moisture content values of different depths according to the soil data characteristics, and obtaining parameter factors influencing the soil moisture content; according to the parameter factors, a soil texture identification model is adopted to identify soil sample features, a soil monitoring and positioning model is established, real-time soil monitoring is performed on different scenes, a more comprehensive data view angle is provided, real-time monitoring of soil conditions is realized, soil texture feature data can be acquired and processed in real time, and the real-time monitoring of the soil conditions is realized. And dynamic monitoring requirements under different scenes are supported, challenges under complex environmental conditions are dealt with, and continuity and reliability of soil monitoring tasks are ensured.

The utility model relates to the technical field of greenhouses, and provides a ventilation circulation device for greenhouses, including: a heating and ventilation component, a cooling and ventilation component, a cold air diaphragm, a temperature sensor, a top air pipe, a bottom air pipe and a greenhouse, the heating and ventilation component and the cooling and ventilation component are arranged on the outside of the greenhouse, the top air pipe is arranged at the top of the greenhouse, the bottom air pipe is arranged at the bottom of the greenhouse, the output end of the heating and ventilation component is connected to one end of the bottom air pipe, the output end of the cooling and ventilation component is connected to one end of the top air pipe, the cold air diaphragm is arranged on the lower side of the top air pipe, and the temperature sensor is arranged in the planting area of ​​the greenhouse. The utility model can heat the gas inhaled from the outside when the external ambient temperature is low, thereby preventing the temperature and dryness and humidity on the surface of the fruit from changing drastically, and avoiding the "dry edge" of the crop leaves and the "chapped skin" of the fruit.

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 discloses a radix ranunculi ternati planting system and method with real-time pest and disease damage monitoring and early warning functions, and relates to the field of unknown technologies, soil strains of all alternative planting areas in a target farmland are detected, then all planting areas of radix ranunculi ternati are obtained through analysis, a radix ranunculi ternati planting scheme and a monitoring scheme are determined, and the planting efficiency of radix ranunculi ternati is improved. The soil strain change condition and the radix ranunculi ternati pest and disease damage condition of each planting area are continuously monitored, the radix ranunculi ternati pest and disease damage risk index of each planting area is obtained through analysis, then corresponding early warning prompt is conducted according to the radix ranunculi ternati pest and disease damage risk index of each planting area, and it is ensured that the occurrence signs of pest and disease damage can be found in time; therefore, the disease and pest spreading caused by inaccurate disease and pest analysis and untimely discovery can be avoided, the completeness and accuracy of the disease and pest real-time monitoring and early warning system can be ensured, and the long-term development of the radix ranunculi ternati planting industry can be facilitated.

The invention relates to a high-carbon biological organic conditioner and application thereof in red soil peanut planting, the high-carbon biological organic conditioner comprises mineral powder, oyster shell powder, zeolite powder, potassium fulvic acid, decomposed cow dung, alanine and glycine according to a mass part ratio of 800: 200: 10: 50: 140: 0.7: 0.5, the content of organic matters in the high-carbon biological organic conditioner is not less than 45 wt.%, the content of humic acid is 20 wt.%, the content of potassium fulvic acid is 20 wt.%, and the content of potassium fulvic acid is 20 wt.%. And bacillus subtilis is contained. By applying the high-carbon biological organic conditioner, the soil quality of the red soil peanut planting area is rapidly improved, the peanut yield is remarkably increased, and an effective technical solution is provided for agricultural sustainable development of the red soil area.