2151 results about "Paddy field" patented technology

Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.

The invention relates to the technical field of agricultural irrigation, in particular to a rice water-saving intelligent irrigation decision-making method and system, and the method comprises the following steps: determining the basic information of a target rice field, and obtaining a rice intelligent water-saving irrigation path by using a random landmark method and an adaptive heuristic optimization algorithm; the water demand of rice in the target rice field is obtained in real time, and then a water demand prediction model is constructed based on CEEMD and LSTM; predicting the water demand of the target rice field by using a water demand prediction model, and obtaining rice water-saving irrigation parameters of each irrigation point on the rice intelligent water-saving irrigation path in combination with rainfall information and a rice growth stage; according to the rice water-saving irrigation parameters, the irrigation system is used for completing irrigation of the target rice field. According to the method, the accuracy and the intelligent degree of rice irrigation decision making can be improved, the complexity of rice irrigation decision making and the dependence on expert experience are reduced, and more optional schemes are provided for rice irrigation control.

The invention relates to the field of agricultural data analysis, in particular to a japonica rice paddy field soil carbon nitrogen ratio dynamic intelligent assessment method, which comprises the following steps: acquiring and preprocessing japonica rice paddy field environment time sequence data to obtain a standardized environment data set; performing nonlinear congestion analysis on the soil moisture content state to obtain a corrected moisture dimension distance component; obtaining a corrected temperature dimension distance component by evaluating the metabolism permissible degree of the soil moisture on the temperature effect; performing double weighted fusion on the moisture and temperature correction distance components to obtain a soil environment state clustering result; through coupling calculation of a soil environment state clustering result and a first-order dynamic decomposition model, a dynamic intelligent evaluation result of the carbon-nitrogen ratio of the japonica rice paddy field soil is obtained, so that the problem that existing clustering cannot accurately distinguish aerobic and anaerobic environment states in a japonica rice paddy field scene and causes deviation of the evaluation result of the carbon-nitrogen ratio of the soil is solved.

The invention discloses a rice disease and insect pest early warning method and system based on image recognition, and particularly relates to the technical field of insect pest recognition, different areas of a rice field are comprehensively covered through multi-source high-resolution image collection, it is ensured that the image quality reaches a preset threshold value, median filtering and histogram equalization are adopted in the image preprocessing step, and the image quality is improved. Based on a geographic information system and a Kriging interpolation method, a spatial heterogeneity model of rice field disease transmission is established, a deep convolutional neural network is used for carrying out feature extraction on the image, the type, position and severity of diseases and pests are accurately recognized, the extracted features are combined with the spatial heterogeneity model, and the accuracy of the disease and pest detection is improved. A spatial autocorrelation analysis method is adopted to deeply evaluate the distribution mode of diseases in the rice field, and a support vector machine algorithm is applied to perform classification evaluation on the disease and pest risk, so that the problems of early warning lag and missing report caused by disease spatial heterogeneity are effectively solved, and the accuracy and timeliness of early warning are improved.

The invention relates to the technical field of agricultural fertilization, in particular to a precise fertilization method and system based on rice field nutrient real-time monitoring. The method comprises the following steps: acquiring map data of a rice field; dividing the rice field into a plurality of uniform areas by using a random sampling algorithm and an expansion algorithm; acquiring soil data and nutrient data of the rice field; constructing a rice field region comprehensive evaluation model used for comprehensively evaluating the difference between rice field region nutrients and soil characteristics; classifying the uniform regions according to the difference between nutrients and soil characteristics, and obtaining classified regions; constructing a rice field nutrient demand analysis model; constructing a rice field precise fertilization model; the fertilization amount is determined, and accurate graded fertilization is realized. According to the method, the nutrient and soil characteristics of the rice field are comprehensively evaluated in different regions, the fertilization priority sequence is determined, the nutrient requirements of all the regions are accurately matched to specific fertilizers and fertilization amounts, tight connection of the regions, the nutrients, the fertilizer types and the fertilization amounts is ensured, and precise fertilization management is achieved.

The invention discloses a microbial agent and a method for repairing cadmium-polluted rice field soil by using the microbial agent, relates to the technical field of repairing cadmium pollution of rice field soil, and solves the problem that the cadmium metal enrichment capacity of duckweed is limited due to the fact that the duckweed has certain endurance capacity to cadmium stress. The microbial agent comprises bacillus subtilis ATCC6633 and pseudomonas aeruginosa ATCC27853, and the mass ratio of the bacillus subtilis ATCC6633 to the pseudomonas aeruginosa ATCC27853 is 2: 1, and the method comprises the following steps: step 1, spreading the microbial agent on the surface layer of cadmium-polluted rice field soil, then mixing the microbial agent with the rice field soil, raking the rice field soil, adjusting the water-holding capacity of the rice field soil, applying a base fertilizer, and irrigating the rice field. According to the method, the cadmium tolerance of the duckweed can be promoted, so that the enrichment capacity of the duckweed on cadmium is promoted, the remediation capacity of the duckweed on cadmium-polluted soil is improved, the cadmium content in the soil can be reduced, and the content of metal cadmium in rice is also reduced through a competitive cadmium absorption way.

The invention discloses a rice field irrigation drainage strategy dynamic optimization method and system. The method comprises the steps of collecting environmental parameters of a target rice field in a current decision period; determining a to-be-optimized target of the target rice field, and constructing a multi-target optimization model in combination with the environmental parameters; performing dynamic planning solution on the multi-objective optimization model by adopting an intelligent algorithm to obtain a real-time optimized irrigation drainage strategy of the current decision period; implementing a real-time optimized irrigation drainage strategy of the current decision period; monitoring whether the final period of the rice growth period is reached when the current decision-making period is ended or not: otherwise, acquiring actual environment parameters when the decision-making period is ended, correcting the state vector at the moment, updating the initial state vector of the next decision-making period of the target rice field through the transfer function, and repeating the solving and implementation process of the irrigation and drainage strategy optimized in real time; if yes, the circulation is ended, and the optimal irrigation drainage strategy in the whole growth period of the target rice field is obtained.

A basement roof lawn vegetation construction method under complex working conditions comprises the steps of implementing complex basement roof modeling and implementing lawn vegetation above a basement roof. The complex basement top plate modeling method comprises the steps of a flat site large-area backfill construction method, a step-shaped basement top plate backfill construction method and a basement top plate large-gradient inclined plate earthwork backfill construction method. The step of implementing the lawn vegetation above the basement top plate comprises the steps of cross construction, backfilling and excavation, slope toe construction and solid soil area construction. According to the technology, the problem of construction of greening in a complex shape on the basement roof of a project is solved, particularly the problem that more slope roofs in a mountaintop shape or roofs in a stepped rice field shape exist is solved, the problem that backfill soil construction is difficult after the basement roof is complex in shape is solved, the stability of backfill soil after backfill soil is improved, and the construction period is shortened. And the concept of green building is practiced.

The invention relates to the technical field of sowing methods, and provides a full-amount straw returning sowing method for high rice yield and rice field emission reduction, which can realize the uniform penetration rate of straws to more than 90%, promote the increase of oxygen content in a plough layer and rhizosphere, effectively promote the oxidation of rice field methane, reduce methane emission and relieve the poison of reducing substances to rice roots. The method effectively relieves the problems of early-stage runt seedlings and later-stage withering of rice under straw returning, saves cost, realizes rice yield increase, and comprises the following steps: 7-10 days before rice harvesting, draining water in a rice field, and adopting a harvester with a straw crushing function and a throwing device for harvesting during harvesting, the method comprises the following steps: chopping straws, uniformly scattering the chopped straws in a field, turning over as soon as possible after rice is harvested, soaking the field in shallow water of 1-2 cm on the field surface for half a day before rice transplanting, performing rotary-tillage-free soil preparation and stubble burying, selecting a rice variety suitable for local climate and soil conditions for seeding, and performing field management and strengthening field disease and pest monitoring after seeding.

The remediation method for the moderately and severely cadmium-polluted rice field aims at strictly controlled cadmium-polluted rice fields, and is divided into two stages of strict control and safe utilization for remediation. In the strict management and control stage, an activating agent is applied to the soil in spring, then microalgae are inoculated, cadmium-enriched plants are planted, the plants are collected after being full of the microalgae, the plants are harvested when the plants grow to the enrichment extreme value, stubbles are reserved for growth, and the steps are repeated till winter; applying an activating agent in winter, planting green manure, and harvesting in spring in the next year; the process is cyclically carried out until the cadmium content of the soil is reduced to a safe utilization level. Entering a safe utilization stage, applying an activating agent in spring, inoculating nitrogen-fixing cyanobacteria, collecting the cyanobacteria after the cyanobacteria overgrow, and repeating the steps until the rice seedlings are transplanted; applying nitrogen-fixing blue-green algae, and planting rice; applying an activating agent after rice harvesting, planting winter green manure, and harvesting in next spring; the process is cyclically carried out until the cadmium content of the soil is reduced to a preferential protection level. The method is low in cost, high in remediation efficiency and good in effect, and a new way is provided for cadmium-polluted rice field remediation.

The invention relates to the technical field of target detection, in particular to a rice disease and insect pest target detection algorithm based on Mamba and YOLOv8, which comprises the following steps: acquiring a rice field RGB image and preprocessing to generate an abnormal image; inputting the abnormal image into a MedVSSM module, extracting global long-distance dependency features through a Mama state space model, extracting local features such as a polypide contour and scab texture in combination with CNN, and fusing and outputting multi-scale features; performing cross-layer semantic enhancement and path aggregation through a C2F-MedVSSM module, and generating an optimized feature map through dynamic weight adjustment; and the YOLOv8 module outputs a target detection frame, a category and confidence, and a final detection result is obtained after non-maximum suppression processing. According to the method, the Mama global modeling capability and the CNN local feature extraction advantages are fused, feature complementation is realized through a dynamic weight mechanism, the detection precision under the scenes of complex illumination, leaf shielding and the like is effectively improved, and the method has a practical application value for rice disease and pest prevention and control.

The invention belongs to the technical field of data processing, and particularly relates to a rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling and a medium, and the method comprises the steps: collecting rice field nitrogen application information in a target area through literature statistics, dividing rice fields with similar nitrogen application into the same type, and selecting a plurality of typical areas from each type; carrying out soil sampling at fixed intervals in the typical rice field area, analyzing the nitrogen concentration level of the soil, and obtaining representative samples; performing soil column experimental simulation by using the representative sample, and estimating the average emission of specific gas; and determining significant influence factors, setting different carbon-nitrogen coupling situations to carry out an orthogonal simulation experiment, quantifying factor importance, carrying out weighted accumulation to obtain a comprehensive emission estimation value, and evaluating the carbon-nitrogen coupling-based biochar rice field emission reduction capability. The method can effectively estimate the rice field soil emission reduction potential.

The invention provides a rice yield prediction method and system based on multi-source time sequence remote sensing data, and relates to the field of agricultural informatization and remote sensing technology application, and the method comprises the steps: collecting multi-spectral data and laser radar data, and carrying out the precise geographic positioning of the data through a high-precision satellite positioning system, obtaining multi-source unmanned aerial vehicle remote sensing data; performing camera internal orientation, wave band registration, orthoimage splicing and embedding, radiation correction and remote sensing index calculation on the multispectral data to obtain processed multispectral data; and performing point cloud reconstruction and point cloud filtering on the laser radar data, and calculating the height of the rice plant to obtain processed laser radar data. According to the method, multi-source remote sensing data and spatio-temporal data are fused, and remote sensing characteristics of different phenological periods of rice are dynamically analyzed and filtered, so that the precision and reliability of rice field yield prediction are remarkably improved.

The invention provides an agriculture and forestry wet compound system and a comprehensive synergistic method for a rice farming aquatic product area. The agriculture and forestry wet compound system comprises an ecological rice field area, an ecological pond area, a forest and wet compound area, an aquatic product culture area and an ecological hydroponic area. The ecological paddy field area is communicated with the ecological pond area through a pipeline, the ecological pond area is communicated with the forest wetting composite area through a pipeline, the forest wetting composite area is communicated with the aquaculture area through a pipeline, and the aquaculture area is communicated with the ecological hydroponic area through a pipeline. Incoming water containing bottom mud, residual feed and biological debris in the ecological hydroponic area is communicated with the ecological rice field area through a pipeline. Based on different nutrient requirements of each unit, agriculture and forestry wet compounding is taken as a core, and the opposite relationship between water, fertilizer and bait requirements of a rice field and a culture pond and tail water pollution treatment is converted into a coupled adaptive multi-element cooperative relationship.

According to the rice field methane emission prediction method based on the CH4MN model, the influence of the rice field methane emission microorganism process and soil nitrogen is considered, the uncertainty of simulation is reduced, and rice field methane emission is accurately predicted. Comprising the following steps: S1, acquiring rice field agricultural production input, water management measures, meteorological data, observed rice field soil environment information, rice information and microorganism information as input data of a CH4MN model; s2, based on a CH4MN model algorithm, calculating a substrate source and a soil environment required by methane generation, and the influence of soil nitrogen on organic matter decomposition; s3, calculating the influence of soil oxidation reduction potential, microorganisms and rice plants on methane generation and oxidation based on a CH4MN model algorithm; s4, calculating a methane generation rate and an emission rate based on a CH4MN model algorithm by using the methane generation and oxidation influence factors calculated in the step S1-3; and S5, verifying the CH4MN model precision based on point observation information.

The invention discloses a method for repairing arsenic-cadmium combined polluted rice field soil, which comprises the following steps of: reducing the redox potential Eh value of the soil to-100 to-300mV by controlling the height of a flooded water layer of the polluted soil and adding a reducing agent, reducing arsenic in the soil into trivalent arsenic, and simultaneously constructing an indigenous microbial community with high arsenic methylation capability; oxidizing by draining and drying, methylating trivalent arsenic by indigenous microbial communities, and gradually volatilizing arsenic through methylation; and applying a carbon-based functional biological membrane mainly comprising a cadmium passivation flora to stably passivate cadmium. According to the method, the arsenic methylation capacity of microorganisms is improved by precisely regulating and controlling the Eh value, in-situ passivation of cadmium is enhanced through the synergistic effect of the carbon-based carrier and the composite flora, synchronous enhancement of arsenic methylation volatilization and cadmium biological membrane passivation is achieved, the difficulty that an arsenic and cadmium pollution treatment technology is'helpful 'is converted into an acting point of technical breakthrough, and the method is suitable for large-scale popularization and application. The arsenic and cadmium co-treatment of the combined polluted soil is realized.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

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

The invention discloses a method for improving an acidic clayey soil plough layer, which comprises the following steps of: 1, after autumn rice is harvested, draining water in a rice field, and carrying out conventional tillage, soil turning and leveling on the rice field for later use; 2, green manure planting is conducted in the suitable green manure sowing period from October to November; 3, in the full-bloom stage, green manure is smashed, turned, pressed and returned to the field, lime and organic fertilizer are spread, the rice field is deeply ploughed at a time, and the green manure, the lime and the organic fertilizer are deeply buried in soil of the rice field; 4, the rice field is irrigated and soaked in water for a period of time, and then rice is planted. The method can inhibit and stabilize acid, improve the buffer performance of the soil, effectively expand a deep ploughing layer, improve the physical structure of the soil and improve the utilization rate of rainfall resources by the soil, so that the purposes of minimal ploughing, high yield and farmland protection are achieved.

The invention discloses a gleying rice field intelligent irrigation and drainage regulation and control method based on reinforcement learning. The method comprises the steps that meteorological data, soil data and field surface water level data are collected; calculating crop state information based on the meteorological data and the soil data by using a crop growth model; inputting the meteorological data, the soil data, the field surface water level data and the crop state information into an offline training strategy model, outputting action suggestions, and performing verification and decision making according to preset regulation and control logic in combination with the meteorological data, the soil data, the field surface water level data, an external data source and agronomic rule base data; a control strategy is generated, irrigation, drainage or oxygenation is executed, and irrigation and drainage regulation and control are completed. Compared with the prior art, the method has the advantages that priority regulation and control logic for ORP / pH value real-time feedback is established, a prospective pre-drainage mechanism for rainfall forecast is fused, water layer constraint in the growth period and conditional filtering rules in the fertilization safety period are fused, and a perception-decision-execution closed-loop control system is formed.

The invention relates to the technical field of soil function improvement, in particular to a rice field soil recarburization and capacity expansion method based on a charcoal-based organic fertilizer. The method comprises the following steps: crushing a biomass raw material, pyrolyzing and cooling to obtain biochar; immersing the biochar in a functional bacteria solution containing actinomycetes and bacillus for activating treatment to obtain modified biochar; mixing the modified biochar and decomposed straw, composting and fermenting to obtain organic fertilizers with different particle sizes; the large-particle-size organic fertilizer is applied to deep soil, the soil is covered with soil after straw is laid on the soil, and the small-particle-size organic fertilizer is applied to surface soil. Through layered application, the large-particle-size biochar repairs the deep soil structure and expands the carbon storage capacity, and meanwhile, the carbon stability is improved through carbon-nitrogen synergistic metabolism driven by functional bacteria, so that the carbon immobilization efficiency is improved, meanwhile, soil recarburization and expansion are achieved, the contradiction between carbon sequestration and structure degradation and nutrient imbalance in a traditional recarburization and expansion technology is solved, and the soil recarburization and expansion efficiency is improved. Synergic improvement of a carbon sink function and agricultural productivity is promoted.

The invention relates to a three-dimensional instance segmentation method in a paddy field high-density shielding scene in combination with a multi-modal fusion network. The method comprises the steps of collecting a multi-view RGB image and three-dimensional point cloud data of a paddy field for preprocessing; inputting the multi-view RGB image in the multi-source data set into the image segmentation model to generate an image instance segmentation result; matching the image instance segmentation result with the three-dimensional point cloud data in the multi-source data set; and inputting the point cloud data containing the multi-view RGB image information into the improved three-dimensional instance segmentation network model, and outputting a three-dimensional instance segmentation result. According to the method, accurate segmentation in a high-density shielding scene is realized, and the segmentation accuracy is remarkably improved; effective fusion of multi-modal data is realized, the robustness and precision of instance segmentation are effectively improved, and the method is especially suitable for a fine-grained segmentation task in a dense vegetation scene. The calculation complexity is reduced, the processing efficiency is improved, and the long-distance dependency relationship in the point cloud can be captured while the efficient calculation performance is kept.

The invention discloses a method and a system for predicting diseases and insect pests of saline-alkaline tolerant rice based on artificial intelligence. The method comprises the steps of collecting rice data, optimizing the rice data, establishing a prediction model for the diseases and insect pests of the saline-alkaline tolerant rice and predicting the diseases and insect pests of the saline-alkaline tolerant rice. The invention belongs to the technical field of agricultural information, and particularly relates to a method and a system for predicting diseases and insect pests of saline-alkali tolerant rice based on artificial intelligence. According to the scheme, rhombic plot segmentation is dynamically adjusted according to the irregular terrain and the saline-alkali accumulation area boundary of the saline-alkali tolerant rice field, and the collaborative weight is calculated based on the saline-alkali stress degree and the disease and insect pest risk level; key region learning is enhanced in combination with a plot total score and a selection probability formula; a plot score, a neighborhood pest and disease density mean value and a neighborhood saline-alkaline gradient are introduced into a spatial branch, so that the capturing capability of spatial correlation is improved; on the basis of the dynamic adjustment tolerance threshold, the influence of the extreme value is limited through the loss upper limit coefficient, a pest and disease prediction error regulation and control function is constructed, and the pest and disease prediction accuracy is improved.

The invention discloses a rice field irrigation and drainage decision-making method and system based on human-in-loop reinforcement learning, and the method is realized based on reinforcement learning, and the method comprises the steps: collecting real-time environment parameters of a target rice field in a current decision-making period; constructing an environment state vector based on the real-time environment parameters; constructing an irrigation drainage decision model of a continuous action space based on a soft actor commentator algorithm, wherein the model outputs an action according to the environment state vector; when it is judged that the output action deviates from a reasonable strategy or has a potential risk, a human-in-loop collaborative intervention mechanism in which a human expert participates is introduced, the output action is corrected, and a human-in-loop reward signal is generated through expert intervention; an output action is executed, environment feedback and a multi-target weighted reward signal containing a human-in-loop reward signal are collected, loop iteration is performed based on a closed-loop control system, and the irrigation and drainage decision model is updated; and obtaining irrigation and drainage decision output of the current decision period according to the updated irrigation and drainage decision model.

The invention provides a method for preparing carbon quantum dot reinforced subcritical artificial humic acid and applying the carbon quantum dot reinforced subcritical artificial humic acid to a rice field, which comprises the following steps: collecting organic wastes, and drying, grinding and sieving the organic wastes to obtain pretreated raw materials; the preparation method comprises the following steps: mixing pretreated raw materials with water, reacting in a hydrothermal carbonization system at 200-280 DEG C for 1-3 hours, modifying through acid-base pretreatment, adjusting a reaction medium or an additive, and naturally cooling to obtain a natural substance subjected to hydrothermal carbonization; carrying out solid-liquid separation on the natural substance subjected to hydrothermal carbonization by using a 100-mesh coarse filter screen to obtain solid-phase hydrothermal carbon and liquid-phase artificial humic acid; carrying out purification treatment on the liquid-phase artificial humic acid for 48 hours by using a dialysis bag to obtain carbon quantum dots and characterizing the carbon quantum dots; the carbon quantum dot reinforced subcritical artificial humic acid is applied to a rice field, and is additionally applied in a fertilization period. Compared with a traditional composting technology, the scheme has the advantages that the carbon conversion rate is increased by 2-3 times, the soil fertility can be improved, and the rice yield is increased.

The invention provides a rice transplanting robot suitable for a small rice field and a control method thereof, and belongs to the technical field of agricultural machinery. Comprising a moving part and a rice transplanting assembly, the moving part comprises a machine body, a plurality of mechanical legs and driving assemblies corresponding to the mechanical legs, the mechanical legs are connected with the machine body through the corresponding driving assemblies, the rice transplanting assembly comprises a rice seedling storage box, a mechanical arm, a mechanical claw, an angle sensor and a finger pressure sensor, and the rice seedling storage box is arranged on the machine body; one end of the mechanical arm is rotationally connected with the machine body, the angle sensor is arranged on the mechanical arm, the mechanical claw is arranged at the other end of the mechanical arm, the finger pressure sensor is arranged on the mechanical claw, and the mechanical arm has multidirectional rotation freedom degrees. And the insertion depth of the seedlings is adjusted according to readings of the angle sensor and the finger pressure sensor. The technical problems that existing rice transplanting equipment is limited in use in small rice fields due to structural defects, and the survival rate of planted seedlings is low can be solved.

The invention discloses a method for extracting plant available cadmium in neutral-alkaline rice field soil, which comprises the following steps: naturally air-drying the soil, grinding and sieving to obtain a soil sample, weighing the soil sample, adding deionized water to enable the water content of the soil to be 35-110% of the maximum water-holding capacity of the soil, balancing at room temperature for 12-48 hours, adding a calcium chloride solution, oscillating and extracting at constant temperature, and filtering to obtain the plant available cadmium in the neutral-alkaline rice field soil. And filtering to obtain an extracting solution, measuring the concentration of cadmium in the extracting solution, and calculating the content of the plant available cadmium in the measured soil sample according to the measured mass of the soil sample and the measured concentration of cadmium in the extracting solution. The method is easy and convenient to operate and low in cost, the change characteristics of the plant effectiveness of cadmium in the neutral-alkaline rice field soil in a field water management mode can be well reflected, and the measured plant effective-state cadmium content in the neutral-alkaline rice field soil and the rice cadmium content have a better linear correlation relationship.

The invention discloses a method and a system for detecting metabolism kinetics of organic carbon and microbial carbon in deep soil, and relates to the technical field of soil science and environmental science. Comprising the following steps: acquiring the content and structural characteristics of organic carbon in the soil of the rice field, and determining the composition, function and dynamic response mechanism of microbial communities to environmental change; setting a control group and a treatment group, and obtaining soil organic carbon reserve change rates, CO2 emissions and influence factors in the control group and the treatment group at different time points; and constructing a data analysis model according to the soil organic carbon reserve change rate, CO2 emission and influence factors in the control group and the treatment group at different time points, and generating a comprehensive report of deep soil organic carbon and microbial carbon metabolism kinetics. According to the invention, a scientific basis is provided for optimizing agricultural management and coping with climate change, the positive influence of biomass charcoal addition on soil health is disclosed, and the development of sustainable agriculture is promoted.

The invention discloses a system for accurately removing abnormal plants in rice breeding based on intelligent image recognition, and relates to the field of agricultural intelligent equipment.The system comprises an image acquisition module, an abnormal plant detection module and an impurity removal control module, the preprocessing module is used for preprocessing rice field plant images and transmitting the preprocessed rice field plant images into the abnormal plant detection module, the abnormal plant detection module is used for analyzing and processing the rice field plant images and identifying to-be-processed abnormal plants, and the impurity removal control module is used for receiving the positions of the to-be-processed abnormal plants and controlling the impurity removal structure to remove impurities.

The invention discloses a rice field aquaculture intelligent management system based on the Internet of Things, and relates to the technical field of intelligent management. During operation of the system, various environmental parameters in a rice field are monitored and collected in real time by deploying an Internet of Things sensor network, and water quality changes and other factors of a farmland environment are monitored; water quality adjusting equipment is controlled to adjust a rice field water body, automatic adjustment is achieved through a feedback control mechanism, water quality changes are responded in real time, a crop growth monitoring sensor and a camera are installed, the growth state of rice is monitored in real time, visual feedback of the growth state is provided through the image recognition technology and the data analysis technology, and the rice field water quality monitoring system is established. The automatic irrigation system is used for controlling the water volume of the paddy field, automatically adjusting the irrigation frequency and the water volume, calculating the comprehensive benefit index TGI of the paddy field by using the data mining and statistical analysis technology, monitoring the environmental parameters of the paddy field and the growth state of crops in real time, identifying abnormal conditions and timely giving out early warning.