102 results about "Soil conductivity" patented technology

The invention relates to the technical field of intelligent control, and discloses a water-saving fertilization variable control method suitable for sandy soil, and the method comprises the steps: obtaining collection data based on a sensor; and establishing and updating an infiltration-storage-leaching loss prediction model of the sandy soil according to the collected data, performing state estimation on soil moisture and nutrient states, and calculating root layer moisture deviation, fertilizer concentration deviation and deep seepage risk. And executing model predictive control in the rolling time domain to obtain a control result. And executing water-saving fertilization operation according to the control result. In the execution process, the soil volume moisture content, the soil water potential, the soil conductivity and shallow layer leakage observation data are fused, the prediction model is corrected, the control result is dynamically adjusted according to the correction result, and when the environment wind speed exceeds a set threshold value, the spray irrigation parameters are corrected. The fine level of control is improved, deep layer leakage can be inhibited under the sandy soil condition, water and fertilizer balance of a root layer is guaranteed, and external disturbance is coped with.

The invention provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

The invention discloses a DNAPLs pollutant diffusion migration path depicting method and system, and relates to the technical field of pollution monitoring, and the method comprises the steps: obtaining the soil conductivity of a target region through an improved TDR probe, obtaining the time sequence characteristics and spatial characteristics of a soil conductivity data set, obtaining the fusion spatial-temporal characteristics, calculating the spatial-temporal similarity of historical migration paths, and obtaining the DNAPLs pollutant diffusion migration path depicting result. Screening prior knowledge; constructing a space-time prediction model by using a graph neural network and priori knowledge, extracting deep space-time features from a space adjacent graph and a time-dependent heterogeneous graph corresponding to the target region, and obtaining a conductivity prediction value of a future time step; the diffusion direction of the pollutants is deduced by combining the relation between the resistivity and the DNAPLs concentration, migration frontal surface diagrams at different time points are drawn, and dynamic path visualization is achieved. According to the method, the improved TDR (time domain reflectometry) technology and the deep learning model are combined, so that high-precision monitoring and prediction of the dynamic migration process of the pollutants are realized.

The invention relates to the technical field of environment monitoring, in particular to an intelligent digital rural environment monitoring system and method based on big data, and the system comprises a layered water potential collection module, a gradient reverse correction module, a path inversion modeling module and a multi-dimensional diffusion modeling module, and the layered water potential collection module calculates and generates rural soil environment monitoring data; the rural soil environment monitoring data comprises layered soil water potential distribution, soil water content gradient and a soil conductivity characteristic value, and the soil water potential abnormal flow data comprises a vertical water potential difference value, an abnormal water flow direction vector and a water flow intensity grade; the moisture permeation path distribution diagram comprises a permeation path distribution structure, a soil water potential accumulation interval and a water flow propagation direction identifier, and the diffusion boundary space model comprises a pollutant diffusion boundary range, a moisture transverse migration region and multi-dimensional diffusion rate distribution. According to the scheme, the abnormal distribution range, the duration time and the development trend are quantitatively described, and the research and judgment accuracy of the abnormal penetration and diffusion process is improved.

The invention provides a photovoltaic system and power distribution network state cooperative monitoring method based on the Internet of Things, and the method comprises the steps: deploying a multi-point collection device in a key region of a photovoltaic power station through a sensor network, and obtaining the soil humidity change and soil conductivity at regular time; denoising and standardizing the acquired soil humidity change and soil conductivity data to generate a standardized environmental parameter matrix; according to the dynamic change trend of grounding grid corrosion, identifying the mapping relation between the soil environment parameters and the grounding resistance value, and combining the real-time environment parameters and the corrosion trend to predict the resistance change; and comparing the predicted resistance change with the current grounding resistance value in real time, if the current grounding resistance value is detected to exceed a safety threshold range, triggering an abnormal early warning signal, and determining a potential area with high electric shock risk according to the resistance deviation degree and the corrosion rate.

The invention relates to an unbalanced data-oriented electric power system lightning disaster risk detection method, which comprises the following steps of: S1, gridding a target detection land parcel to obtain a plurality of grid units; S2, based on latitude and longitude coordinates of each grid unit, obtaining line parameters and environment characteristics of each grid unit in combination with a line database and a geographic information base, wherein the line parameters comprise tower height, span, grounding resistance, loop number, lightning arrester number and transformer capacity, and the environment characteristics comprise soil conductivity, building height and land utilization type; and S3, inputting the line parameters and the environment characteristics of each grid unit into a trained first detection model to obtain the thunder and lightning probability and the maximum peak current of each grid unit. Compared with the prior art, the multi-source data such as the tower height, the span, the grounding resistance and the soil conductivity are comprehensively considered, and the risk assessment result has higher regional adaptability and accuracy.

A power transmission tower grounding resistance non-contact measurement method based on the electromagnetic coupling principle comprises the following steps that transmitting and receiving electromagnetic coupling coils are arranged around an iron tower grounding body, and geometric calibration and spatial positioning of a measurement area are completed by combining grounding grid structure parameters and soil conduction characteristics; injecting a high-frequency alternating-current excitation signal into the transmitting coil, and synchronously acquiring the amplitude and phase response of the induced voltage at a receiving coil end; carrying out filtering processing on the induction signal by adopting a time-frequency analysis and phase decoupling algorithm, and solving a function relationship between electromagnetic response and grounding impedance by combining a coupling equivalent model; based on experimental calibration data and scene parameters, a nonlinear mapping model of induction response and grounding resistance is constructed, and real-time inversion calculation is carried out by combining a solving result obtained in the third step. According to the invention, the real-time accurate measurement of the grounding resistance under the conditions of no power failure and no wire breakage is realized, and the safety, the operation convenience and the anti-interference capability in a complex environment in the measurement process are obviously improved.

The invention discloses a salinization remote sensing inversion method fusing crop information and data preprocessing. A land utilization data set is subjected to splicing, cutting and other processing to obtain a high-confidence-coefficient cultivated land range; point distribution sampling is carried out according to a regular grid method, and soil conductivity is measured; preprocessing the remote sensing image; dividing crop growth stages and constructing a multi-temporal spectral feature set; constructing an NDVI time sequence data set and extracting phenological characteristics; extracting planting structure information based on the multi-temporal spectrum and phenological characteristics; a planting structure is converted into crop type data through one-hot coding, and the crop type data is fused with multi-temporal spectrum and phenological characteristics to construct a crop information data set; the method comprises the following steps: constructing a salinity inversion model through data preprocessing and combining with ground actually measured salinity data, and verifying the salinity inversion model; evaluating the cross-year generalization ability of the model; and finally, the inversion result is visualized to generate a salinization map. According to the invention, the system integrates the multi-dimensional information of crops and optimizes the data preprocessing process, and significantly improves the salinization monitoring precision and the cross-space-time adaptability.

The invention relates to a composite microbial inoculant for soil improvement, the composite microbial inoculant for soil improvement is composed of PGPR (Pseudomonas tianjinensis) and AMF (Amplified Macrophage Fungi), the PGPR is Pseudomonas tianjinensis and Pseudomonas oryzae, and the AMF is rhizophagus clarus. The composite microbial inoculant for soil improvement is characterized in that the PGPR is Pseudomonas tianjinensis and Pseudomonas oryzae, and the AMF is Pseudomonas oryzae, the PGPR is Pseudomonas tianjinensis and Pseudomonas oryzae. According to the invention, the Pseudomonas tianjinensis, Pseudomonas oryzae and AMF are compounded, so that a synergistic effect is exerted, an obvious growth promoting effect on the ryegrass in the saline-alkali soil is achieved, the content of photosynthetic pigments in the ryegrass is increased, the contents of soluble sugar, soluble protein, proline and malondialdehyde in the ryegrass are further increased, and the CAT enzyme activity is improved. In addition, the complex microbial inoculant is combined with ryegrass planting, a plant-microorganism synergistic system is formed, salinized soil is effectively improved, the conductivity and pH of the soil are reduced, the content of nitrogen, phosphorus and potassium in the soil and the content of rapidly available phosphorus and rapidly available potassium are increased, and meanwhile the activity of urease and phosphatase is improved.

Methods, systems, and computer program products for implementing a soil analysis process. A set of soil data may be received at a device from one or more soil sensors. The one or more soil sensors may be coupled to a portion of an agricultural machine configured to be in contact with soil. The device may be configured to analyze soil when a portion of the one or more soil sensors is in contact with the soil. Spectral data and soil conductivity and / or capacitance data may be obtained based on the collected set of soil data. Analyzed soil data may be determined based on the spectral data and soil conductivity and / or capacitance data. The analyzed soil data may be provided to a user device configured to display a visual representation of at least part of the analyzed soil data.

The invention discloses an irrigation water and fertilizer fine adjustment system and method, and relates to the technical field of agricultural irrigation adjustment, and the method comprises the steps of information collection and storage, visual feature recognition and growth stage matching, irrigation area division, water application amount and fertilization amount calculation, irrigation instruction generation and irrigation operation execution. According to the invention, crop information is collected and visual feature identification is carried out to determine a growth stage, a crop growth stage spatial distribution map is generated, and irrigation area division is carried out according to the map; water and fertility calculation is carried out according to the soil moisture content and the soil conductivity of the soil in the irrigation area to obtain the water application amount and the fertilizer application amount, finally, an irrigation instruction is generated according to the water application amount and the fertilizer application amount, irrigation operation is executed, accurate water and fertilizer supply for crops in different growth stages is achieved, and the irrigation water and fertilizer adjustment fineness is improved.

The embodiment of the invention discloses a soil nutrient classification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring soil attribute information of a target area within a preset duration; the soil attribute information comprises soil conductivity, soil temperature and spectral reflectivity; performing data preprocessing on the soil attribute information to obtain preprocessed multi-channel time sequence data; performing feature extraction and fusion on the multi-channel time sequence data based on a target feature extraction model to obtain hidden state features; the target feature extraction model is obtained by performing semi-supervised mutual learning on a preset feature extraction model by using different enhanced samples obtained by performing different data enhancement processing on unlabeled samples; and determining a soil nutrient classification result of the target region based on the target classification model and the hidden state features. Through the technical scheme of the embodiment of the invention, the soil nutrients can be accurately and conveniently analyzed, and efficient soil nutrient classification in a few-sample environment is realized.

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 tropical agricultural planting, in particular to a method for planting pineapple-high-canopy crops on a steep-slope terrain in a low-latitude region. The planting method comprises the following steps: (1) transforming a terrain and interplanting, namely transforming a contour line terrace along the gradient direction, constructing a stepped planting belt, planting high-canopy crops in terrace ridges, and planting pineapples in the terrace surface; (2) complete-cycle photo-thermal management: the canopies of the high-canopy crops intercept direct light, and the pineapples utilize transmission light or diffused light; (3) water and fertilizer collaborative management: dividing a contour line terrace into multiple stages of irrigation areas according to the elevation difference, configuring an independent drip irrigation pipe network for each stage of irrigation area, monitoring the soil conductivity and the leaf surface transpiration rate in real time, and dynamically adjusting the fertilization amount; slow-release compound fertilizer is applied to high-canopy crops, and the application frequency is 4 times per year; the macroelement water-soluble fertilizer is applied to the pineapples by adopting pulse type drip irrigation, so that the fertilizing period of the pineapples and the fertilizing period of the high-canopy crops are staggered.

This invention discloses a method for soil salinity inversion based on the fusion of remote sensing features at multiple time scales, comprising the following steps: A1 field sampling and soil conductivity measurement; A2 remote sensing data preprocessing and spectral index calculation; A3 environmental covariate acquisition and preprocessing; A4 determination of the optimal time scale feature combination; A5 feature variable screening and optimization; A6 soil salinity inversion model construction; fusing remote sensing features at different time scales, and constructing a soil salinity remote sensing inversion model based on the optimal feature combination using a random forest algorithm. By systematically integrating the instantaneous response features of single-period images with the features of seasonal composite images, the information of remote sensing data at different time scales is fully exploited, solving the problems of single time scale of remote sensing features, insufficient information utilization, and difficulty in coordinating multi-scale features in traditional modeling processes. This improves the model's ability to characterize the spatial distribution of soil salinity and its prediction accuracy, providing more scientific and reliable technical support for remote sensing monitoring of salinized areas.

The invention discloses a soil quality monitoring system and method for citrus planting, and belongs to the technical field of soil quality monitoring, and the system comprises a soil salinity accumulation judgment module, a soil salinity accumulation adjustment module and a citrus absorption efficiency analysis module. By solving the problem of salt accumulation caused by continuous increase of soil conductivity and adjusting the problem of unbalanced citrus absorption efficiency caused by fluctuation of pH values of different plots, the quality and yield of the planted citrus are guaranteed, more efficient resource utilization is achieved, soil health is maintained, and the absorption efficiency of citrus root systems is improved.

The present invention provides a method and system for constructing a rice growth model based on salt stress, relating to the technical field of growth model construction. The method first sets up monitoring points in a planting sample area and obtains soil conductivity data at different depths using multi-depth soil conductivity sensors and the Kriging interpolation method. Root density, multispectral remote sensing imagery, and leaf area index are collected to calculate the canopy salt stress index. The sodium adsorption ratio is determined by measuring relevant ion concentrations, and a salt stress coefficient is established by combining soil conductivity and irrigation volume. The canopy and soil stress indicators are integrated to generate a comprehensive stress index, and a salt feedback term is introduced to characterize crop compensation capacity. Finally, a hybrid machine learning model is constructed using a convolutional neural network and a long-short-term memory network to achieve dynamic prediction of rice growth. The present invention achieves multidimensional salt stress quantification in the soil-crop system, providing decision support for salinization control in precision agriculture.

The present invention relates to the field of permafrost monitoring technology, and in particular to a thermodynamic tracking method and system for phase change of water storage in permafrost areas, comprising: obtaining spatiotemporal distribution data and temperature data of soil conductivity in a study area; correcting the temperature data based on the spatiotemporal distribution data of soil conductivity; constructing a topological mapping framework to calculate ice-water phase change latent heat data; constructing a nonlinear dynamic model to track the latent heat evolution process; calculating the moisture content and water storage of the active layer; deducing the freeze-thaw state of the active layer; and providing early warning of possible environmental disasters. Through innovative methods such as multi-source data fusion, topological dynamics modeling, and multi-scale spatiotemporal coupling, the temperature monitoring accuracy is improved by 30% to 50%, the moisture content derivation accuracy is improved by 40% to 60%, the water storage prediction accuracy is improved by 35% to 55%, and the early warning time is extended from the traditional 24 to 48 hours to 7 to 14 days, providing a powerful tool for environmental monitoring and disaster prevention in permafrost areas.

The application discloses a soil salinity inversion method and system based on multi-modal remote sensing data fusion, a device and a storage medium, and is applied to the technical field of soil quality monitoring.The method comprises the following steps: acquiring radar and optical remote sensing data of a soil salinity area to be inversed, and performing conductivity determination as an inversion index of soil salinity; acquiring multi-modal remote sensing features based on radar and optical remote sensing data fusion, combining the radar and optical remote sensing data, and based on a random forest algorithm, using a recursive feature elimination method to optimize the remote sensing feature combination, using cross validation to evaluate the model performance of the remote sensing feature combination, and obtaining an optimal remote sensing feature combination; based on the optimal remote sensing feature combination, a soil conductivity prediction model based on the random forest algorithm is constructed and trained, inputting the data to be measured into the conductivity prediction model after hyperparameter adjustment, and the soil salinity inversion is completed.The application realizes more accurate inversion of soil salinity under the interaction influence of complex environment and human factors.

A solution for measuring soil moisture status and climate parameters in real time, and for providing users (irrigators, supervisors, researchers, etc.) with a dashboard to support decision-making regarding crop irrigation. It combines three complementary technologies: soil conductivity measurement, an intelligent and connected embedded system, computing servers, and web and mobile applications. The solution is presented as a single unit, easy to install, simple to activate with the push of a button, and provides instant access to results via a web and mobile application. This solution offers users, even those without expertise in new technologies (ICT, IoT), a user-friendly way to monitor their crop soils, optimize irrigation timing, and save water.

The invention relates to the technical field of rice stress-resistant cultivation, and discloses a rice root system salt tolerance adjusting method based on exogenous spermidine, which comprises the following core steps: in the critical period of rice root system development, directionally applying a spermidine solution subjected to purity verification to a rice root system area, by accurately regulating and controlling the application concentration, frequency, duration and application environment conditions of spermidine, the osmotic regulation capability and the anti-oxidation system function of a rice root system in a high-salt environment with the soil conductivity larger than or equal to 2 dS / m are improved, the salt tolerance of the root system is remarkably improved, and then growth and development of rice are guaranteed. The spermidine is a straight-chain polyamine compound with good biocompatibility, the chemical purity is not lower than 98%, no heavy metal is left, and the contents of lead, cadmium and mercury are all smaller than or equal to 0.1 mg / kg. According to the invention, precise targeted regulation and control of the salt tolerance of the rice root system are realized, and the regulation effect is stable and remarkable.

This invention relates to the field of environmental monitoring technology, specifically to a smart digital rural environmental monitoring system and method based on big data. The system includes a stratified water potential acquisition module, a gradient inversion correction module, a path inversion modeling module, and a multidimensional diffusion modeling module. The stratified water potential acquisition module calculates and generates rural soil environmental monitoring data. This data includes stratified soil water potential distribution, soil moisture content gradient, and soil electrical conductivity characteristics. Abnormal soil water flow data includes vertical water potential difference, abnormal flow direction vector, and flow intensity level. The water infiltration path distribution map includes the infiltration path distribution structure, soil water potential accumulation intervals, and flow propagation direction indicators. The diffusion boundary space model includes the pollutant diffusion boundary range, water lateral migration area, and multidimensional diffusion rate distribution. This solution quantifies the abnormal distribution range, duration, and development trend, improving the accuracy of judging abnormal infiltration and diffusion processes.

This application discloses a high-precision quantification method, equipment, and system for farmland salinity patches integrating multi-source near-ground sensing, relating to the field of soil salinization. The developed equipment utilizes a vehicle towing an electromagnetically transparent platform carrying sensors via a non-metallic flexible traction component. Measurement software synchronously records centimeter-level spatial position, vehicle speed, sensor attitude, and apparent electrical conductivity of soil at different depths at a unified timestamp while traveling along a planned strip path. Simultaneously, it cleans up uncertain readings, abrupt local anomalies, and data from abnormal instrument attitudes. Subsequently, the apparent electrical conductivity data at different depths is rasterized, and the soil electrical conductivity estimates at different depths are inverted using an electromagnetic induction Q-3D algorithm. A three-dimensional distribution map of total soil salinity is then drawn, and salt accumulation areas are extracted using soil salinization grading standards, accurately defining and visualizing the geometric boundaries of farmland salinity patches. This application enables rapid and accurate monitoring of the three-dimensional spatial distribution of farmland salinity patches, contributing to the precise improvement of saline-alkali land.

The application discloses a kind of for forestry nursery stock transplanting after pipe maintenance regulation and control system and method, belong to forest seedling raising technical field.The application obtains total value of fertilization authority by soil moisture content and soil conductivity, and judges whether it is higher than preset authority threshold, if not, then store fertilization task temporarily, if yes, then carry out fertilization regulation and control, after fertilization, the change condition of soil moisture content is continuously monitored, whether to dynamically adjust basic fertilization amount and fertilizer solution concentration is determined, then respectively obtain seedling absorption effect index and root health index, then judge whether seedling absorption effect index belongs to absorption effect range, determine whether to adjust fertilizer solution concentration, finally judge whether root health index belongs to preset stage interval, determine whether to adjust current fertilization amount, improve the linkage control relevance of water management and fertilization management, solve the problem that the linkage control relevance of water management and fertilization management in prior art is lower.

The invention discloses a multi-source remote sensing data soil salt content inversion method and system based on a stacking model, and relates to the technical field of soil salt content prediction.The method comprises the following steps that the boundary of a target region is clearly researched, target monitoring points are arranged, soil conductivity data of sample sampling points are extracted, and then the soil salt content is calculated; ground spectral data, unmanned aerial vehicle images and satellite remote sensing spectral data are collected and subjected to spectral band frequency alignment, spectral reflectivity is extracted for each sample sampling point, a spectral characteristic index is constructed, salt content sensitive variables are screened out, the sensitive variables serve as input, the soil salt content serves as a label, and the salt content of the soil is calculated through a stacking model. According to the method, a soil salt content prediction model is constructed, salt content estimation of image pixel points of a research target region is realized, the model uncertainty during inversion of a single model is effectively reduced, and the prediction accuracy is improved.

The application discloses a dual-configuration fused soil conductivity measuring device and method, belongs to the technical field of intelligent agricultural equipment detection, and mainly comprises a test system, a rack and a detection device, wherein the test system and the detection device are both installed on the rack, the test system is installed above the rack, and the detection device is installed below the inside of the rack; according to different configurations, sensor electrodes are inserted into soil; soil conductivity values obtained by two configurations are transmitted to a JESTON nano through a controller; and a dual-configuration fused soil conductivity calculation model established based on a BP neural network is used to take the soil conductivity values obtained by the two configurations as model input parameters, to perform real-time calculation through the JESTON nano, and to display and output the optimized soil conductivity values on a touch liquid crystal display screen. The application can realize accurate measurement and storage of soil conductivity values at different depths in soil, is simple to operate, and is high in practicability.

The invention relates to the field of tea tree planting methods, in particular to an ecological niche complementary method for interplanting camphor trees with tea trees and application of the ecological niche complementary method. The method has the following effects: 1, interplanting of the cinnamomum camphora and the tea tree has an inhibition phenomenon on growth of physalospora piricola and colletotrichum gloeosporioides; 2, tea polyphenol of tea leaves can be reduced by interplanting the camphor trees and the tea trees; 3, the soil conductivity and the content of organic matters, alkali-hydrolyzable nitrogen and available phosphorus can be improved by intercropping cinnamomum camphora in the tea garden; and 4, tea amino acids can be increased by interplanting the camphor trees and the tea trees.

The application relates to a saline-alkali soil irrigation method, device, intelligent control system and saline-alkali soil treatment system, and is applied to the field of agricultural planting irrigation in a salinization area. The method comprises the following steps: periodically acquiring soil information of a target irrigation area and a current month, wherein the soil information comprises soil water content and soil conductivity; according to the current month, accurate irrigation quantity and soil information threshold values which are in line with the characteristics of the current month are determined from preset irrigation information, wherein the soil information threshold values comprise a soil water content threshold value and a soil conductivity threshold value; when it is determined that irrigation needs to be performed according to the soil information and the soil information threshold values, the irrigation system is controlled to automatically realize water source irrigation according to the irrigation quantity, irrigation efficiency is improved, and the saline-alkali soil treatment effect is improved.

Provided is a rice planter (1) for planting seedlings in a paddy field while traveling, comprising: a seedling planting device for planting seedlings, the seedling planting device including a float of which a float (110) bottom surface is brought into contact with soil; a fertilizing device for spraying fertilizer to the paddy field; and an electrode (310) for detecting the electrical conductivity of the soil in order to calculate the degree of fertility of the soil in the paddy field. The electrode (310) is attached to the float (110) and has a fertilizer clogging sensor for notifying the occurrence of clogging of the fertilizer in the fertilizing device. Thus, a rice planter having improved usability and simplified configuration is provided in which, in order to calculate the degree of fertility of the soil in the paddy field, the electrical conductivity of the soil between the electrode (310) and a second fertilizer clogging sensor electrode of the fertilizer clogging sensor is detected.

The utility model relates to soil detection technical field. The utility model discloses a kind of farmland soil conductivity real-time detection probe of anti-interference, including protective shell, the side wall of protective shell is slidably connected with cover plate, the inside of protective shell is slidably connected with conductivity detector, the bottom of conductivity detector is fixedly connected with detection probe, the detection probe is set by setting pollution cleaning component, pull rod is pulled upward, piston plate is driven along the inner wall of protective shell to slide upward, while piston plate drives conductivity detector and detection probe gradually retract to inside protective shell, effectively protect the integrity of detection probe, air in protective shell can be extruded to gas guide pipe by air guide component at this time, subsequently sprayed on the surface of detection probe, to improve the impact force and cleaning effect of airflow on the surface of detection probe impurity, more effectively remove soil particles, organic matter and other impurities attached on probe.