30 results about "Soil map" patented technology

The invention relates to the technical field of soil image processing, in particular to a vegetation detection method and system for soil environment restoration. According to the method, color features and position distribution features of pixel points are analyzed, a vegetation coverage factor of each superpixel block is obtained, and vegetation coverage blocks are screened out; according to the position distribution of the pixel points in each vegetation coverage block and the chromaticity fluctuation characteristics, the artificial cultivation characteristic degree of each vegetation coverage block is obtained; according to the centroid position and the vegetation coverage factor of each superpixel block, obtaining a vegetation coverage class cluster and a non-vegetation coverage class cluster; and in combination with the relative position between each vegetation coverage block in each non-vegetation coverage class cluster and each vegetation coverage block in the adjacent vegetation coverage class cluster, obtaining the soil remediation vegetation significance of each super-pixel block, and further obtaining the soil remediation weighted significance of each super-pixel block. According to the method, the vegetation characteristics in the soil environment restoration process are accurately analyzed, so that the accuracy of soil environment restoration assessment is improved.

The invention discloses a soil moisture content calculation method and device and a medium, and relates to the technical field of air-shrinkage cracked soil moisture content detection.The method comprises the steps that an air-shrinkage cracked soil image is obtained; the air shrinkage cracked soil image is input into a preset crack segmentation model, a crack area of the air shrinkage cracked soil image is obtained, and the preset crack segmentation model is a trained deep learning network; based on the crack region of the air-shrinkage cracked soil image, calculating the crack area density in the air-shrinkage cracked soil image; and based on a preset area density-water content correlation model, calculating the water content corresponding to the air shrinkage cracked soil image according to the crack area density. According to the method, the moisture content of the surface soil can be efficiently and accurately obtained on the premise that the original state of the soil is prevented from being damaged.

The invention relates to the technical field of intelligent irrigation, and discloses an intelligent irrigation system for a fruit tree planting greenhouse. The system comprises a multi-dimensional data acquisition module, a data fusion processing module, an irrigation decision module and an irrigation control execution module which are connected in sequence. The system obtains a root soil image, a leaf surface temperature distribution map and stem micro-deformation data, and performs multi-dimensional fusion calculation to generate a spatial water potential distribution map capable of representing the overall moisture condition of crops. And calculating specific regional water demand and preferential irrigation sites based on the map, and further driving an execution terminal to realize precise irrigation. According to the method, the physiological water demand signal of the crop can be responded, the spatial difference of water stress can be identified, the limitation that the traditional technology depends on single soil information is overcome, the crossing from environment monitoring to crop body sensing is realized, and the irrigation accuracy and the water resource utilization efficiency are improved.

The invention relates to the technical field of machine vision, and discloses a road defect evaluation system and method based on machine vision, and the method comprises the steps: collecting road surface pictures and road surface gradient data, analyzing road surface defects based on the road surface pictures and the road surface gradient data, collecting slope soil data and vegetation pictures, and evaluating the road surface defects. The method comprises the steps of analyzing soil loosening based on a soil picture, analyzing a vegetation disease risk based on a vegetation picture, analyzing a slope risk based on the soil loosening and the vegetation disease risk, collecting future weather data, predicting the influence of weather on a landslide based on the weather data and the slope risk, and collecting historical traffic flow data in the same period. The influence of traffic flow on landslide is predicted based on the traffic flow data and the slope risk, the safety of the road under the influence of the landslide is predicted by integrating the influence of weather on the landslide, the influence of the traffic flow on the landslide and pavement defects, and whether the road needs traffic restriction is analyzed based on the safety of the road, so that the accuracy of road risk prediction is improved, and the traffic safety is guaranteed.

The invention belongs to the technical field of soil prediction mapping. The invention provides a soil type map updating method fusing a soil voluntary and soil map data. According to the embodiment of the invention, by constructing the soil-environment text information model, the unstructured soil voluntary and the soil map vector polygon are uniformly converted into the sample point data set. Obtaining a partition clustering number based on a soil-environment text information model, and obtaining a class cluster corresponding to an environment factor combination in each parent material partition by adopting partition clustering analysis; based on the environment factor information of the environment factor combination class clusters and the quantified environment factor information in the soil-environment text information model, calculating the similarity between the environment factor combination and the text information framework, and further determining the soil type semantic information of each environment factor combination class cluster; and a representative point set corresponding to each soil type is screened out from each environment combination class cluster, and a quantitative soil-environment relationship is obtained by adopting a kernel density estimation method.

The invention discloses a farming agricultural machine operation quality detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a first farmland soil image before operation and a second farmland soil image after operation, recognizing the displacement deviation of a farmland calibration region through the comparison of the images, and determining the displacement deviation as an operation fluctuation deviation, and determining a to-be-detected area in the tillage area, outputting a soil attribute based on a soil density difference value between the to-be-detected area and the reference area, and performing comprehensive scoring by combining a fluctuation stability index output based on the operation fluctuation deviation and a land parcel analysis index output based on the soil attribute of the to-be-detected area so as to output an operation quality grade. According to the method, the two-dimensional evaluation system of the operation fluctuation stability and the cultivated soil form is established, so that the dynamic monitoring of the operation process and the quantitative analysis of the cultivation effect are realized, the one-sidedness of single-dimensional evaluation is overcome, and the output operation quality grade is more comprehensive, objective and accurate.

The present invention discloses a spinach sowing positioning method and system based on image data analysis, which relates to the technical field of spinach sowing positioning. The method comprises the following steps: obtaining a soil image, determining a screenshot size according to an actual area, and intercepting a plurality of image blocks for analysis; analyzing the soil color and texture characteristics of each block, and calculating an average value to construct a soil comprehensive characteristic expression model; adjusting model parameters to obtain a soil comprehensive characteristic expression model for comparison and a corresponding sowing interval; classifying the models, establishing a reference expression model set, analyzing and determining marker factor parameters of characteristic factors, and constructing a reference expression model library; analyzing a currently captured soil image, constructing a current soil comprehensive characteristic expression model, and determining a comparison model with the highest degree of fit by comparing it with models in the reference expression model library, thereby accurately determining the position of spinach sowing. The technical solution of the present invention improves the accuracy and efficiency of sowing, and provides strong support for intelligent agriculture.

To promote the automation of paddy rice cultivation.SOLUTION: An information processing device includes first acquisition means for acquiring a soil image obtained by imaging soil of a paddy field, second acquisition means for acquiring an inference result indicating a degree of partial drying of the soil of the paddy field by causing inference means to perform inference based on the soil image, and execution means for executing control processing related to management of the paddy field based on the inference result.SELECTED DRAWING: Figure 6

The invention provides a surface soil type identification method based on MCP-ShuffleNet V2, and the method comprises the following steps: S1, obtaining a sample soil image, and carrying out the preprocessing of the sample soil image, and then forming a training data set; s2, constructing an MCP-ShuffleNet V2 prediction model, and inputting the training data set into the MCP-ShuffleNet V2 prediction model to carry out training; and S3, obtaining a to-be-detected soil image, preprocessing the to-be-detected soil image, and inputting the preprocessed to-be-detected soil image into the trained MCP-ShuffleNet V2 prediction model to obtain a soil classification result of the to-be-detected soil image, so that interference of non-soil elements in the soil image can be effectively inhibited, a neural network is guided to focus on a key soil area, a continuous and derivable MELU activation function is provided, and the accuracy of the soil classification result is improved. And the gradient disappearance problem is effectively avoided, so that the final soil type identification precision is effectively ensured.

Present disclosure discloses determining soiling maps based on soiling loss of photovoltaic panels (PV). Receive sensor data associated with accumulation of plurality of particles on PV panels and environmental data. Sensor data is received for each time interval of a plurality of predefined time intervals. Sensor data includes particle data associated with plurality of particles, tilt angle data, and orientation data. Determine deposition rate data associated with the accumulation of plurality of particles on PV panel based on sensor data and the environmental data. Determine soiling loss data associated with PV panel based on deposition rate data. Determine correlation coefficient data based on deposition rate data and soiling loss data. Correlation coefficient data indicates one or more correlation coefficients between corresponding soiling loss and observational data. Generate one or more soiling maps associated with PV panel based on correlation coefficient data. Output one or more for PV panels.

The application provides a soil species identification method based on residual graph convolution, which comprises the following steps: mapping a sample soil image from an RGB color space to a Lab color space, removing a brightness component L in the Lab color space, and forming first input information by combining components of three color channels of the RGB color space of the sample soil image with a component a and a component b of the Lab color space; converting the sample soil image into a gray-scale image and forming second input information from the gray-scale image; constructing a soil species identification model, wherein the soil species identification model comprises a PH color channel correlation enhancement module, a multi-scale edge space self-adaptive enhancement module, an average pooling layer, a soil layer graph convolution module and a priori layer level guiding module; inputting first input information and second input information of a soil sample to be tested, soil genus-soil species layer level label word embedding features and a soil genus-soil species layer level relationship adjacency matrix into the soil species identification model which has been trained to obtain a soil species of the soil sample to be tested.

The invention is related to a system that can analyze the condition of the soil using image processing and artificial intelligence algorithms and displays the mineral values that trees take from the soil and its characterized by; at least one infrared camera (3) that takes soil images, database (2) where the collected data is recorded, a analysis application (5), which analyzes data collected from infrared cameras (3) and compares it with previous data stored in the database (2), at least one processor (1) containing the artificial intelligence algorithm (6) and an artificial intelligence algorithm (6) to report the soil condition with the help of the collected data.

This invention discloses a multi-parameter statistical matching rhizosphere soil microfluidic chip device and its construction method, belonging to the field of soil microfluidics and porous media visualization technology. The device includes a glass slide, a PDMS chip layer, a fluid inlet, an outlet, a branch-shaped flow distribution zone, a buffer zone, and a soil pore simulation zone, forming a heterogeneous pore network within the simulation zone. Based on real rhizosphere soil CT images, the method extracts pore diameter, pore throat width, porosity, tortuosity, and Euler number. A network skeleton is generated using a random seed lattice and polygon segmentation, combined with probability density sampling and iterative optimization, to match the chip to real soil in terms of pore / throat size distribution, porosity, tortuosity, and connectivity. Results show that the KS distances for pore diameter and pore throat width distributions are 0.0142 and 0.0010, respectively, and the porosity is approximately 0.15. This provides a visualization platform for studying gas migration and micro-seepage in rhizosphere soil.

A computer-implemented method is provided for facilitating agricultural operations in an agricultural field. In one example embodiment, the method includes identifying, based on spatial sampling of soil spectrum data for an agricultural field, ground sampling locations within the field to obtain physical soil samples representative of soil makeup for the field. The method also includes generating a soil model particular to the field by correlating soil properties for soil included in the soil samples obtained from the identified ground sampling locations to particular soil spectral bands included in the soil spectrum data for the field at the corresponding ground sampling locations. The method then further includes compiling, using the soil model, a soil map of the entire field visually illustrating particular seeds and / or populations of seeds to plant at different locations across the field and / or particular nutrient applications to apply at different locations across the field.

The invention relates to a method for generating a semantically segmented ground map of at least part of a ground in an environment, on which ground in particular a mobile device, in particular a cleaning robot, moves or is intended to move, comprising: providing (204) one or more environmental images (202), each of which is or has been captured by a camera; performing (210) a semantic segmentation for the one or each of the plurality of environmental images (202), each for at least a part of the environmental image that includes at least a part of the ground, in order to obtain one or more semantically segmented intermediate images (212); determining, for the one or each of the plurality of intermediate images, one or more ground image regions based on calibration parameters (222) of the respective camera and based on intersection points at which straight lines originating from the camera intersect a plane corresponding to the ground;Determining (240) the semantically segmented soil map (242) based on the soil image areas; and providing (250) the semantically segmented soil map.

The present application relates to the field of solid waste treatment, and discloses a comprehensive treatment method for rural residual garbage, comprising: arranging sampling points in the area covered by the residual garbage to obtain soil samples of different depths, collecting garbage samples in the area covered by the residual garbage to obtain garbage samples; detecting and analyzing the soil data and the garbage samples to obtain the soil pollution degree level; collecting the utilization status map, the topographic map and the soil map of the area covered by the residual garbage and superimposing them, and dividing the superimposed area into multiple sub-regions; extracting the feature information of each sub-region, grouping the multiple sub-regions into multiple sub-region groups according to the feature information, and obtaining the multiple sub-region groups; for each sub-region group, generating the corresponding treatment mode according to the soil pollution degree level, the soil characteristics and the topographic information. The residual garbage is comprehensively treated according to the corresponding treatment mode. Thus, the garbage plot treatment with pertinence is realized.

The application discloses a luminance-controllable soil image enhancement method based on Sigmoid curve fitting, which comprises the following steps: S1, using Sigmoid to fit the cumulative probability density of a soil image Y component to obtain a Sigmoid fitting curve of the cumulative probability density; S2, performing luminance migration processing on the Sigmoid fitting curve to obtain a luminance-migrated Sigmoid fitting curve cdf tar ; S3, performing luminance sorting and migration processing on the soil image Y component according to the fitting curve cdf tar ; S4, correcting soil image U and V components to obtain corrected color components; S5, fusing the processed Y component and the corrected color components and converting back to an RGB color space to obtain an enhanced RGB image. The application can controllably enhance the luminance of a soil image, and has high enhancement precision and small distortion.

The present application relates to the technical field of soil image processing, and particularly relates to a vegetation detection method and system for soil environment remediation. The present application analyzes color features and position distribution features of pixel points, obtains a vegetation coverage factor of each superpixel block, and screens out vegetation coverage blocks; according to position distribution and chroma fluctuation features of pixel points in each vegetation coverage block, an artificial cultivation feature degree of each vegetation coverage block is obtained; according to a centroid position and the vegetation coverage factor of each superpixel block, a vegetation coverage cluster and a non-vegetation coverage cluster are obtained; in combination with relative positions between each non-vegetation coverage cluster and each vegetation coverage block in an adjacent vegetation coverage cluster, a soil remediation vegetation salience of each superpixel block is obtained, and then a soil remediation weighted salience of each superpixel block is obtained. The present application accurately analyzes vegetation features in a soil environment remediation process, and improves the accuracy of soil environment remediation evaluation.

Present disclosure discloses determining soiling maps based on soiling loss of photovoltaic panels (PV). Receive sensor data associated with accumulation of plurality of particles on PV panels and environmental data. Sensor data is received for each time interval of a plurality of predefined time intervals. Sensor data includes particle data associated with plurality of particles, tilt angle data, and orientation data. Determine deposition rate data associated with the accumulation of plurality of particles on PV panel based on sensor data and the environmental data. Determine soiling loss data associated with PV panel based on deposition rate data. Determine correlation coefficient data based on deposition rate data and soiling loss data. Correlation coefficient data indicates one or more correlation coefficients between corresponding soiling loss and observational data. Generate one or more soiling maps associated with PV panel based on correlation coefficient data. Output one or more soiling maps for PV panels.

The invention discloses a soil crushing quality evaluation method of a rotary cultivator, and belongs to the technical field of image processing. The method comprises the following steps: firstly, acquiring a soil image after cultivation of a rotary cultivator, dividing the soil image into M * L image blocks, and extracting profile diagrams of fine, medium and coarse granularities from each image block; according to contour point distribution in each granularity contour map, respectively constructing local uniform value matrixes, and then constructing a global uniform value matrix according to the number of contour points; and finally, processing the constructed local uniform value matrix and the global uniform value matrix by adopting a crushed soil quality multi-granularity convolution evaluation model so as to obtain a crushed soil quality score, thereby effectively solving the problem of low evaluation precision in the prior art, and realizing accurate evaluation of the crushed soil quality of the rotary cultivator.

The application discloses a soil image enhancement method based on brightness migration and local information fusion, and comprises the following steps: S1, fitting a V component of a soil image by using an asymmetric generalized Gaussian; S2, performing brightness migration on the fitted asymmetric generalized Gaussian curve to obtain a migration result of the V component of the soil image after brightness migration; S3, performing local brightness enhancement processing on the V component of the soil image to obtain an enhancement result of the V component of the soil image after local enhancement; S4, fusing the migration result of the V component of the soil image and the enhancement result of the V component of the soil image to obtain a fused V component of the soil image; and S5, processing the fused V component of the soil image according to a color ratio invariability principle to obtain an RGB image after color recovery and enhancement. The application can realize high-precision brightness migration, has high enhancement precision for controllable brightness of the soil image, and the enhanced image is close to a target image collected in a real environment.

The application discloses a soil crushing quality evaluation method of a rotary cultivator and belongs to the technical field of image processing. The application first collects soil images after the rotary cultivator is cultivated, divides the soil images into M*L image blocks, and extracts contour maps of three granularities, namely fine granularity, medium granularity and coarse granularity, from each image block; then, according to the distribution of contour points in the contour maps of different granularities, local uniform value matrices are respectively constructed, and a global uniform value matrix is constructed according to the number of the contour points; finally, the constructed local uniform value matrices and the global uniform value matrix are processed by using a soil crushing quality multi-granularity convolution evaluation model, so that a soil crushing quality score is obtained, the problem of low evaluation accuracy existing in the prior art is effectively solved, and accurate evaluation of the soil crushing quality of the rotary cultivator is realized.

The invention provides a variable-force compacting method based on image recognition, relates to the technical field of intelligent agriculture, is applied to a variable-force compacting device, obtains a soil image through a camera, recognizes the soil image based on a deep learning model, obtains a soil type, and adjusts the compacting force of the variable-force compacting device according to the soil type. The problem that in an existing compacting technology, the compacting force cannot be dynamically adjusted according to the actual soil condition is solved, and the soil compacting device is suitable for compacting operation on the soil.

The application relates to the field of robot terrain perception, and proposes a light condition adaptive soil moisture estimation method based on multi-modal data fusion. First, an optimal exposure time calculation method is proposed to construct a multi-modal soil moisture dataset. Second, the SS-GAN algorithm is used to remove soil image shadows, and the channel attention mechanism ECA and the standard convolution layer are improved in combination with environmental information, ResNet-50 is embedded as the network backbone, and a soil moisture estimation submodel suitable for different light conditions is designed. Then, the improved loss function is used to train each submodel to adapt to specific light conditions. Finally, a submodel fusion and switching method is proposed to generate a soil moisture estimation map through gray value mapping. Through the improved neural network model for multi-modal data fusion and the design of the fusion and switching algorithm of the submodel, the soil moisture estimation precision of the robot under complex light conditions can be improved, and the terrain perception capability can be improved.

The invention discloses a soil organic matter detection device and method based on a light-heat synergistic principle, and belongs to the technical field of rapid detection and intelligent sensing of soil physical and chemical parameters. The method comprises the steps that ground soil is prepared and placed in a groove position, mass data of the soil placed in each time is collected based on a weight sensor, and a rotating disc is driven to rotate based on a stepping motor; high-resolution micro-distance soil images, visible near-infrared reflectivity curves and infrared reflectivity curves of corresponding wave bands in all the grooves are collected in the first measuring darkroom, the rotating disc is driven to rotate based on the stepping motor, temperature change curves in all the grooves are collected in the second measuring darkroom, and the soil apparent specific heat capacity is calculated based on the photothermal effect; the spectrum and thermal characteristics are fused, and the soil organic matter content is inverted through a multi-source information model. Non-contact and rapid in-situ detection of soil organic matters is realized, moisture interference is effectively separated through light-heat-spectrum cooperation, the precision is remarkably improved, and the device is low in cost and easy to popularize.

The application discloses a kind of soil moisture content remote sensing monitoring methods, comprising: unmanned aerial vehicle gathers the remote sensing image of monitoring area;Base station device receives remote sensing image, analysis determines the bare proportion of soil in remote sensing image;Bare proportion is compared with the set proportion threshold value, determine whether bare proportion is less than proportion threshold value;If bare proportion is less than proportion threshold value, unmanned vehicle is started to go to monitoring area;Unmanned vehicle gathers the soil image in monitoring area;Base station device synthesizes remote sensing image and soil image, forms synthesis image;Synthesis image is analyzed, and the soil moisture content of monitoring area is determined.The method of the application is not affected by ground vegetation coverage, in any case, the information of soil can be obtained, and then the more accurate soil moisture content is determined.

The present invention discloses a soil remediation monitoring method and system. The method includes: inputting historical soil images and real-time soil images into a pre-built image recognition model; determining, based on the image recognition model, whether at least one pixel in the real-time soil image is identical to a pixel at a corresponding position in the historical soil image; if so, marking the at least one pixel in the real-time soil image as an abnormal pixel; clustering at least two adjacent abnormal pixels to obtain at least one abnormal region; and correcting at least one risk region in the real-time soil image based on a preset correction strategy to obtain a real-time contaminated region map at the current acquisition time. This method reduces the amount of soil data collected while improving the accuracy of determining the scope of soil remediation.

The present invention provides a rotary tiller soil crushing quality detection system and method based on image technology. The method includes obtaining soil images during the working process of the rotary tiller and preprocessing them. The soil images are multi-channel color images; extracting soil feature regions from the preprocessed soil images through image segmentation technology; constructing a regularization model for soil quality evaluation based on the texture information of the soil feature regions, and accordingly constructing a minimization problem for soil quality evaluation; calculating parameters for soil quality evaluation based on the minimization problem, including indicators such as soil particle size distribution and soil humidity; and evaluating the soil crushing quality of the rotary tiller according to the calculated soil quality evaluation parameters to obtain the final soil crushing quality evaluation result. The present invention can achieve rapid and accurate evaluation of key indicators such as soil particle size distribution and humidity, and improve the efficiency of agricultural mechanized operations and the quality of soil tillage.

The application discloses a soil map branch shape map spot automatic distinguishing method and device based on morphological characteristics, and the method comprises the following steps: generating a spot set SP based on two-dimensional soil layer data, and generating a GeoDEM based on DEM data; screening soil map spots with a height difference greater than a preset threshold from the SP based on the GeoDEM and storing the soil map spots in a set SP1; screening soil map spots with a geometric circular rate less than a preset threshold and storing the soil map spots in SP2; screening soil map plates with a branch number greater than a preset threshold and storing the soil map plates in a set SP3; and outputting each soil map spot in the set SP3 to generate a soil branch shape map spot file. The application realizes automatic distinguishing of a large number of soil map branch shape map spots, and the accuracy and running efficiency of the distinguishing result are relatively high.