18 results about "Soil map" patented technology

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 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.

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.

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 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 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.