1382 results about "Cultivated land" patented technology

The present invention relates to the technical field of land engineering, and relates to a water and soil resource comprehensive improvement method for loess hilly and gully region channels. The method comprises: (1) soil layer general survey: performing soil layer thickness survey and soil nutrition content measurement; (2) soil body reconstruction: on the basis of soil layer general survey data, selecting a soil body construction mode, and performing soil body profile reconstruction, soil body nutrition reconstruction and field parcel arrangement; and (3) constructing a water resource regulation and control system for the loess hilly and gully region channels. According to the present invention, investigation and survey are performed firstly, then treatment is performed, water and soil are treated simultaneously, and steps are coordinated, so that ecological treatment of the loess hilly and gully region channels is finally completed, and comprehensive improvement of water and soil resources is achieved. The present invention ensures mechanical cultivation of the region after the improvement, effectively increases the area of the irrigable land and the area of the non-irrigated land, effectively improves the farmland quality and the grain yield, and effectively reduces the soil erosion amount of the channels.

The invention provides a soil fertility quality analysis method and system based on corn planting cultivated land, and relates to the technical field of soil quality analysis and evaluation.The soil fertility quality analysis method comprises the steps that a visible-near infrared spectrum image of a soil sample is collected, reflectivity data of a key wave band is extracted from the spectrum image, and a soil health index and a nutrient balance index are calculated; calculating a soil fertility quality comprehensive score for comprehensively evaluating the soil fertility according to the soil health index, the nutrient balance index and the soil moisture content; comparing the comprehensive score of the soil fertility quality with a preset threshold value, and evaluating the soil fertility; a machine learning model is introduced, historical soil data is trained and predicted, a quality analysis model is constructed based on a random forest algorithm, the trained quality analysis model is put into use, and new data is continuously learned to dynamically adjust and update, so that the soil detection process is simplified, and the soil fertility change can be monitored in real time. And agricultural managers are helped to make scientific decisions.

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

The invention discloses a farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion, and relates to the technical field of agricultural information, and the method comprises the steps: collecting physical and chemical parameters of soil temperature, humidity, pH value, organic matter content, nitrogen phosphorus and potassium concentration and the like in real time through a multi-source sensor network; removing abnormal values by using a data preprocessing algorithm, performing standardization processing, generating a standardized soil parameter set for subsequent climate and management factor correlation analysis, automatically generating a risk early warning signal, pushing the risk early warning signal to an agricultural management decision platform, and completing full-process analysis from data acquisition to decision support; the farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion can provide a scientific basis for agricultural management decision, effectively guide farmland management practice, and improve soil quality and agricultural production efficiency.

The invention relates to the technical field of cultivated land resource management, and discloses a data optimization and intelligent decision support system in cultivated land resource early warning, which comprises a cultivated land data acquisition module, a soil index analysis module, a degradation identification module, a land force deduction module, a threshold adaptation module and a decision generation module. The cultivated land data acquisition module acquires cultivated land multi-dimensional attribute data in real time and partitions the data; the soil index analysis module divides soil fertility units and fuses soil indexes to generate resource state vectors; the degradation identification module extracts a core degradation factor, establishes a risk layering model and obtains a data correction coefficient; the land force deduction module identifies the association rule and the dynamic correction factor and calculates the land force attenuation gradient; the threshold adaptation module derives an optimal early warning threshold and generates a risk difference sequence; and the decision generation module analyzes the sequence and integrates the sequence into a regulation and control decision scheme. According to the system, intelligent analysis of cultivated land data, dynamic early warning of degradation risks and accurate decision making are realized, and scientificity and efficiency of cultivated land resource management are improved.

The invention provides a remote sensing AI intelligent interpretation-based cultivated land change pattern spot intelligent identification method and system, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, comprehensively recording the cultivated land conditions of the target region in different time periods, carrying out the preprocessing operation of the obtained multi-temporal remote sensing image data set, and carrying out the recognition of the cultivated land change pattern spots. Then feature extraction operation is carried out on the preprocessed multi-temporal remote sensing image data set, a spectral feature sequence and a texture feature sequence of a target area are generated respectively, farmland features are described in detail from different angles, and finally, a time sequence analysis method is combined to analyze the farmland features. And performing change detection processing on the generated spectral feature sequence and texture feature sequence to generate a cultivated land change pattern spot data set of the target area, thereby effectively improving accuracy and efficiency of cultivated land change pattern spot identification.

The invention discloses a farmland abandoned land identification method based on multi-source remote sensing data and time sequence correction. The method comprises the following steps: 1, data acquisition and preprocessing; 2, extracting a key phenological period; 3, constructing a feature library; 4, training and predicting a random forest model; and 5, post-processing and precision evaluation are carried out. The method comprises the following steps: firstly, integrating multi-source remote sensing image data, and strictly preprocessing the multi-source remote sensing image data to ensure the time-space consistency of the data; and accurately extracting key phenological period information by deeply analyzing the minimum vegetative period and the vegetative peak period of the vegetation through NDVI and NDSI time sequences. In the feature construction stage, multiple spectral indexes are fused, principal component analysis and feature importance screening are applied, feature dimensions are optimized, and model performance is improved. A random forest model is adopted for training and prediction, and a high-precision abandoned land spatial distribution diagram is finally generated by combining an oversampling technology and a category weight adjustment technology.

The invention relates to the technical field of image processing, in particular to a remote sensing monitoring method and system for cultivated land protection, and the method comprises the following steps: obtaining a remote sensing image sequence of a cultivated land region at a differential time node, extracting pixel gray values of the same land parcel, arranging the pixel gray values according to time, calculating the ratio of a difference value between adjacent frames to an intra-frame gray average value, and obtaining a remote sensing image sequence; and marking positions continuously exceeding the fluctuation reference ratio, and generating a pixel region set. According to the method, a sampling frequency threshold optimization strategy is adopted, abnormal areas are screened in combination with albedo change rate, short-time fluctuation interference is reduced, time sequence consistency is enhanced, a solar incident angle and a terrain slope are introduced to calculate projection influence, a slope surface shielding area is eliminated, and space grid marking and boundary integrity judgment are adopted; the method improves the continuity of cultivated land distribution information, combines time sequence analysis, sampling optimization, physical characteristic compensation and spatial consistency correction, enhances the fine recognition of the dynamic change of cultivated land, and improves the monitoring precision and data reliability.

The invention discloses a remote sensing image cultivated land segmentation method and system fusing context and boundary perception, and belongs to the technical field of remote sensing image processing and agricultural information. Constructing a cultivated land segmentation initial model composed of a backbone network, a feature enhancement module, a multi-scale feature fusion de-wharf module and a mask prediction module; training set data are input into the initial model, a composite loss function value is calculated, back propagation is executed, and a cultivated land segmentation model with boundary sensing ability is obtained through multi-round iterative optimization; and inputting the remote sensing image into the trained cultivated land segmentation model, and outputting a binary segmentation image representing the cultivated land position. Visual state space modeling and large receptive field convolution are combined, deep and shallow layer information is fused through feature injection, boundary perception supervision and composite loss are introduced, cultivated land boundary discrimination is improved, remote sensing image cultivated land high-precision extraction is achieved, and the method is suitable for agricultural interpretation and monitoring.

The invention relates to the field of soil spectrum analysis, in particular to a farmland soil environment detection method and system. The method comprises the following steps: firstly, analyzing fluctuation characteristics of near infrared spectrum curves of a plurality of soil samples in a preset window of a target wavelength to obtain undisturbed degrees of the near infrared spectrum curves in the target wavelength; fitting spectral data points of the near infrared spectral curve, and obtaining feature retention degrees of the extreme points on the fitted curve according to the difference of fitting absorbance of adjacent extreme points on the fitted curve, the absorbance of each wavelength and the difference of the fitting absorbance and the wavelength distribution of the extreme points on each fitted curve; filtering the spectral data points of the target curve based on the non-interference degree and the feature retention degree to obtain a filtering curve; according to the method, the interference information in the near infrared spectrum curve is removed, the existing key information is reserved as much as possible, and the precision of farmland soil environment detection is improved.

The invention discloses an automatic auditing and analyzing method for cultivated land soil data. The method comprises the following steps: S1, acquiring original soil data through multi-spectral imaging of an Internet of Things sensor and an unmanned aerial vehicle to form a data set; s2, using an extended Kalman filter to reduce noise and remove abnormal values, and using time sequence interpolation to complement missing data; s3, analyzing and auditing the preprocessed data based on the time sequence, and identifying an abnormal mode; s4, analyzing and fusing auditing data by using a causal reasoning graph neural network; s5, performing multi-dimensional analysis and prediction on the fused data by applying a big data analysis technology; s6, realizing real-time data visualization and interactive display through a visualization method driven by quantum computing; s7, using a natural language generation technology to generate a report for the soil data set and the analysis result; and S8, implementing encryption and access control in the data processing and transmission process. The invention provides a comprehensive soil data processing solution, and the data auditing efficiency, accuracy and safety are effectively improved.

The invention provides a fine-grained cultivated land protection potential optimization method, medium and system, and belongs to the technical field of cultivated land data processing.The method comprises the steps that firstly, multi-source remote sensing, soil and climate data are obtained, and a basic feature matrix is extracted through multi-dimensional decomposition; then, multi-scale analysis of soil quality features is realized by utilizing a pyramid neural network model, and adaptive fusion is carried out through a feature integration module; and then constructing a contribution degree optimization equation set, comprehensively considering four aspects of benchmark evaluation, space correlation, time sequence evolution and weight balance, realizing accurate evaluation of the cultivated land protection potential, and generating an optimized protection partition scheme. On one hand, the pyramid neural network model overcomes the problem of insufficient feature extraction of a traditional method through multi-scale feature extraction and adaptive fusion; and on the other hand, through multi-dimensional information fusion and dynamic optimization, the contribution degree optimization equation set solves the problem that accurate evaluation of the cultivated land protection potential is difficult to realize in the prior art.

The invention relates to the technical field of cultivated land supervision, in particular to a big data-based cultivated land utilization supervision system, which comprises a pattern spot remote sensing interpretation module, a pixel state aggregation module, a boundary mutation recognition module, a time sequence cultivation fluctuation analysis module and a response priority pattern output module. According to the method, a time sequence label grid set is established through spatial boundary analysis and pixel index extraction of remote sensing image spots, state labels are introduced to classify pixel tillage evolution, an image spot state evolution chain and path structure is constructed on the basis of spatial aggregation, and a continuous fluctuation area is extracted through sliding window analysis of planting behavior factors in the image spots. A co-occurrence block of boundary disturbance and behavior fluctuation is positioned in combination with a space overlapping relation, a response urgency index is constructed after multiple indexes such as state density, disturbance range and duration period are quantified, multi-dimensional judgment and intervention priority ranking of the farmland utilization abnormal area are achieved, and the identification precision and response efficiency of farmland utilization changes are effectively improved.

The invention discloses a cultivated land non-agrochemical detection method combining an attention mechanism and Mama, relates to the technical field of change detection, and solves the problems that during existing cultivated land non-agrochemical detection, false change of a detection result is caused by changes of imaging conditions, accurate spatial information is lost in downsampling operation, and the detection result is not accurate. Small targets cannot be effectively positioned, detail features cannot be effectively captured, missing detection is caused, and the edge of a change area is rough. According to the method, features of different levels and different scales are reserved through feature extraction, depth semantic information and space detail information are fused, information in all directions is thoroughly scanned through selective scanning, the global dependency relationship in the remote sensing image can be effectively captured, targets of different sizes are considered, and the global dependency relationship in the remote sensing image can be effectively captured. And the problem of missing detection of small targets is greatly improved. Through a cross attention mechanism, the features of the two time points are fused, and the space-time dependency relationship of the double-time-phase image is better captured, so that the model performance is improved.

The invention discloses an Aspergillus tubingensis strain and application thereof, the classification name of the Aspergillus tubingensis strain is Aspergillus tubingensis TL-B31f, and the preservation number of the Aspergillus tubingensis strain is CCTCC (China Center For Type Culture Collection) M 20241814. The Aspergillus tubingensis strain has the capabilities of efficiently dissolving phosphorus and producing iron carriers and 3-indoleacetic acid (IAA), can be applied to non-grain cultivated land as a growth promoting agent, and can improve soil matrix components, so that the non-grain cultivated land can be suitable for planting rice and promoting the growth of the rice, thereby being beneficial to improving the grain yield.

The invention discloses a fragmented cultivated land remote sensing extraction method based on space-time deep learning, and the method comprises the following steps: 1, data preparation: selecting a high-spatial-resolution remote sensing image, and capturing the space-time change of cultivated land in different growth cycles and environment conditions in combination with multi-temporal data of different times and seasons; a second step of model construction: constructing a deep learning model fusing ConvNeXt and a U-Net framework, namely a ConvNeXt-U model; 3, model training is carried out; 4, crushing cultivated land extraction; and 5, analyzing and outputting a result. Through multi-dimensional technical innovation, remarkable performance breakthrough and application value improvement are realized in the field of fragmented cultivated land remote sensing monitoring, and the method is specifically embodied in the following three aspects: 1, high precision and robustness improvement are realized; 2, optimizing calculation efficiency and generalization ability; and 3, dynamic evolution analysis and decision support capability.

The invention discloses a sand-blown saline-alkali area cultivated land quality evaluation method, relates to the technical field of computer agriculture, and aims to solve the problem that an existing cultivated land quality evaluation method is inaccurate in a sand-blown saline-alkali area cultivated land evaluation result. The method comprises the following steps: 1, collecting research area data of cultivated land in a sand-blown saline-alkali area, wherein the data comprises field survey data, remote sensing image data and other auxiliary data; 2, calculating a remote sensing index; 3, constructing a farmland quality evaluation system in the sandstorm saline-alkali area; 4, determining the weight of each evaluation index of the index layer by adopting a consistent matrix method; 5, carrying out membership degree assignment on each evaluation index based on a membership function and a Delphi method; 6, adopting an accumulation method to obtain a cultivated land quality comprehensive index P; and 7, dividing the distribution interval of the cultivated land quality comprehensive index P into ten grades by adopting an equidistant method. The method is high in speed, good in stability, low in cost and capable of achieving large-area and long-time-sequence cultivated land quality evaluation in the sandstorm and saline-alkali area.

The invention provides a black soil cultivated land organic carbon degradation diagnosis method based on geographic big data and AI, and the method comprises the steps: constructing a black soil area cultivated land organic carbon degradation potential influence factor data set and a diagnosis model, judging the spatial correlation of potential influence factors, training the diagnosis model based on the data set, and obtaining a diagnosis result of the black soil area cultivated land organic carbon degradation. And performing black soil area cultivated land organic carbon degradation influence factor identification based on the trained diagnosis model, and performing organic carbon degradation influence factor action mechanism analysis based on an identification result. According to the method, by utilizing high resolution and high timeliness of geographic space big data and cloud computing, multiple types of potential factors such as climate, soil, terrain and agricultural activities which influence organic carbon change of the cultivated land in the black soil area can be quickly calculated on a large-area scale, and manpower, material resources and time costs caused by large-area data collection and analysis are avoided; by using an artificial intelligence algorithm, the main control factor of organic carbon degradation of the cultivated land in the black soil area is accurately identified from multiple potential factors, and the current degradation situation can be objectively evaluated.

The invention provides a space-spectrum fusion high-standard farmland utilization mode remote sensing monitoring method and system, and relates to the field of remote sensing monitoring, and the method comprises the steps: carrying out the multi-temporal image synthesis and preprocessing of a remote sensing image set, and obtaining a key phenological period synthesis image set, the NDVI value of the research area in each key phenological period is calculated through the key phenological period synthetic image set; phenological characteristic analysis is performed on different farmland utilization modes based on each NDVI value, farmland utilization priori knowledge is combined to construct a cultivated land utilization map, and spectrum information of the cultivated land utilization map is acquired; performing multi-scale segmentation on the remote sensing image set to obtain field images with spatial information; and assigning spectrum information of the farmland utilization map to the field images with spatial information, and judging the farmland utilization mode of the research area. According to the invention, the spectrum information of the cultivated land utilization map and the field images with spatial information are fused, the farmland utilization mode of each field is visually displayed, and remote sensing monitoring of the farmland utilization mode at the land parcel level is realized.

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The invention discloses a cultivated land reserve resource potential grading method based on multi-dimensional cultivated land quality evaluation, particularly relates to the technical field of land resource management, and is used for solving the problems of insufficient cultivated land potential grading precision and lack of ecological constraints caused by single dimension and data splitting of an existing evaluation method. Interpreting land types by verifying and fusing cultivated land quality classification codes and remote sensing, complementing constraint fields such as ecological protection areas and pollution levels, and constructing a dynamic model associated with natural quality parameters and ecological limitation indexes; on the basis of land parcel type matching differentiation screening rules, threshold screening and suitability scoring are carried out on unutilized lands, abandoned garden lands and the like in a classified mode; eliminating ecological sensitive or pollution exceeding land parcels through logic verification, calculating a comprehensive potential score in combination with a multi-dimensional parameter weight and a dynamic threshold value, and dividing the comprehensive potential score into high, medium and low grades according to regional resource requirements; and the cultivated land quality multi-dimensional collaborative evaluation and ecological protection two-way decision is realized.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention provides a soil erosion evaluation and prediction method based on a public data set, and belongs to the field of soil erosion. An improved RUSLE model is constructed by obtaining public land utilization data, rainfall data, vegetation coverage data, soil texture data and DEM data of a target area, and the soil erosion condition of the target area is evaluated; four prediction scenes including a natural development scene, a city extension suppression scene, an ecological priority scene and a cultivated land protection scene are put forward according to policies, adaptive atlas under the four scenes are made, existing land utilization data and the adaptive atlas of all the scenes are input into a CA-Markov model, and land utilization conditions under all the scenes in the future are obtained; and on the basis of the predicted land utilization data, a water and soil conservation measure factor under each scene is obtained in combination with an assignment method, and then an improved RUSLE model is constructed in the grid, so that the soil erosion condition of the target area under multiple scenes in the future can be obtained.

The invention discloses a farmland field segmentation method and system based on multispectral SAM model and multitask learning guidance, and belongs to the field of field segmentation. The problem that in the prior art, channel number matching is conducted in a simple channel compression or deep convolution mode, and the challenges that the boundaries of cultivated land parcels are complex, diversified and subtle are difficult to effectively deal with is solved. The method comprises the following steps: performing spectrum compression on a multispectral remote sensing image through a U-Net structure fused with a CBAM attention mechanism to generate a three-channel pseudo visible light image; the image is input into an SAM-ViT encoder subjected to LoRA fine tuning, and global semantic features are extracted; a task token sequence is constructed, explicit interaction between the token and the image features is realized through a bidirectional Transform module, and high-resolution fusion features are generated; calculating cross-task attention, and outputting a multi-task mask through inner product operation; and performing post-processing by using a watershed algorithm to generate a field boundary. The method is used in the field of cultivated land identification.

The invention discloses a photovoltaic auxiliary greenhouse cultivation system which comprises a greenhouse body composed of a framework system and a photovoltaic ceiling, and the photovoltaic ceiling is laid on the top of the framework system, provided with a light splitting type solar thin film cell panel set with adjustable light transmittance and laid in a partitioned mode according to the illumination requirement. The dynamic light compensation system, the temperature balance system and the energy storage system are arranged at the top of the framework system, the energy storage system is used for storing energy and supplying power, the dynamic light compensation system is used for supplementing light for each indoor crop area according to photosynthetically active radiation and sun illumination conditions, and the temperature balance system is used for ventilating and humidifying the greenhouse; and the control machine is arranged on the greenhouse main body and is electrically connected with the dynamic light compensation system, the temperature balance system and the energy storage system so as to receive the monitoring data and control the operation of each electric appliance element. According to the system, lithium battery energy storage and a grid-connected inverter are integrated, the energy self-sufficiency rate is optimized, and the space utilization rate is increased through spectrum customization and three-dimensional planting; the method is suitable for tilled land shortage areas, and gives consideration to clean energy production and agricultural efficient output.

The invention relates to the technical field of cultivated land information management and systems, in particular to a cultivated land protection management system and method based on an informatization technology. A cultivated land protection management system based on an informatization technology comprises a storage database and a management system in communication connection with the storage database, and the management system is in communication connection with a GIS system and a mobile device through an API interface. The management system comprises a login module, a data management module, a space analysis module, a task management module, a cultivated land monitoring and early warning module and a user authority management module. The problems of low data management efficiency, insufficient informatization degree, low patrol work efficiency, lack of early warning mechanism, insufficient data sharing and authority management, lack of mobile application support and the like are solved, the efficiency and quality of cultivated land protection management are improved, and the method has important significance in protection and reasonable utilization of cultivated land resources.

The invention discloses a semantic edge and segmentation-based agricultural plot form local updating method and device. The method comprises the following steps of: preparing a plot boundary vector of a previous period of a specified application area and a later-period target area remote sensing image which finally needs to be locally updated and extracted; selecting and designing a multi-task learning network model capable of simultaneously generating semantic segmentation and semantic edges; using the semantic segmentation and semantic edge multi-task learning model to train a previous-stage plot boundary vector and a later-stage remote sensing image to obtain a multi-task learning model weight, predicting high-resolution remote sensing image data of a target application area, and obtaining a line result and a surface result of agricultural cultivated land of the application area; and performing small pattern spot removal processing on a surface prediction result, comparing results of two-stage semantic segmentation, and selecting updated land parcel elements according to a threshold value. The updated land parcel elements are classified and processed according to the following conditions: 1, common edges do not exist: if the common edges do not exist, an edge vector result generated by a multi-task learning model through later remote sensing is directly adopted; 2, existence of a common edge: in the two-stage remote sensing images, the area of the agricultural plot may change (increase or decrease), the two-stage images are compared according to an edge fusion algorithm, the same edge in the two-stage images is reserved, and the changed edge is updated; according to the invention, local updating of the remote sensing image plot is finally realized.

The invention provides a cultivated land quality monitoring system based on cultivated land protection. The cultivated land quality monitoring system comprises a multi-source sensing module, a heterogeneous transmission module, a space-time database module, a quality degradation prediction module, a dynamic early warning module, a repair decision module and a quality traceability block chain module. The multi-source sensing module collects data such as soil pH value, organic matter content, heavy metal concentration, crop canopy temperature and vegetation index; the heterogeneous transmission module carries out encryption packaging and format standardization on the data; the space-time database module stores the processed data and space-time associated information; the quality degradation prediction module generates multi-dimensional monitoring data and predicts a degradation risk; the dynamic early warning module generates a multi-stage early warning signal according to the risk value; the restoration decision module outputs soil improvement engineering technical parameters and ecological compensation schemes; and the quality traceability block chain module realizes data verifiable evidence storage. The intelligent level of cultivated land protection can be improved, and comprehensive technical support is provided for sustainable utilization of cultivated land quality.

The utility model discloses a modularized intelligent mobile fungus culture cabin, which belongs to the technical field of fungus culture cabins, and comprises support columns and heat insulation boxes, the bottoms of the four support columns are all fixedly provided with trundles, each heat insulation box is internally provided with a planting frame, monitoring equipment and a detection module, one end of each heat insulation box is fixedly provided with an intelligent special all-in-one machine, and the other end of each heat insulation box is fixedly provided with an intelligent special all-in-one machine. An illumination mechanism, a fresh air pipe and a humidifying pipe are arranged in the heat preservation box in a penetrating mode, and one end of the fresh air pipe and one end of the humidifying pipe are fixedly connected with the intelligent special all-in-one machine. The chamber body is supported through the supporting columns and the frame, the strength of the chamber body is improved, the chamber body is convenient to move through the trundles, conventional cultivated land is not occupied, the placement position is flexible, management and use are more convenient, equipment operation can be remotely monitored and controlled in a batched mode through communication of the 5G Internet of Things, various cultivation parameters in the fungus cultivation chamber are accurately controlled, and the cultivation efficiency is improved. And a remote monitoring user can monitor and track real-time data of a plurality of culture cabins in a centralized manner at any time.

The invention discloses a cultivated land quality grade evaluation method and system, and is applied to the technical field of intelligent evaluation, and the method comprises the steps: obtaining a cultivated land distribution state and a plurality of sampling points of a target region; acquiring basic information and soil information of the sampling points; the missing soil information of the missing sampling points is fitted through the soil information of the matched sampling points; and evaluating the cultivated land quality grade in the cultivated land improvement process through the basic information and the soil information of all the sampling points. According to the invention, the supplement of soil information loss caused by the fault or power failure of the soil sensor is effectively realized, so that the monitoring of the cultivated land quality grade change at different time points is more accurate, and the evaluation of the cultivated land quality grade change in the cultivated land improvement process is facilitated; a high-standard farmland cultivated land quality grade evaluation program and a technical method suitable for regional reality are formed, and a foundation is laid for accumulating experience for comprehensively developing high-standard farmland cultivated land quality grade evaluation.