44 results about "Moisture index" patented technology

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The present invention discloses a GNSS-R soil moisture inversion method for mountainous areas that integrates a terrain moisture index. The method comprises the following steps: obtaining remote sensing data from target mountainous areas other than water bodies and soil moisture data from each observation station; selecting altitude, slope, aspect, longitude and latitude, vegetation index, clay content, and signal-to-noise ratio as influencing factors for soil moisture inversion in mountainous areas; calculating the corrected signal-to-noise ratio and terrain moisture index; constructing a mountainous area soil moisture inversion model, training it, and validating it; inputting a test set into the trained model to obtain daily soil moisture prediction data with a 1km resolution, taking the monthly average, and obtaining monthly soil moisture prediction data. The present invention's integration of the terrain moisture index with CYGNSS data can fully leverage the advantages of terrain factors and remote sensing technology, further improving the accuracy of soil moisture distribution and the ability to predict spatiotemporal changes, and better revealing the dynamic changes in soil moisture in complex terrain areas such as mountainous areas.

The invention provides a sunlight-induced chlorophyll fluorescence reconstruction method and system based on satellite observation data. Comprising the following steps: collecting and processing ground site observation data, air temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir correction reflectivity data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, and OCO-2 and TROPOMI satellite observation SIF data; performing data reconstruction by using the OCO-2 SIF and the TROPOMI SIF as original data respectively by using a machine learning method; then, an empirical formula between the two kinds of reconstructed SIF data and the site observation SIF is constructed, and estimation parameters on the site are obtained through optimization; then, calculating estimation parameters of the empirical formula on each land coverage type; and finally, substituting the two types of reconstructed SIF data into an empirical formula to obtain a reconstructed SIF with site SIF information.

The invention provides a tobacco shred wet mass online detection method based on hyperspectral imaging and time-space fusion, which comprises the following steps: acquiring a hyperspectral data cube of a tobacco shred flow, extracting a tobacco shred area and generating a mask; calculating a standardized moisture index of each pixel point in the mask according to the spectral response values of the moisture absorption wave band and the reference wave band to obtain a standardized moisture index image; based on a set index threshold value, converting the standardized moisture index image into a binary image, and performing morphological cleaning; carrying out wet group identification and marking on the cleaned binarized image to obtain an external rectangular frame of the binarized image; overlapping or adjacent external rectangular frames are combined, the combined external rectangular frames are screened based on the minimum area threshold value, the external rectangular frames which do not meet the area condition are removed, and the final tobacco shred wet mass external rectangular frames are obtained; according to the invention, misjudgment caused by shielding is significantly reduced; the high-speed and real-time online detection requirements of a cigarette production line can be completely met.

The invention provides a remote sensing water line distance water depth inversion method, and belongs to the field of remote sensing water depth inversion. Aiming at the problems that the distance between a water depth point with geographic coordinates and a water boundary line is in direct proportion to the water depth and the chlorophyll content of water in a near-shore area is relatively high, the method comprises the following steps: selecting wave bands such as longitude and latitude, the distance between a pixel and the water boundary line, a coast wave band, a blue wave band, a green wave band and a red wave band; the improved normalized water body index (MNDWI), the red edge chlorophyll vegetation index (RECI) and the normalized difference moisture index (NDWI) serve as feature influence factors, a remote sensing waterline distance water depth inversion model is constructed, the water depth is inverted, and the remote sensing image water depth inversion precision can be effectively improved.

The invention discloses a GIS-based arid and semi-arid region farmland irrigation priority dynamic evaluation and zoning method, system, device and medium, and belongs to the technical field of farmland irrigation priority evaluation and zoning, and the method comprises the steps: collecting various types of remote sensing satellite data of a to-be-irrigated region; performing pretreatment; identifying the boundary and range of the to-be-irrigated area, and extracting a remote sensing image of the to-be-irrigated area; based on the remote sensing image of the to-be-irrigated area and the basic geographic information, parameter calculation is carried out, and normalization processing is carried out; performing superposition calculation on each normalized parameter according to a set weight to obtain an irrigation priority index; partitioning the to-be-irrigated area based on the irrigation priority index to generate an irrigation priority map; and judging whether agricultural irrigation management requirements are met or not according to preset conditions. According to the method, temperature vegetation drought indexes, normalized moisture indexes, water resource accessibility and crop economic value parameters are coupled; the irrigation priority index weight can be automatically adjusted according to a season strategy.

The invention provides a remote sensing estimation method for high spatial-temporal resolution crop evapotranspiration in an arid and semi-arid region, which comprises the following steps of: acquiring cloudless or less-cloudless high spatial resolution optical remote sensing data of a crop growing season in a research region, and calculating or inverting remote sensing earth surface parameters required by high spatial resolution evapotranspiration; comprising a vegetation index NDVI, a leaf area index LAI, a vegetation coverage FVC, a surface water index LSWI and a surface albedo; the method comprises the following steps: acquiring meteorological reanalysis data of daily scales ERA5-Land and GLADAS of a research area, wherein the meteorological reanalysis data comprises relative humidity RH, average temperature Ta, air pressure Pa, solar short-wave radiation # imgabs0 #, downward long-wave radiation # imgabs1 # and upward long-wave radiation # imgabs2 #; according to the technical scheme, multi-source remote sensing data, an evapotranspiration physical model and a space-time fusion technology based on evapotranspiration characteristics are utilized, and estimation of high space-time resolution evapotranspiration of the arid and semi-arid agricultural areas is achieved.

The invention discloses a stony desertification differentiation treatment method based on regional analysis, and relates to the technical field of environmental protection stony desertification treatment, and the method comprises the steps: obtaining the vegetation ecological image data of a stony desertification region through a remote sensing technology, obtaining the vegetation image of the stony desertification region in a manner of taking a remote sensing image as a main part and taking a computer image as an auxiliary part; processing the vegetation image of the stony desertification area to obtain a processed vegetation image; the method comprises the following steps: acquiring meteorological data of a stony desertification area, and performing spatial interpolation through an inverse distance weighting method to obtain gridding meteorological data with the spatial resolution of 1km * 1km; constructing a drought monitoring model of a vegetation moisture index, a vegetation water supply index, a vegetation temperature index, an air temperature and a vegetation drought index in the stony desertification area through the processed vegetation image and the grid meteorological data; according to the method, the drought and vegetation conditions of the stony desertification area are evaluated through the vegetation coverage, the minimum vegetation water demand threshold value and the vegetation suitable ecological water demand threshold value, and the stony desertification treatment efficiency is improved.

The invention relates to a method for estimating high-resolution atmosphere carbon dioxide concentration in a land area of China, which comprises the following steps of: (1) calculating a vegetation index, a surface water index and a vegetation phenology index by using satellite data, and generating a vegetation data file; (2) generating a carbon dioxide background field file and a meteorological background field file by using a CAMS global greenhouse gas concentration data set and a fifth-generation atmosphere reanalysis data set of the European mid-term weather forecast center; (3) generating a human-derived carbon dioxide emission data file by using a multi-scale emission list of the Xinghua University; (4) constructing an optimized atmospheric carbon dioxide simulation system; and (5) inputting the vegetation data file, the carbon dioxide background field and meteorological background field file and the man-made source carbon dioxide emission data file into the optimized atmospheric carbon dioxide simulation system, and operating to obtain a high-resolution atmospheric carbon dioxide concentration estimation result in the land area of China. The method can significantly improve the ability to estimate the carbon dioxide concentration of the atmosphere in the land area of China.

The invention relates to the technical field of geological disaster early warning, in particular to a rainfall type landslide critical early warning method and system, and the method comprises the steps: obtaining multi-source geographic data of a target region, and generating grid data through a spatial interpolation method; extracting terrain parameters and terrain humidity indexes based on a DEM model, and determining landslide susceptibility evaluation factors in combination with stratum lithology, soil types and human activity data; distributing weights by adopting an analytic hierarchy process, dividing a landslide susceptible area and positioning a prediction area; based on historical disaster data and rock-soil body permeability coefficients, landslide critical curves of different stratum lithology are dynamically fitted, and a landslide occurrence probability is output through a random forest algorithm; and multiplying the susceptibility by a probability result to generate a gridding risk value, and dividing early warning grades. The method has the effect of improving the precision and timeliness of landslide early warning analysis.

The invention relates to the technical field of garbage classification, in particular to an intelligent kitchen garbage classification system based on the Internet of Things, which comprises a node access module, an image preprocessing module, an edge structure judgment module, an oil-water proportion sensing module and a garbage type classification module. According to the method, effective binding of node states is realized through combination of node geographic identifiers and timestamps, the spatial traceability precision of garbage throwing behaviors is improved, the physical identification accuracy of garbage components in the image is enhanced through linkage of image gray difference extraction and spectral reflectivity calculation, direction features are constructed through gradient amplitudes of edge regions, and the spatial traceability precision of the garbage throwing behaviors is improved. The target stability of edge features in the image is enhanced by combining a texture consistency screening mechanism of a continuous region, and spectrum inversion processing of oil and water distribution in the image is completed through grease feature peak matching and moisture index threshold extraction; and the imaging recognition capability of typical components of the kitchen waste is improved, and the reliability of kitchen waste image recognition and the automation level of the classification process are enhanced.

The invention discloses a multi-mode rubber plantation remote sensing identification and forest age estimation method, which utilizes multi-source remote sensing data and combines an advanced image processing and analysis method to accurately monitor and estimate the distribution and forest age of different modes of rubber plantations such as field and small farmers. Firstly, through data preparation and integration, unsupervised image segmentation and classification optimization of a Gaussian mixture model are combined, and a high-precision rubber plantation distribution map is generated. And secondly, phenological characteristics are extracted based on time sequence vegetation-moisture index (such as NDWI) data, the planting year of the rubber tree is identified, the forest age is estimated, a rubber forest age distribution diagram is finally generated, and scientific support is provided for plantation management and ecological assessment.

The invention provides a cotton sample cleaning and feeding device and method based on seed cotton detection and metering, and belongs to the technical field of cotton sample processing equipment. Comprising a working box body, a seed cotton pushing unit, a detecting and pressing unit, a preliminary cleaning and conveying unit, a secondary cleaning and recycling unit, a cotton stripping processing unit, a cotton seed collecting unit and a cotton sample collecting unit are integrated in the working box body, and all the units are transmitted through a synchronous belt wheel set. The method comprises the steps that seed cotton is pushed to the detection unit to be pressed and detect the moisture index, large-particle impurities are separated through the cotton feeding roller and the barbed nail roller of the primary cleaning unit, then multi-stage cleaning is conducted through the rack roller and the cotton doffing roller of the secondary cleaning unit, noil is reduced through the recovery roller, and finally cotton seeds and pure cotton samples are collected in a classified mode after cotton stripping is conducted. By the adoption of the cotton sample cleaning and feeding device and method based on seed cotton detection and metering, the cotton hull cleaning efficiency is improved to 98%, the noil rate is controlled to be 0, the problem that detection is inaccurate due to the fact that an existing device is low in impurity cleaning efficiency and high in noil rate is effectively solved, and the accuracy of seed cotton detection indexes is guaranteed.

The invention provides a vegetable crop growth information method and system based on spectrogram fusion, and relates to the technical field of crop growth monitoring. The method comprises the following steps: acquiring remote sensing images of vegetable crops at different growth stages in multiple wavebands, correcting the remote sensing images to generate a first image set, acquiring moisture content, chlorophyll content and nitrogen content of the vegetable crops, firstly extracting reflectivity data of near-infrared and short-wave infrared wavebands in the first image set, and generating a normalized differential moisture index; then generating a normalized difference chlorophyll index by using red edge and red light wave band data; and generating a normalized differential nitrogen index at specific bands of 1510 nm and 1680 nm. And establishing a relation curve between the normalized difference indexes of the vegetable crops in different growth stages and the moisture content, the chlorophyll content and the nitrogen content, determining the growth stages of the vegetable crops to be identified, collecting multispectral images of the vegetable crops, generating the normalized difference indexes, and determining the moisture content, the chlorophyll content and the nitrogen content according to the relation curve.

The invention discloses a moso bamboo forest big-year and small-year classification method suitable for different areas, and belongs to the technical field of remote sensing monitoring, and the method comprises the following steps: obtaining a plurality of remote sensing images of a selected area in two consecutive years; calculating a normalized vegetation index, a land surface moisture index, a near-infrared wave band reflectivity and a red edge wave band reflectivity of each pixel in each remote sensing image; calculating a moso bamboo time sequence index of each pixel; calculating a phenological difference index of each pixel; judging whether the position represented by each pixel is a moso bamboo forest or a non-moso bamboo forest based on the moso bamboo time sequence index of each pixel, and obtaining a binary image; and based on the binary image and the phenological difference index of each pixel, judging whether each pixel is a mao bamboo forest, a young mao bamboo forest or a non-mao bamboo forest. According to the method, the classification precision of the moso bamboo forest in all years and the regional applicability can be improved.

An embodiment of the present invention provides a soil nutrient spectrum detection and regulation method and system, belonging to the technical field of soil nutrient spectrum detection and regulation. The method includes: collecting soil multispectral raw data and environmental data, generating a calibration matrix and nonlinear correction parameters, performing optical reflectance conversion, and synchronously fusing the time-domain reflectometry dielectric constant and the spectral moisture index to obtain a fused moisture content; selecting a nutrient diagnostic characteristic band pair to obtain a moisture-suppressed spectral index; using a pre-trained transfer learning model to predict soil nutrient content, obtaining a soil nutrient content prediction value, and performing fertilization regulation based on the fertilization decision risk coefficient. The present invention addresses extreme soil moisture measurement deviations through soil type adaptive moisture fusion technology; through multi-source risk quantification decision-making and PID adaptive regulation, it can dynamically optimize fertilizer application and ensure execution safety, forming a precision agriculture closed loop of detection-decision-execution.

The present invention discloses a method for detecting farmland plot irrigation by integrating multi-source remote sensing data, which includes obtaining time-series remote sensing data and auxiliary data and performing preprocessing; based on the above data, obtaining the normalized difference moisture index, land surface temperature and precipitation at the plot scale; constructing a scatter plot of NDMI-LST feature space with the normalized difference moisture index at the plot scale as the abscissa and the land surface temperature as the ordinate, obtaining the upper and lower boundary lines of the NDMI-LST feature space, and calculating the temperature vegetation drought index; constructing a time-series curve of the temperature vegetation drought index at the plot scale, setting a rule set for plot irrigation detection, establishing a plot irrigation detection algorithm with an adaptive dynamic threshold, obtaining the plot irrigation cycle, and obtaining the irrigation information of the analysis and statistical area. Therefore, by using the above method, it is possible to quickly and accurately extract farmland plot irrigation events and cycle information, providing a reference for decision-making such as water resource management and agricultural production activities.

The invention provides a filtering performance determination method, device and equipment, a medium and a program product, and relates to the technical field of iron ore powder grinding and separation. The method comprises the following steps: obtaining the target molecular water content and the target capillary water content of a target type of iron ore powder sample after wet grinding, and the target water content of a target filter cake obtained by filtering the iron ore powder sample; determining a target moisture index based on the target molecular water content, the target capillary water content and the target moisture content; obtaining the average particle size of target mineral powder obtained by freezing and scattering the target filter cake; determining a target dispersion index based on the average particle size; based on the target moisture index and the target dispersion index, the filtering performance of the iron ore powder of the target type is determined. According to the filtering performance determination method, device and equipment, the medium and the program product provided by the invention, the filtering performance of the iron ore powder can be accurately determined.

The present invention provides a method and system for reconstructing sunlight-induced chlorophyll fluorescence based on satellite observation data, comprising: collecting and processing ground site observation data, temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir-corrected reflectance data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, OCO-2 and TROPOMI satellite observation SIF data; then using a machine learning method to reconstruct data using OCO-2 SIF and TROPOMI SIF as original data respectively; then, constructing an empirical formula between the two reconstructed SIF data and the site observation SIF, and optimizing to obtain estimated parameters at the site; then, calculating the estimated parameters of the empirical formula for each land cover type; finally, substituting the two reconstructed SIF data into the empirical formula to obtain a reconstructed SIF with site SIF information.

The invention relates to a monthly-scale net primary productivity intelligent prediction method based on multi-source data fusion, and the method comprises the steps: extracting time features according to a monthly-scale GPP sequence, monthly meteorological data and a soil moisture time sequence of a to-be-predicted region; extracting spatial features according to the photosynthetically active radiation absorption ratio, the digital elevation model and the terrain humidity index of the region to be predicted; and the cross attention module based on physical guidance performs weighted recombination on the time features and the space features, and outputs a monthly-scale net primary productivity predicted value according to the recombination features. Through multi-source data deep fusion, spatial-temporal feature collaborative modeling and physical constraint guidance, the problems of monthly-scale NPP product deficiency, insufficient environmental adaptability and the like in the prior art are solved, and a high-precision and high-resolution innovative tool is provided for vegetation productivity monitoring.

This invention discloses a remote sensing identification and stand age estimation method for multi-mode rubber plantations. Utilizing multi-source remote sensing data and advanced image processing and analysis methods, it accurately monitors and estimates the distribution and stand age of rubber plantations with different modes, such as large-scale and small-scale farming. First, through data preparation and integration, combined with unsupervised image segmentation and classification optimization using Gaussian mixture models, a high-precision rubber plantation distribution map is generated. Second, based on time-series vegetation-moisture index (e.g., NDWI) data, phenological features are extracted to identify the planting year of rubber trees and estimate stand age, ultimately generating a rubber stand age distribution map, providing scientific support for plantation management and ecological assessment.

The invention discloses a remote sensing moisture index construction method combining surface temperature and short wave infrared information. The method comprises the following steps: step 1, collecting satellite remote sensing data in a research area; step 2, meteorological radiation data collection and spatial interpolation of a research area; 3, performing remote sensing inversion on the surface biophysical parameters; step 4, calculating surface net radiation and soil heat flux; step 5, constructing a remote sensing moisture index combined with the surface temperature and the short wave infrared information; and step 6, performing space-time reconstruction on the remote sensing moisture index. The remote sensing moisture index combining the surface temperature and the short-wave infrared information is constructed, the key defect that a traditional moisture index based on the surface temperature information is greatly influenced by albedo is overcome, and the supersaturation effect of the moisture index based on the short-wave infrared information in a high vegetation coverage area is optimized; satellite thermal infrared and short wave infrared observation information can be fully utilized, and large-range, data-area-free, complex and non-uniform underlying surface moisture index calculation is achieved.

This invention discloses a method for dynamic monitoring and inversion of vegetation moisture based on the GNSS reflection geometric response index, belonging to the field of GNSS remote sensing inversion and surface ecological parameter monitoring technology. This invention solves the problems of existing GNSS-R vegetation moisture indices, such as reliance on empirical normalization, sensitivity to observation geometry, and difficulty in long-term stable comparative analysis. This invention utilizes direct sunlight and surface reflection signals observed by a ground-based GNSS receiver during continuous observation. Based on the response characteristics of the reflection signals to changes in satellite geometry, a vegetation reflection geometric response index is established to characterize the changing features of vegetation moisture status. This invention uses the establishment of a reflection response function and the extraction of geometric sensitivity parameters and stable historical reference states to quantitatively characterize vegetation moisture changes. This reduces the impact of noise interference or abnormal observations, eliminates the need for inversion using external remote sensing products or ground-based measured data, and is suitable for long-term continuous vegetation moisture monitoring in various climatic zones and vegetation types.

The invention discloses an extract water content detection method and system based on a two-waveband hyperspectrum, and belongs to the field of extract production. In order to realize efficient, lossless, real-time and accurate detection of the water content of the extract, the method for detecting the water content of the extract based on the two-waveband hyperspectrum comprises the following steps: acquiring a hyperspectral image of the extract in drying treatment; according to the spectral images of at least two different wavelengths, at least obtaining reflectivity corresponding to the two different wavelengths; obtaining the moisture index of the extract at least according to the reflectivity corresponding to the two different wavelengths; and calculating the dehydration rate of the extract according to the water index of the extract. The method can be applied to an extract production system, and has the advantages of high efficiency and real-time detection of the water content of the extract.

The invention relates to the technical field of data analysis, in particular to a corn canopy moisture sensing system and method based on an unmanned aerial vehicle sensor network, and the method specifically comprises the steps: obtaining a specific reflectivity image of a corn canopy; calculating a self-adaptive moisture index, and outputting a corn canopy moisture content thermodynamic diagram; a barrier effect coefficient and a corn transpiration source item are calculated, water vapor channel blocking hot spots are marked on the corn field three-dimensional structure model, the position of a peripheral barrier area and the position of an internal retention area are divided, the corn growth potential is estimated, and a high potential area and a risk area are distinguished; and carrying out plant thinning priority scoring, formulating a dynamic plant thinning strategy according to the plant thinning priority score, and outputting plant thinning coordinate points of the corn field. The problem that in the prior art, water vapor flux model simplification neglects the three-dimensional barrier effect, and consequently analysis of the water retention area is not accurate is solved.

The embodiment of the invention provides a camellia oleifera grafting quality assessment method and system, and relates to the technical field of camellia oleifera grafting quality assessment, and the method comprises the steps: collecting a hyperspectral spectrum of a graft union region under a preset wave band, and obtaining the hyperspectral spectrum of the graft union region; obtaining a moisture index of the graft union area according to the hyperspectral spectrum of the graft union area; preset temperature stimulation is applied to the graft union area, and a physiological response infrared image sequence of the graft union area after temperature stimulation is obtained; calculating the temperature recovery rate of the graft union area according to the infrared image sequence; evaluating the temperature distribution uniformity according to the infrared image sequence; and judging the healing quality of the graft union according to the moisture index, the temperature recovery rate or the temperature distribution uniformity. The survival rate and the subsequent growth quality of the camellia oleifera grafted seedlings can be improved.

The invention provides a use control method and system for a concrete spraying maintenance device, and the method comprises the steps: obtaining a spraying request, and controlling a spraying device located at the top of a slope in a dam slope according to the spraying request to carry out the spraying of a top grid unit of the dam slope; wherein the slope of the dam is uniformly divided into a plurality of grid units in advance, and each grid unit is provided with a corresponding spraying device; a multispectral image of the dam slope is obtained, the moisture index of each cell in the multispectral image is calculated, and the position of a moist frontal surface in the dam slope moisture infiltration process is monitored in real time based on the moisture indexes; and dynamically adjusting a spraying strategy of the slope of the dam according to the position of the wet frontal surface, and controlling a spraying device of a specified grid unit to execute corresponding spraying operation according to the adjusted spraying strategy. According to the mode of dynamically adjusting the spraying strategy, the water resource utilization rate can be increased, the good maintenance effect of the concrete structure is guaranteed, and meanwhile water resource waste can be reduced.

The present invention relates to a method for estimating high-resolution atmospheric carbon dioxide concentration in China's land area. The method comprises the following steps: (1) using satellite data to calculate vegetation index, surface moisture index, and vegetation phenology index to generate vegetation data files; (2) using the CAMS global greenhouse gas concentration dataset and the fifth-generation atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts to generate carbon dioxide background field and meteorological background field files; (3) using the multi-scale emission inventory of Tsinghua University to generate anthropogenic carbon dioxide emission data files; (4) constructing an optimized atmospheric carbon dioxide simulation system; and (5) inputting the vegetation data files, carbon dioxide background field and meteorological background field files, and anthropogenic carbon dioxide emission data files into the optimized atmospheric carbon dioxide simulation system and running the system to obtain high-resolution atmospheric carbon dioxide concentration estimates for China's land area. The present invention can significantly improve the ability to estimate atmospheric carbon dioxide concentration in China's land area.

The moisture control method for air flow drying process comprises the following steps: comparing the reference moisture of the product with the real-time moisture of the material to obtain a first error, wherein the reference moisture is the moisture index of the product; obtaining a temperature fine-tuning value according to the first error; predicting a compensation temperature according to the real-time detected inlet air temperature and outlet air temperature and the real-time moisture of the material, wherein the compensation temperature is the theoretical outlet air temperature predicted in advance based on the real-time working condition; superimposing the temperature fine-tuning value and the compensation temperature to obtain a reference temperature; adjusting the rotating speed of the feeding mechanism for feeding to adjust the real-time moisture according to the reference temperature; and repeating the above five steps. The control method can overcome the time lag existing in the air flow drying process and improve the precision of moisture control. A control system for executing the control method is also provided.

The invention discloses a soil water content determination method, prediction model and system based on multispectral data and machine learning inversion. The method comprises the following steps: firstly, constructing a standard sample set containing a plurality of soil types and a plurality of water content gradients; then, collecting a multispectral image of the sample and extracting spectral reflectivity; calculating a plurality of vegetation indexes including a normalized vegetation index NDVI, a soil conditioning vegetation index SAVI, a normalized moisture index NDWI, a ratio vegetation index RVI and a normalized red edge index NDRE based on the reflectivity; and finally, taking the multiple vegetation indexes as input characteristics, taking the actually measured water content as an output target, and adopting a random forest algorithm to train to obtain a soil water content prediction model which can be simultaneously suitable for the multiple soil types. Through multi-feature fusion and ensemble learning, the generalization ability of the model among different soil types is effectively improved, and the problem that a single model is difficult to be universally applied to various soils is solved.