417 results about "Soil nutrients" patented technology

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

The invention relates to the technical field of organic fertilizers and planting of watermelons and muskmelons, and provides a special fertilizer for milk irrigated watermelons and muskmelons and a double-stubble planting method for sweetness enhancement of milk irrigated watermelons and muskmelons. The special fertilizer for the milk irrigated watermelons and muskmelons comprises black goat manure, sophora alopecuroide, sesame seed meal, bran, organic fresh milk, brown sugar and zymophyte. The black goat manure is pulverized into powder by a pulverizer, the sophora alopecuroide is pulverizedinto 1 cm to 2 cm, and the ratio of the black goat manure, the sophora alopecuroide, the sesame seed meal, the brans, the organic fresh milk, the brown sugar and the zymophyte is 2700-3200: 80: 50: 150: 5: 1. The double-stubble planting method for sweetness enhancement of the milk irrigated watermelons and muskmelons comprises the following steps: selection of a nursery seedling plate and soil, cleaning and germination accelerating of seeds, soil preparation, transplanting, irrigation, topdressing, shaping and forking of watermelon and muskmelon vines and fruit bearing of the watermelons and the muskmelons. The problems that in an existing watermelon and muskmelon planting process, soil nutrients are damaged due to the fact that chemical fertilizers are used for a long time, and the sweetness of melons is reduced are solved.

The invention provides a fertilization control method and system, and belongs to the technical field of smart agriculture, and the method comprises the steps: determining a target element-deficient region based on a multispectral remote sensing image of a target region crop, a crop type and a crop growth stage; inputting the multispectral reflectivity data, the soil nutrient data and the crop growth stage of the target nutrient-deficient area into a preset nutrition diagnosis model to obtain a deficient nutrient element type and a deficient degree; inputting the nutrient element type, the lack degree, the soil nutrient data, the crop growth stage, the planned irrigation water amount and the fertilizer mother liquor concentration into a water and fertilizer decision model, and calculating to obtain the target fertilizer liquor concentration and the total fertilizer demand; and controlling the fertilization device to adjust the mother liquor injection proportion and the irrigation flow based on the target fertilizer solution concentration and the total fertilizer demand. The fertilization control method and control system provided by the invention realize accurate regulation and control of water and fertilizer, not only can improve the utilization rate of water and fertilizer, but also can improve the yield and quality of crops.

The invention discloses an intelligent agricultural planting management method and system, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data layer construction, water and fertilizer prediction layer construction, planting optimization layer construction and agricultural planting management. The space-time adaptive water and fertilizer prediction layer construction method combining the crop physiological load and the crop growth stage is adopted, the water demand and nitrogen, phosphorus and potassium nutrient demands of crops in the future stage are accurately predicted, the crop growth state and environment changes are dynamically responded, the water and fertilizer demand difference in different growth stages is considered, and the water and fertilizer prediction efficiency is improved. The accuracy and applicability of prediction are improved through time-space fusion, and accurate fertilization can be realized; a planting optimization layer construction method combining digital twinborn and multi-objective optimization is adopted, a water and fertilizer management strategy is subjected to refined and dynamic optimization under the condition of considering a crop growth stage, a soil nutrient condition and a future weather condition, and an optimal management strategy capable of improving the yield, saving water and fertilizer resources and reducing the nitrogen leaching risk is generated.

The invention discloses a soil fertilizer nutrition customization regulation and control method and system. The system comprises a data acquisition module, an environment compensation module, a soil nutrient dynamic prediction module, a target demand management module, a multi-target optimization module, a safety evaluation module, a monitoring self-adaptive learning module and the like, soil nutrition and safety indexes and external environment factors are acquired, data are corrected by using a compensation function, nutrient changes are predicted, a target range is set according to different growth stages of crops, and a monitoring self-adaptive learning module is established. A multi-objective optimization model is established based on organic fertilizer contribution and missing nutrients, an optimal formula is generated by comprehensively considering crop requirements, cost and environmental risks, and the model is dynamically updated through continuous monitoring and feedback closed loop after fertilization. According to the method, the fertilizer utilization rate and the fertilization precision can be remarkably improved, the soil acidification and heavy metal risks are reduced, and the balance of yield, quality and ecological safety is achieved.

The invention relates to the field of agricultural data analysis, in particular to a straw returning effect analysis method combining soil nutrients and crop yield, and the method comprises the steps: collecting a multi-source feature data matrix for risk assessment; performing conjoint analysis on the crop yield data and the soil attribute data to obtain a static nitrogen deficiency risk amplification factor; performing difference analysis on the vegetation index data, the yield data and the cluster centroid to obtain a dynamic growth response asymmetric regulatory factor; carrying out fusion evaluation on the static nitrogen deficiency risk amplification factor and the dynamic growth response asymmetric regulation factor to obtain risk perception asymmetric distance measurement based on yield data; the clustering process of risk perception asymmetric distance measurement based on yield data is subjected to zoning optimization, and an optimal management zoning result for formulating a variable fertilization strategy is obtained, so that the nitrogen deficiency risk of crops in the seedling stage is reduced, and the nutrient utilization efficiency and the yield stability are improved.

The invention discloses a fertilization recommendation method combined with a rice seedling raising fertilizer carrying technology in the technical field of agricultural production. The fertilization recommendation method comprises the following steps: S1, acquiring geology and seedling raising states; s2, presetting rice growth requirements; s3, calculating to obtain an additional nutrient amount; s4, dividing the growth cycle of the rice; s5, recommending the fertilization amount according to stages; according to the method, nutrient supply of seedling raising fertilizer is brought into a whole-growth-period fertilization plan, and accurate matching is achieved; on the premise that the yield is not affected, the fertilizing amount in the early and whole growth periods of rice is remarkably reduced, and the fertilizer utilization rate is increased; through dynamic matching of the early-stage fertilizer loading and supplying amount and the middle and later-stage fertilizer needing amount, resource waste caused by excessive fertilization is avoided, and the fertilizer cost is reduced; the nutrient proportion is scientifically distributed according to the nutrient absorption efficiency of each stage, and the fertilizer utilization rate is increased; the final yield can be improved; soil fertility indexes and historical data are combined, the profit and loss trend of soil nutrients is recognized, and cyclic utilization of the soil nutrients is achieved through dynamic matching of the sustained and controlled release fertilizer and the organic fertilizer.

The invention relates to the technical field of information management, in particular to a smart agriculture precision big data information management system, which comprises a growth index extraction module, a stage data clustering module, a nutrient fluctuation identification module, an agricultural material strategy generation module and a scheduling information scheduling module. According to the method, the acceleration criterion is constructed based on the continuous period leaf area index change sequence, transition node identification is completed in combination with the photosynthetically active radiation utilization rate and the nitrogen absorption rate change trend, accurate positioning of crop growth stage change time points is achieved, the stability of stage classification in a space region is enhanced, and the accuracy of crop growth stage classification is improved. The agricultural material putting grade is judged and the fertilization frequency and dosage are blended by analyzing a root zone soil nutrient concentration difference value and a rate fluctuation frequency detection mode in combination with moisture content and root activity conditions, and operation period adjustment is guided in a task schedule in cooperation with a meteorological element fluctuation rate. The precision of farmland crop stage identification and the timeliness of nutrient fluctuation response are integrally improved.

The invention relates to the field of soil nutrient management, in particular to a rice soil nutrient coordinated regulation and control method and system based on big data analysis, and the method comprises the steps: firstly, collecting data through a multi-source sensing array, and constructing a feature state space with time sequence memory; a physical field theory is introduced, and the buffer effect of soil on nutrients is modeled into a chemical potential energy field by utilizing a variety Hooke's law, so that the system instability is quantified, and the problem of excessive fertilization caused by a hysteresis effect is solved. In the aspect of control decision, rolling horizon optimization is adopted, the potential energy burden serves as a penalty term to be introduced into a genetic algorithm, and blind topdressing under model mismatch is prevented by adaptively correcting and searching a spatial constraint boundary through a predicted residual error of a previous period. And finally, a smoothing instruction is generated through low-pass filtering, and accurate and steady regulation and control of rice soil nutrients are realized.

The invention discloses a Vinck Cellulomicrobe and application thereof in degradation of corn straw and improvement of soil fertility, and belongs to the field of agricultural microbial technology and solid waste resource utilization. The bacterial strain can secrete a plurality of hydrolase and oxidase including AA10 family lytic polysaccharide monooxygenase, AA2 peroxidase, GH3 and GH1 beta-glucosidase, GH16 / GH17 beta-glucosan endonuclease and AA1 multi-copper oxidase. The bacterial strain has the advantages that the bacterial strain can secrete a plurality of hydrolase and oxidase; fermentation liquor is applied to soil according to the straw mass ratio of 1: 5, and the corn straw degradation rate can be increased under the condition that the mass ratio of soil to straw is 100: 1. Meanwhile, the microbial agent can synergistically improve soil nutrients and increase the contents of rapidly available phosphorus, rapidly available potassium, alkali-hydrolyzable nitrogen, soluble organic carbon and total phosphorus in soil. According to the microbial agent, a straw lignocellulose structure is effectively cracked through a multi-enzyme system synergistic effect, and the dual effects of accelerating straw degradation and improving soil fertility are achieved.

The invention relates to the technical field of agricultural cultivation management, in particular to a precise honeysuckle fertilization method based on soil nutrient monitoring, which comprises the following steps: S1, measuring soil nutrient parameters of a honeysuckle planting field; s2, the nitrogen supply level, the phosphorus supply level and the potassium supply level of the soil are judged; s3, determining the dilution ratio of the humic acid macroelement water-soluble fertilizer; s4, diluting the humic acid macroelement water-soluble fertilizer into a solution according to the dilution ratio; s5, applying the solution diluted in the step S4 in a root irrigation mode; s6, measuring the growth and quality indexes of the honeysuckle after fertilization to verify the fertilization effect. According to the method, on-demand application of the humic acid water-soluble fertilizer and traceable quantitative management of the fertilization effect are realized through closed-loop verification of the fertilization decision and the growth quality index based on the soil nutrient supply level.

The invention discloses a soil attribute high-precision mapping method and system for optimizing the sampling number by fusing an air-ground imaging hyperspectral technology, and belongs to the technical field of soil monitoring. The method comprises the following steps: collecting and preprocessing a regional unmanned aerial vehicle hyperspectral image; collecting a sample point soil sample and obtaining ground imaging hyperspectral data of the sample point soil sample; screening and optimizing a sample point subset based on ground imaging hyperspectral data, measuring the available phosphorus content and training a prediction model; segmenting and extracting a soil region in the unmanned aerial vehicle image by combining a vegetation index and a maximum between-cluster variance method; and carrying out wave band alignment and reflectivity correction on the ground imaging hyperspectrum and the unmanned aerial vehicle soil spectrum, complementing corrected unmanned aerial vehicle hyperspectral data, and inputting the corrected unmanned aerial vehicle hyperspectral data into a soil available phosphorus prediction model to carry out inversion mapping. The problems that ground imaging data coverage is insufficient and unmanned aerial vehicle data is easily affected by the environment are solved, and key technical support is provided for intelligent agriculture and accurate soil nutrient management.

The invention relates to the technical field of forest fertilization, in particular to a forest precise fertilization decision-making method and system based on soil nutrient profit and loss, and the method comprises the steps: obtaining basic information of a target forest land, and dividing forest growth stages based on the basic information; the method comprises the following steps: collecting an undisturbed soil sample and a mixed soil sample of a 0-40cm soil layer of a target forest land by adopting an S-shaped point distribution method, separating the mixed soil sample, and respectively measuring the soil nutrient content, the bioavailability, the soil enzyme activity and the microbial functional gene. According to the method, by measuring the dimension indexes, constructing a five-dimensional profit and loss evaluation index system for determining the reference value and dynamically adjusting the weight according to the forest land data, calculating the comprehensive profit and loss index, judging the nutrient state and generating the personalized fertilization scheme, the defects that an existing fertilization scheme is single in basis index and lacks an effective dynamic adjustment link are effectively overcome; the requirements of multi-generation continuous planting forest land are met, and the environmental risks such as fertilizer waste, soil acidification and water eutrophication are effectively reduced.

The invention discloses bacillus orientalis ZL3 for regulating rhizosphere soil microecosystems of haloxylon ammodendron soil in deserts and promoting the growth of the haloxylon ammodendron soil, particularly relates to a fungicide ZL3 for promoting the growth of haloxylon ammodendron and regulating rhizosphere microbial communities and application of the fungicide ZL3, and belongs to the technical field of microorganisms. According to the bacillus toonensis ZL3 provided by the invention, the conversion of soil nutrients to available nutrients is improved, and the rhizosphere microbial community structure is improved, so that the effect of promoting the growth of haloxylon ammodendron is achieved. When the ZL3 is applied to a haloxylon ammodendron field, the content of soil organic matters, the content of soil quick-acting potassium, the content of available phosphorus and the activity of sucrase are remarkably improved by 37.78%, 11.01%, 49.66% and 67.71% respectively. The plant height is increased by 17.59%, and the root length is increased by 109.45%. Meanwhile, the positive change of microbial communities is regulated and controlled, especially the enrichment of beneficial bacteria, the relative abundance of the prevotella and the vibrio is remarkably increased, and the relative abundance of the burkholderia is remarkably reduced. In general, inoculation of the ZL3 fungicide can promote the growth and development of haloxylon ammodendron by improving the soil fertility and improving the rhizosphere microflora structure.

The invention discloses a forest tree genotype-environment interaction modeling method based on multi-modal deep learning, and relates to the technical field of forest tree breeding, the method comprises the following specific steps: multi-modal data acquisition: adopting a high-throughput phenotype platform, a whole genome sequencing technology and soil nutrient detection equipment to acquire multi-modal data; sNP locus genotype data, soil key physicochemical index environmental data and growth-related morphology and biomass parameter phenotype data of forest trees are collected respectively; forest genotype, environment and phenotype multi-modal data are collected through the system, the genotype-environment interaction algorithm and the phenotype prediction model are constructed after preprocessing and fusion, the model can accurately predict forest phenotypes, the breeding screening period is remarkably shortened, the breeding selection precision and efficiency are greatly improved, and the method is suitable for large-scale popularization and application. The method effectively solves the problem that a traditional breeding mode is short in time and efficiency, enables breeding work to respond to market demands and environmental changes more quickly and accurately, and provides powerful support for sustainable development of the forestry industry.

The invention discloses a saline-alkali soil crop-soil system nutrient dynamic matching regulation and control method, relates to the technical field of agricultural planting, and aims to solve the technical problems of saline-alkali soil nutrient imbalance, limited crop nutrient absorption and low efficiency of a traditional regulation and control mode, and the method comprises the following steps: S1, preparation before sowing; performing layered deep scarification treatment on a plough layer of the saline-alkali soil, synchronously applying a salt control improvement material to a deep plough layer to improve a saline-alkali environment, and arranging a layered under-film drip irrigation system matched with crop root zone distribution; s2, monitoring in a whole growth period; in the whole growth period of crops, plough layer soil samples are collected in a layered mode, soil salinity and nutrient indexes are measured, meanwhile, the growth state of the crops is monitored, and the incidence relation between the soil indexes and crop growth is established. The method solves the problem that nutrient supply is disjointed with the requirements of the root zone and the growth period of the oil sunflower in the prior art, creates adaptive saline-alkali environment and nutrient conditions for the whole growth period of the oil sunflower, and guarantees normal growth and development of the oil sunflower.

The invention belongs to the field of guidance of crop planting through image data processing, and particularly relates to a crop nutrient abundance and deficiency diagnosis model based on unmanned aerial vehicle image data and application thereof. The invention firstly provides a method for constructing a crop nutrient abundance and deficiency diagnosis model based on unmanned aerial vehicle image data, and the method comprises the following steps: S1, collecting canopy multispectral data of crops with different fertilizer application amount gradients in different growth periods, and collecting soil samples; s2; processing the multispectral data to obtain a vegetation index of crop growth, and measuring a soil nutrient index; and S3, obtaining a regression equation for calculating the soil nutrient index by using the key vegetation index, and judging whether the fertilizer needs to be supplemented or not by using the full-amount fertilization contrast as a threshold value. The invention further comprises the model and application of the model in guiding fertilization. Through unmanned aerial vehicle multispectral monitoring and dynamic threshold analysis, nutrient demand changes of crops in different growth periods are accurately captured in combination with an intelligent algorithm, and a field phosphate fertilizer deficient area is effectively identified to realize accurate fertilization.

This invention discloses a non-uniform fertilization method for controlling non-point source pollution in sloping farmland, comprising: determining the type of cultivated crop and fertilization spacing based on the slope of the target treatment area; dividing the target treatment area into several plots of the same size, determining the slope length based on the slope of each plot, and dividing each plot into three slope positions: the foot, the middle, and the top; determining the fertilization amount at the top of the corresponding plot based on the soil nutrient background value; fertilizing each plot in the target treatment area, with the fertilization amount decreasing proportionally at the top, middle, and foot of the slope; collecting surface runoff samples generated after rainfall at the lower part of each plot, measuring pollutant indicators in the collected water samples to obtain non-point source pollution results; and after the crops on the sloping farmland are harvested, measuring and calculating the crop's fertilizer absorption and utilization efficiency, and obtaining the crop output under non-uniform fertilization. This invention can effectively reduce non-point source pollution in sloping farmland while ensuring crop yield, and has the advantage of low cost.

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

The invention provides a saline-alkali soil conditioner as well as a preparation method and application thereof, and belongs to the technical field of soil improvement. The saline-alkali soil conditioner is prepared from the following raw material components in parts by mass: 80 to 90 parts of edible fungus residues, 10 to 20 parts of humic acid and 0.1 to 0.3 part of a microbial agent. The saline-alkali soil conditioner provided by the invention can obviously reduce the pH and total salt content of the surface layer of soil, obviously improve the content of nitrogen nutrients in the soil, optimize the distribution structure of the nitrogen nutrients and reduce the risk of nitrogen leaching loss. Meanwhile, the water and fertilizer retention capacity of soil can be improved, soil nutrients can be supplemented, the soil fertility can be improved, the harm of soil salinity to crops can be reduced, and the stress resistance and the yield of the crops can be improved. The soil conditioner for the saline-alkali land can solve the problems that at present, soil of salinized farmland in Hetao irrigation districts is prone to hardening, the soil fertility is low, the soil water and fertilizer utilization rate is low, and the crop biomass is low.

The invention discloses a banana root zone nutrition regulation and control method and system based on integration of water and fertilizer, and the method comprises the following steps: obtaining soil monitoring data and meteorological data in a banana field, and constructing a soil nutrient diagnosis model and a banana growth prediction model; based on the soil nutrient diagnosis model and in combination with a banana growth prediction model, obtaining the fertilizer demand of each stage of banana growth; constructing a fertilization decision rule base, and determining an optimal fertilization formula and fertilization time on the basis of the fertilization decision rule base in combination with the fertilizer demand of the bananas in each growth stage; and acquiring an optimal configuration scheme of the fertilization equipment, and then transmitting the optimal fertilization formula and the fertilization time to the fertilization equipment for automatic fertilization. The informatization and intelligence level of banana planting is improved, and efficient utilization of water and fertilizer resources and improvement of the yield and quality of bananas are achieved.

The invention discloses an organic-inorganic composite acid soil conditioner with synergism of nitrogen element synergism and soil expansion and application, and belongs to the technical field of soil conditioners. The modifier prepared by the invention is prepared from an acid neutralizer, a structure modifier, a capacity expansion synergist and a nitrogen synergist and is prepared from the following components in parts by weight: 18 to 22 parts of dolomite powder, 6 to 10 parts of lime powder, 20 to 24 parts of oyster shell powder, 8 to 12 parts of humic acid, 4 to 8 parts of sargassum, 1 to 3 parts of sodium alginate, 7 to 11 parts of charcoal, 5 to 9 parts of sepiolite powder, 3 to 7 parts of bentonite, 6 to 10 parts of poly gamma-calcium glutamate and 3 to 7 parts of chitosan. The soil conditioner can regulate and control the soil acidity, buffer the soil capacity, improve the soil structure and balance soil nutrients, solves the problems of high re-acidification rate, soil hardening and nutrient imbalance of a traditional lime conditioner, and is suitable for pHlt; and 6.5, repairing the acid soil.

The invention discloses hillside orchard fertilization equipment with intelligent detection and precise fertilization, and belongs to the technical field of agricultural fertilization equipment. The hillside orchard fertilization equipment with the intelligent detection and precise fertilization functions comprises a frame, wheel carriers are installed on the two sides of the bottom end of the frame, crawler wheels are installed at the two ends of one side of each wheel carrier, a crawler sleeves the outer sides of the pair of crawler wheels, a first motor is installed on one side of each wheel carrier, and the output end of each first motor is connected with one end of the corresponding crawler wheel; one side of the top end of the frame is provided with an extension arm, one end of the extension arm is provided with a fixing plate, the bottom end of the fixing plate is provided with an extension rod, the bottom end of the extension rod is provided with a soil nutrient sensor, one end of the extension arm is provided with a height adjusting mechanism used for adjusting the height of the fixing plate, and the other side of the top end of the frame is provided with a liquid storage tank. The system can effectively realize integration of intelligent detection and precise fertilization, improves the fertilization efficiency and the fertilizer utilization rate, and has a relatively high practical value.

The invention discloses a deep learning-based precise sorghum fertilization amount dynamic adjustment method and system, and relates to the technical field of deep learning-based fertilization amount control. According to soil nutrient concentration and sorghum root physiological index data, a time sequence analysis method is adopted to generate a time sequence change sequence of root absorption capability; and inputting a pre-trained deep learning model to determine a nitrogen-phosphorus-potassium demand difference value. And when the demand difference value exceeds a preset demand threshold value, optimizing a fertilization amount ratio by using a convolutional neural network, simulating a response of a root system to fertilization adjustment in combination with a recurrent neural network, and outputting a target fertilization regulation and control scheme when it is detected that a predicted response accords with an expected yield index, so that equipment is controlled to perform fertilization subsequently according to the target fertilization scheme. According to the method, a nonlinear mapping relation between the fertilization amount and the physiological response of the sorghum root system is established through a deep learning technology, and the fertilization proportion of nitrogen, phosphorus and potassium is dynamically adjusted in combination with time sequence data, so that precise sorghum fertilization amount regulation is realized.

The present application discloses an agricultural irrigation method and system, a device, a medium, and a product. The method comprises: acquiring a farmland condition dataset of an area to be irrigated; on the basis of a comparison result between minimum soil moisture content data and farmland water-holding capacity data, determining whether to generate a first irrigation instruction, the first irrigation instruction being used for controlling on of a first solenoid valve and a first water pump which are located on a first pipe, and watering a farmland on the basis of the first irrigation instruction; on the basis of a comparison result between soil nutrition data and preset nutrition data, determining whether to generate a second irrigation instruction, the second irrigation instruction being used for controlling on of a second solenoid valve and a second water pump which are located on a second pipe, and fertilizing the farmland on the basis of the second irrigation instruction; and on the basis of a level of an expected crop disease index, determining whether to generate a third irrigation instruction, and applying pesticides to the farmland on the basis of the third irrigation instruction.

The invention relates to a rapid method for determining precise fertilization of cigars. Comprising the following steps: 1) collecting spectral data of a cigar canopy in a cigar field by using a portable hyperspectrometer, 2) detecting soil nutrient indexes such as soil available nitrogen, available phosphorus, available potassium and pH in situ in the field by using an electrochemical soil nutrient sensor to obtain real-time detection data of soil nutrients; 3) acquiring meteorological data of the target plot and growth period information of the cigars, wherein the growth period information is divided based on the number of days after the cigars are transplanted; 4) preprocessing and standardizing the canopy spectral parameters, the soil nutrient real-time detection data, the meteorological data and the growth period information to form multi-source fusion input feature data; 5) inputting the multi-source fusion input feature data into a fertilization decision model based on fuzzy logic, and outputting a fertilization formula and a fertilization amount of the cigar in the current growth period; and (6) executing corresponding fertilization operation according to the fertilization formula and the fertilization dosage, regularly repeating the steps (1)-(5) after fertilization, and carrying out rolling correction on a fertilization strategy to realize precise fertilization management of cigars. The fertilizer utilization rate can be remarkably increased, the soil salinization risk caused by excessive fertilization is reduced, the proportion of first-class tobacco is increased, the oil content and the chromaticity of the eggplant coating are improved, economic benefits and ecological benefits are both considered, and the laboratory detection cost and the labor cost are reduced.

The invention discloses a soil nutrient inversion method based on multi-source physical constraint and a multi-scale attention network, and aims to solve the problems that a soil nutrient inversion method based on remote sensing lacks enough truth value marking data and does not consider spatial autocorrelation of soil nutrients in geographic space and a physical imaging rule influenced by a microenvironment and an earth surface structure. Therefore, the problems of deviation of the inversion value and lack of spatial texture constraint and geographical continuity of the soil nutrient content distribution map generated by inversion are solved. According to the method, the collaborative Kriging trend and soil microenvironment and earth surface structure characteristics are introduced as physical constraints, a sample expansion confrontation model is constructed, sample expansion is performed by a scientific method integrating a physical environment and a spatial law, the physical imaging law of soil nutrients is fully considered, and the accuracy of the sample expansion confrontation model is improved. In combination with a designed multi-scale residual attention inversion model for extracting key information from a spectrum, the generated soil nutrient content distribution map is relatively excellent in spatial texture constraint and geographical continuity.

The invention provides a power grid construction project water and soil conservation management system and method based on big data analysis, and relates to the field of power grid construction projects, the method is helpful to timely discover the influence of a power grid construction project on vegetation and soil, and corresponding protection measures are taken by pre-estimating the future growth condition of plants and the water and soil loss risk in advance, so that the water and soil conservation management of the power grid construction project is realized. The water and soil loss monitoring efficiency of each region of the power grid construction project can be reasonably adjusted by analyzing the abnormal soil growth and the water and soil loss, and the water and soil loss risk region can be accurately and quickly found; from the analysis of the influence of concrete pouring on soil adhesion, the analysis of regional soil growth abnormity, and the prediction of future growth of plants and the analysis of water and soil loss, a plurality of factors such as plant growth conditions, electric field stimulation, soil adhesion, soil nutrients and the like are comprehensively considered, and the influence of a power grid construction project on water and soil conservation is comprehensively evaluated.

The invention discloses streptomyces pseudogriseogriseus for promoting straw degradation and increasing soil organic matters, and belongs to the technical field of microorganisms. The streptomyces pseudogriseolus NF3 disclosed by the invention has the activity of CMC-Na enzyme, xylanase, laccase, lignin peroxidase and manganese peroxidase, and can be used for degrading cellulose, hemicellulose and lignin at the same time, and the degradation rate of the streptomyces pseudogriseolus NF3 on corn straws reaches 67.75%; the fertilizer is applied to straw returning soil, soil nutrient release is accelerated, the soil organic matter content is increased, and the crop yield is increased.