3868 results about "Crop growth" patented technology

The invention discloses a crop growth prediction method based on multi-source data fusion analysis, and the method comprises the steps: carrying out the standardization processing and missing value filling processing of multi-source data, constructing a time window, calculating the correlation between data sources, and generating a current data credibility vector based on historical credibility evaluation data; extracting features by adopting multi-scale decomposition, detecting abnormal points and analyzing abnormal degrees, and constructing an abnormal conduction network to evaluate conduction influence; performing feature importance evaluation and feature fusion; initial weight distribution is calculated, a weight prediction model is constructed, and consistency optimization is carried out on a weight matrix; performing hierarchical prediction by adopting a multi-level prediction model, and performing collaborative integration based on a prediction result and an optimized weight matrix; and calculating a prediction result deviation and carrying out optimization adjustment, and outputting a final prediction result. According to the invention, accurate prediction of the crop growth state is realized, and the reliability and practicability of the prediction result are improved.

The invention provides a crop yield prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining continuous historical meteorological data, soil data, crop planting data and remote sensing image data of a designated region for many years, and carrying out the alignment according to a time sequence, and constructing a multi-dimensional spatio-temporal data cube; a fusion prediction model is trained, the fusion prediction model is used for extracting growth feature vectors in the crop growth and development process and carrying out yield prediction, and model parameters of the fusion prediction model are dynamically adjusted through an adaptive learning rate optimization algorithm; the method comprises the following steps: acquiring real-time meteorological data, soil data and remote sensing image data of a specified area, inputting the data into a pre-constructed fusion prediction model, introducing an agricultural knowledge graph into the fusion prediction model to restrain and correct a prediction result of the fusion prediction model, and reasoning to obtain crop growth and estimated yield of each grid of the specified area. And performing spatial interpolation on the estimated yield of each grid to generate a yield distribution diagram of the specified area.

The invention provides a crop whole growth cycle identification method and system based on deep learning, and the method comprises the steps: obtaining a multispectral image sequence of a target crop in a continuous time period through an image collection device, and carrying out the standardized illumination adjustment processing of each image frame in the multispectral image sequence, obtaining a standard illumination image set corresponding to the multispectral image sequence; carrying out crop region segmentation processing on each image, extracting a local feature region related to a target crop, generating a standardized crop image set containing the local feature region, inputting the standardized crop image set into a pre-trained multi-task deep learning model, extracting combined features through parallel convolution branches, and carrying out image segmentation processing on the combined features; and executing cross-stage correlation analysis in the full connection layer, outputting a multi-task classification result corresponding to the target crop growth cycle, and generating a stage identification report corresponding to the target crop full growth cycle. According to the invention, the crop whole growth cycle identification precision and the agricultural management efficiency can be improved.

The invention discloses an intelligent crop growth prediction and optimization method based on multi-source data fusion, and belongs to the technical field of crop analysis, and the method specifically comprises the steps: obtaining space remote sensing data, ground sensor data and meteorological data, and carrying out the space-time alignment processing to generate fusion data representation; inputting the fused data representation into a prediction model, and outputting a crop future growth state sequence and a yield prediction result by coupling a crop growth mechanism and a dynamic environment response relationship; based on a yield prediction result, simulating long-term effects of different management strategies in a virtual growth environment, and screening out an irrigation scheme and a fertilization scheme with the optimal target function; transmitting the irrigation scheme and the fertilization scheme to a farmland execution terminal; collecting crop state feedback data and environmental parameter feedback data, and updating internal parameters of the prediction model based on the feedback data; according to the invention, crop growth management is converted from passive response to active regulation and control, and a whole-process intelligent solution is provided for precision agriculture.

The invention relates to the technical field of electric digital data processing, in particular to an agricultural planting optimization system and method based on big data analysis. The method comprises the following steps: acquiring agricultural planting environment parameter data, and performing preprocessing and standardization to obtain a standardized environment feature vector; acquiring image, sound and smell data of crops; performing deep feature extraction on the image, sound and smell data to generate a multi-modal feature representation matrix; and performing multi-modal data fusion based on the standardized environment feature vector and the multi-modal feature representation matrix to obtain a comprehensive feature space. According to the invention, through multi-modal feature fusion and digital twinborn modeling, the precision and real-time performance of crop growth risk and pest and disease prediction are improved, and optimal regulation and control and efficient resource utilization of precision agriculture are realized.

The invention relates to the technical field of crop production, in particular to a big data-based crop yield prediction and synergistic management system, which comprises a data acquisition module, a data processing module, a multi-scale crop growth prediction module and a plant stomata dynamic adjustment and transpiration monitoring module, the data acquisition module acquires multi-dimensional agricultural data; the data processing module receives and preprocesses the collected multi-dimensional agricultural data; the multi-scale crop growth prediction module predicts growth trends of crops in different time dimensions; and the plant stomata dynamic adjustment and transpiration monitoring module analyzes and predicts the transpiration rate of the crops, and adjusts the irrigation amount in real time in combination with the prediction result of the growth trend of the crops. According to the invention, the sustainability of water resource utilization is improved, the waste of agricultural resources is reduced, and finally the benefits of agricultural production and the sustainability of the environment are improved.

The invention relates to the technical field of intelligent decision making, in particular to an accurate fertilization intelligent decision making method and system based on multi-source remote sensing, and the method comprises the steps: obtaining a multi-source remote sensing image which comprises an unmanned aerial vehicle image and a stationary satellite image; acquiring soil nutrient data and fertility indexes of the sample field parcels; acquiring meteorological data and soil data; carrying out image preprocessing and feature extraction; constructing a crop identification and classification model; carrying out crop type identification and block division on the multi-source remote sensing image; extracting crop growth features based on the multi-source remote sensing image and the result of the crop identification and classification model; based on the crop growth characteristics and the soil nutrient data, analyzing the relationship between the crop growth stage and the nutrient demand; constructing a nutrient utilization efficiency maximization model; based on a nutrient utilization efficiency maximization model, generating a precise fertilization decision scheme; and outputting a precise fertilization decision scheme, and carrying out precise analysis and intelligent decision making on the growth state and nutrient demand of farmland crops.

The invention relates to the technical field of agricultural irrigation, and discloses a regional crop rolling correction intelligent irrigation decision-making method, which comprises the following steps of S1, collecting basic data and dividing an irrigation decision-making region; s2, crop growth model parameter sensitivity analysis; s3, localizing parameters of the crop growth model; s4, a regional space long-term irrigation decision scheme is formulated; s5, establishing a farmland information database based on remote sensing and Internet of Things technologies; and S6, constructing a regional irrigation system rolling correction system. The invention provides a regional crop rolling correction intelligent irrigation decision-making method. The method comprises the following steps: analyzing a mutual relation and an evolution trend of each element of an irrigation decision-making model; a regional grain and cotton crop irrigation system based on crop water shortage information perception-process recognition-efficient decision is constructed, and irrigation decisions are dynamically adjusted in real time according to future climate changes and field management changes, so that the balance between the water resource utilization efficiency and the crop yield is achieved.

The invention provides a crop monitoring system and method based on multispectral remote sensing and deep learning, and the system comprises a data preprocessing module which is used for carrying out the data preprocessing of a multispectral remote sensing image, and generating a standard reflectivity data set; the feature extraction module is used for extracting a high-dimensional spectral feature vector from the standard reflectivity data set through a multi-scale convolutional neural network; the time sequence dynamic analysis module is used for performing time sequence correlation analysis on the high-dimensional spectral feature vector through a long short-term memory network to generate a weighted time sequence feature vector; the physiological parameter quantification module is used for mapping the weighted time sequence feature vectors into quantitative indexes of crop physiological parameters; and the monitoring result generation module is used for performing dynamic deduction according to the quantitative index and generating dynamic trend prediction data of the crop growth state. The system can dynamically sense the growth stage characteristics of crops and adaptively adjust the spectral feature extraction strategy, thereby improving the crop monitoring precision in a complex agricultural environment.

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention discloses a microbial compound bacterial fertilizer for improving saline alkali soil and increasing the acting yield. A preparation method of the microbial compound bacterial fertilizer comprises the following steps: preparing a microbial agent containing phosphate-solubilizing bacteria, potassium-solubilizing bacteria, nitrogen-fixing bacteria, bacillus subtilis and actinomycetes; the preparation method comprises the following steps: preparing a sodium alginate solution, adding nano-hydroxyapatite and a microbial agent, uniformly mixing, dropwise adding a calcium chloride solution, and embedding an inner layer to obtain microbial embedded particles; uniformly mixing biochar, humic acid and attapulgite, taking a 2-3% HPMC solution as a binder, and carrying out middle-layer coating, so as to obtain prefabricated microbial fertilizer particles; and coating the chitosan-sodium alginate-nano ZnO outer layer to obtain the microbial compound bacterial fertilizer. The microbial compound bacterial fertilizer disclosed by the invention adopts a multi-layer and multifunctional collaborative design concept, realizes efficient protection and slow release of functional microorganisms in a saline-alkali environment, remarkably improves the physicochemical properties of soil and promotes the growth of crops.

The invention relates to the technical field of data decision, in particular to an analysis decision method for crop growth monitoring data. The method comprises the following steps: collecting crop phenotypic characteristic data through a multi-source sensing array; dynamically dividing crop growth response stages according to phenotypic characteristics; constructing a growth situation prediction model based on the crop growth response stage; inputting the phenotypic characteristic data into a growth situation prediction model, simulating a crop growth process through a digital twin engine, and generating growth situation evolution data; extracting physiological anomaly characteristics of the growth situation evolution data, and generating stress response signals; obtaining a canopy three-dimensional phenotype map based on the stress response signal; positioning vascular bundle nodes of the canopy three-dimensional phenotype map through morphological topology analysis; according to the invention, through multi-source data acquisition, dynamic growth stage division, accurate model prediction, three-dimensional phenotype monitoring and self-correction decision optimization, the accuracy of analysis decision for crop growth monitoring data is improved.

The invention relates to the field of crop monitoring and analysis, in particular to a crop growth monitoring method and system based on multi-sensor fusion. The method comprises the following steps: collecting multi-dimensional crop growth environment monitoring parameters, carrying out environment multi-dimensional perception fitting, and constructing a growth environment multi-dimensional perception model; acquiring a crop full-cycle growth monitoring image and a crop physiological state original data set; performing growth state deep evolution according to the crop physiological state original data set, and constructing a multi-mode growth state evolution graph; and carrying out adaptive region filtering and denoising on the crop full-cycle growth monitoring image flow, and carrying out crop three-dimensional morphological evolution analysis to obtain a crop three-dimensional morphological evolution rule. According to the invention, the virtual simulation model is perfected by collecting real-time parameters, intelligent analysis and real-time decision making are carried out, and the stability and accuracy of crop monitoring analysis are improved.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention relates to the technical field of water and fertilizer integration, and provides an intelligent water and fertilizer integration optimization method and device based on the Internet of Things technology, and the method comprises the following three core stages and interaction mechanisms: 1, a data perception and edge processing stage; 2, an intelligent decision-making and dynamic optimization stage; and step 3, an accurate execution and closed-loop control stage. Soil parameters, meteorological parameters and crop physiological parameters are comprehensively collected through a multi-modal sensor network, the data transmission efficiency is improved by using an LoRa / NB-IoT hybrid transmission protocol, data cleaning and standardization processing are completed in combination with edge computing nodes, and the data fusion depth and quality are improved; a multi-objective optimization function is constructed based on a crop growth model, a water and fertilizer regulation and control strategy is dynamically generated through a machine learning algorithm, and collaborative optimization of water saving, yield increasing and environmental protection is achieved.

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention relates to the technical field of ecological monitoring, in particular to an agricultural ecological environment monitoring method and system based on digital twinning, and the method comprises the following steps: obtaining monitoring data in a farmland, constructing a mapping relation, carrying out time sequence arrangement, merging multi-source data, calling a virtual grid to update a three-dimensional model, generating a mapping result, and analyzing a crop growth image. And tracking environmental changes, comparing ecological stability intervals, extracting risk trends, calling the three-dimensional model, and generating a risk early warning interface layer. According to the method, the continuous data of the agricultural ecological environment is acquired, and the mapping relation is constructed to perform time sequence arrangement on the information, so that the response speed and the processing precision of the change of the agricultural ecological environment are improved, the monitoring and management of the crop growth environment are optimized, and the environment change can be visually displayed through the expression of the real-time updated three-dimensional environment model; by comparing humidity and temperature changes and screening space segments, careful monitoring and accurate early warning of environmental changes are enhanced.

The invention belongs to the technical field of intelligent agricultural data analysis, and relates to a crop identification and growth process prediction method and system based on multi-modal data. The method comprises the following steps: collecting growth process images and multi-modal environment data of crops in each growth stage; constructing a crop feature data set; constructing a sample data set; training to obtain a crop type identification model; obtaining a crop type identification result; generating a fusion feature vector; performing data enhancement processing; constructing a long-short-term memory network for training to obtain a growth stage prediction model; and predicting the growth stage of the crops. Crop type identification is performed by constructing a deep neural network model, feature fusion is performed on crop growth process image features and multi-modal environment data, crop growth stage prediction is performed by using a long-short-term memory network, fertilization, irrigation and pest control measures are optimized in combination with the multi-modal environment data, and the crop growth stage prediction accuracy is improved. The crop yield and quality are improved.

The invention discloses a crop sowing parameter control optimization method applied to multi-target precise fertilization, and relates to the technical field of agricultural crop sowing, and the method comprises the following steps: collecting a soil sample, carrying out spectrum scanning and analysis, combining with known soil nutrient data, building a soil nutrient content prediction model, and calculating a soil nutrient content prediction model; and combining spectral data to predict the nutrient content in the soil. According to the invention, through accurate evaluation of soil nutrient conditions and deep analysis of crop growth demands, fertilization and sowing parameters are optimized, so that crops can obtain required nutrients in the growth process, the optimized sowing depth, row spacing and plant spacing can effectively promote the development of crop roots, the absorption ability of the crops to moisture and nutrients is enhanced, and the quality of the crops is improved. And through real-time monitoring and adjustment of the intelligent fertilization equipment, the fertilization amount can be dynamically adjusted according to actual demands of crops, and the common problem of excessive or insufficient fertilizer in a traditional fertilization method is avoided.

The invention discloses a drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring, and relates to the technical field of irrigation regulation and control, the method comprises the following steps: monitoring a multi-point sensor arranged in a target area to obtain target monitoring data; performing actual demand analysis on the target crop to obtain actual demand data; analyzing the target soil permeability of the target area to obtain a target irrigation period; and performing irrigation processing according to the actual demand data and the target irrigation period to obtain irrigation data. The technical problems that in the prior art, real-time requirements of crops cannot be accurately obtained, irrigation is difficult to implement according to soil characteristics, and the agricultural production efficiency and sustainability are limited are solved. The effects that multiple types of monitoring data are obtained by arranging the multi-point sensors in the target area, precise irrigation is achieved, the crop growth environment is optimized, good physicochemical properties of soil are maintained, and the technical effects of high efficiency and sustainability of agricultural production are improved are achieved.

The invention relates to the technical field of agricultural irrigation management, in particular to a method and system for estimating the agricultural irrigation water consumption of an irrigation area. The method comprises the following steps: collecting soil, weather and crop data through a sensor and a detector, processing the collected data through an edge computing node, and then selecting an optimal transmission path to transmit the data through an Internet of Things gateway based on an https protocol; an irrigation area agricultural irrigation water consumption prediction model is constructed based on an intelligent algorithm of deep learning, the model stores, preprocesses and analyzes received data, then modeling and prediction are carried out on soil humidity, meteorological conditions and crop growth conditions, and finally an irrigation water consumption estimation result is obtained; and updating the irrigation area agricultural irrigation water consumption prediction model to ensure the accuracy and timeliness of model prediction. According to the method, through an advanced sensor technology, the Internet of Things, big data analysis and a machine learning algorithm, accurate estimation and optimal management of the agricultural irrigation water consumption of the irrigation area can be realized.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention discloses a crop growth prediction system and method based on an agricultural unmanned aerial vehicle remote sensing technology, and relates to the technical field of agricultural information and intelligent monitoring, the system uses an unmanned aerial vehicle remote sensing module and a ground Internet of Things sensor module to collect multi-source data of banana planting, fish pond breeding and hog house breeding, and the multi-source data is used as a data expression recording carrier. The edge computing node module preprocesses data, and the cloud intelligent platform module is internally provided with a multi-modal data fusion model, a growth prediction model and the like as processing record carriers to realize data depth analysis and prediction. And realizing automatic control of the production process according to the data through block chain evidence storage and the intelligent contract. And constructing a disease prevention and control model by using the hyperspectral image of the unmanned aerial vehicle and related data. According to the invention, the collection, processing and application capabilities of agricultural production data are improved, accurate prediction and intelligent regulation are realized, the resource utilization efficiency is improved, the disease loss is reduced, and the agricultural intelligent development is promoted.

The invention relates to the field of saline-alkali soil water-fertilizer regulation, in particular to a saline-alkali soil soybean water-fertilizer-salt regulation dynamic decision-making method and system. A saline-alkali soil soybean water, fertilizer and salt regulation dynamic decision-making system comprises a region division module, a data monitoring module, an irrigation initial decision-making module, a fertilization initial decision-making module, an irrigation decision-making acquisition module and a fertilization decision-making acquisition module. By integrating an advanced sensor technology and an intelligent decision-making model, key parameters are accurately monitored, and the total amount of irrigation and fertilization required in a specific time window in the future is calculated in combination with crop growth stages and historical water and fertilizer records; meanwhile, the irrigation and fertilization frequency can be dynamically adjusted according to soil and environment data monitored in real time, waste of water resources and fertilizer is avoided, the possible soil salinization problem is solved, soil health can be maintained, and crop growth is promoted.

The invention discloses an intelligent sensing algorithm and system for crop growth vigor, computer equipment and a medium, and the algorithm comprises the following steps: 1, carrying out the image collection of a farmland, and collecting the spectral data of different wavebands in the farmland; 2, preprocessing the collected images and spectral data, and extracting multi-dimensional feature information of the crops; 3, fusing the extracted multi-dimensional feature information of the crops to obtain comprehensive and accurate crop growth feature information; 4, performing data analysis on the crop growth feature information through deep learning, and constructing an intelligent decision model; through the implementation of the invention, the characteristics and beneficial effects of multi-information fusion, high-precision perception, cost reduction, quality improvement, wide application and the like are realized through the crop growth vigor intelligent perception algorithm, and the method plays an important role in improvement of agricultural production and agricultural product quality.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention discloses a strip-shaped compound planting mode water and fertilizer zoning management method and system, and relates to the technical field of modern agriculture, and the method comprises the following steps: installing a sensor network in a field, collecting data through a soil moisture sensor, an intelligent water meter and an unmanned aerial vehicle multispectral camera, threshold values are set according to the water demand and the fertilizer demand in the crop growth cycle to judge fertigation, crop demands are predicted by applying multiple linear regression and a random forest algorithm, a fertigation plan is optimized, the growth trend is analyzed, the yield potential is evaluated, the influence of the irrigation level is researched, and an irrigation strategy adjustment suggestion is provided. The system collects field data through an integrated sensor network, accurately sets an irrigation and fertilization threshold value, predicts crop demands by using an advanced algorithm, optimizes a water and fertilizer management plan, effectively analyzes a growth trend and evaluates yield potential, provides a scientific basis for irrigation strategy adjustment, and significantly improves modern agricultural production efficiency and resource utilization efficiency.

The invention provides an intelligent agriculture method and system based on the Internet of Things and artificial intelligence, and the method comprises the steps: collecting and selecting environment parameters and crop growth state data in a target greenhouse in real time, obtaining high-precision environment parameters and crop growth state data, carrying out the scale conversion and integration of the high-precision data, and obtaining a high-precision environment parameter and crop growth state data; the method comprises the steps of obtaining first structured data, structuring historical experimental data into a sensor-like data format to obtain second structured data, analyzing and outputting the confidence coefficient of the first structured data, and performing crop growth prediction on the first structured data. And obtaining a crop growth state prediction value, a crop yield prediction result and a crop growth quality grade, and generating an optimal greenhouse environment regulation and control strategy and a farming management decision suggestion through an intelligent optimization algorithm. Based on an Internet of Things system and artificial intelligence, the accuracy of collected crop related data is ensured, and intelligent greenhouse environment regulation and control are carried out.