3436results about "Fertilising methods" 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 invention relates to the technical field of farmland nutrient management, and discloses an organic fertilizer application method and system for dynamic management of farmland nutrients, and the method comprises the steps: obtaining and standardizing original data of a farmland environment, extracting features to generate farmland feature vectors, and completing nutrient demand recognition and fertilization type analysis; integrating the data to generate an enhanced farmland matrix, performing multi-dimensional nutrient analysis, calculating application necessity and an environmental influence value, and generating decision data after verification; constructing an enhanced fertilizer feature space based on an organic fertilizer library and the decision data, completing fertilizer matching, proportioning and application path optimization, and outputting pre-inspection report data; and collecting application data to carry out anomaly detection and adaptive learning. The system comprises a processor and a memory, and instructions stored in the memory can implement the method. According to the invention, through multi-dimensional data integration and intelligent decision making, the precision and scientificity of farmland nutrient management are improved, and the system is suitable for precise fertilization requirements of modern agriculture.

An intelligent fertilization and water supply method and system. The method comprises the following steps: step a, comprehensive soil monitoring, involving: comprehensively monitoring various soil indexes by means of a plurality of sensors, so as to obtain real-time data of the soil indexes; b, consideration of crop-specific requirements, involving: for the varieties and growth stages of different crops, thoroughly studying the unique moisture and nutrient requirements of the crops, so as to ensure the rationality and accuracy of a fertilization amount; c, comprehensive consideration of environmental factors, involving: collecting data of surrounding environmental factors; d, utilization of an intelligent irrigation mode, involving: by using a cyclic irrigation mode, an intermittent irrigation mode, etc., in combination with the water requirement pattern of each crop and a change in soil moisture, realizing efficient water utilization; e, implementation of a dynamic fertilization plan, involving: on the basis of a change in a crop growth condition and in soil fertility, dynamically adjusting fertilization time, frequency and dosage, so as to ensure that the nutrient supply matches the requirements of each crop; and f, implementation of a precise fertilization technique, involving: precisely delivering fertilizer to an absorption part of each crop, so as to improve the efficiency of nutrient utilization.

The invention relates to the technical field of intelligent control, in particular to a monopotassium phosphate intelligent control method and system.The method comprises the following steps that rhizosphere phosphorus and potassium concentration changes are obtained, whether the rhizosphere phosphorus and potassium concentration changes are in a critical rate interval or not is judged, an effective absorption time period identifier is generated, and an organ amplification ratio is analyzed to judge a cooperation period; and extracting a stalk concentration ratio, comparing the stalk concentration ratio with a reference interval to generate an application inhibition identifier, fusing information to judge a fertilization window, adjusting the concentration according to the root hair amplification and the water consumption rate, and generating a control instruction set. According to the method, the effective time period of phosphorus and potassium nutrient absorption of the crops is determined through crop rhizosphere phosphorus and potassium ion concentration difference calculation and average treatment, the objective interval of crop nutrient absorption rate indexes is defined, blind fertilization is avoided, the pertinence and accuracy of nutrient absorption and utilization of the crops are enhanced, the excessive application state of nutrient elements is dynamically identified, and the crop quality is improved. The risk of excessive nutrients caused by nutrient enrichment of crops is prevented, and resource waste and environmental burden are effectively reduced.

The invention discloses a crop environment quantitative evaluation and decision making system based on growth stage self-adaption, and relates to the technical field of agricultural intelligent decision making. Acquiring an environment original data sequence of field multi-type sensors through a data acquisition module; the dynamic identification module analyzes the sequence by using a preset crop growth stage discrimination model, identifies the current growth stage and outputs a corresponding key environment parameter weight template; the feature fusion module performs weighted fusion on the original data according to the template to generate an environment feature vector with stage adaptability; the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model; and the decision trigger judgment module calls a corresponding preset dynamic threshold interval according to the growth stage for comparison, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates. According to the invention, stage self-adaptive intelligentization of crop growth monitoring and decision making is realized, and the accuracy of environment state evaluation and the timeliness of management decision making are improved.

The invention discloses a regional scale crop nitrogen dynamic regulation and control method and device. The method comprises the following steps of obtaining multi-source data and constructing a space-time database; selecting a crop growth model, and calibrating and verifying the crop growth model based on the space-time database to obtain the crop growth model and generate a localized model parameter set; dividing a target area into space units, inputting soil data, meteorological data and a localized model parameter set of each space unit to a crop growth model, simulating crop growth processes of different nitrogen gradients, extracting optimal biomass and critical nitrogen concentration data point pairs of each space unit, and performing fitting to generate a spatialized pCNDC parameter map layer; inverting an actual biomass and nitrogen concentration spatial distribution map based on remote sensing data, calculating critical nitrogen concentration and nitrogen nutrition index of each spatial unit in combination with a pCNDC parameter map layer, and generating a spatial distribution map; when the nitrogen nutrition index is lower than a preset threshold value, the nitrogen deficiency amount is calculated, and a variable fertilization prescription map is generated.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention provides a special charcoal-based slow-release fertilizer for sandy soil as well as a preparation method and application thereof, and belongs to the technical field of fertilizers. The special charcoal-based slow-release fertilizer for the sandy soil is prepared from amino modified biomass charcoal, urea, urea formaldehyde, monoammonium phosphate, potassium sulfate, polyglutamic acid, fulvic acid, clay, attapulgite, a compound microbial agent and sodium alginate. The compound microbial agent is prepared from halomonas (ACCC 11681), bacillus subtilis (ACCC 60429) and bacillus mucilaginosus (ACCC 10094), and the compound microbial agent is prepared from bacillus subtilis (ACCC 60429) and bacillus mucilaginosus (ACCC 10094). The invention further provides a preparation method and application of the special charcoal-based slow-release fertilizer for the sandy soil. The special charcoal-based slow-release fertilizer for the sandy soil can remarkably improve the water and fertilizer retention capacity of the sandy soil, improve the farmland productivity of the sandy soil, save fertilizer and water input and improve the yield and quality of crops.

The invention discloses a traditional Chinese medicinal material standard base intelligent irrigation and fertilization method based on the Internet of Things and a decision making system, and relates to the technical field of agricultural informatization. Through multi-source data acquisition and intelligent analysis, the limitation of an existing irrigation and fertilization method is solved, accurate decision making and resource optimization are realized, comprehensive data are acquired by utilizing a soil moisture content sensor, a meteorological station and plant image acquisition equipment, and by combining principal component analysis, a long-short-term memory neural network and a block chain technology, the intelligent irrigation and fertilization method is realized. According to the system, the water and fertilizer requirements are accurately predicted, an irrigation and fertilization scheme is optimized, data credibility and traceability are ensured, meanwhile, real-time control and collaborative operation of equipment are achieved through edge calculation and a wireless sensor network, the resource utilization efficiency is improved, the production cost is reduced, and scientificity and sustainability of traditional Chinese medicine planting are remarkably improved.

The invention relates to the technical field of intelligent control, and discloses a water-saving fertilization variable control method suitable for sandy soil, and the method comprises the steps: obtaining collection data based on a sensor; and establishing and updating an infiltration-storage-leaching loss prediction model of the sandy soil according to the collected data, performing state estimation on soil moisture and nutrient states, and calculating root layer moisture deviation, fertilizer concentration deviation and deep seepage risk. And executing model predictive control in the rolling time domain to obtain a control result. And executing water-saving fertilization operation according to the control result. In the execution process, the soil volume moisture content, the soil water potential, the soil conductivity and shallow layer leakage observation data are fused, the prediction model is corrected, the control result is dynamically adjusted according to the correction result, and when the environment wind speed exceeds a set threshold value, the spray irrigation parameters are corrected. The fine level of control is improved, deep layer leakage can be inhibited under the sandy soil condition, water and fertilizer balance of a root layer is guaranteed, and external disturbance is coped with.

The invention relates to the technical field of water and fertilizer application evaluation, and particularly discloses an intelligent evaluation method for water and fertilizer utilization efficiency and an operation system, and the method comprises the steps: obtaining a crop image of a crop corresponding to a fertilization parameter; identifying the crop image, outputting a growth evaluation report, and constructing a mapping relation from the fertilization parameters to the growth evaluation report for evaluating the fertilization parameters; fertilization parameters are recorded, meanwhile, crop images are obtained through a visual monitoring point, the crop images are recognized, the growth conditions before and after fertilization are determined, then the growth difference before and after fertilization is determined, and the mapping relation from the fertilization parameters to the growth difference is constructed; the growth difference of the fertilization parameters can be predicted before the fertilization behavior, and the prediction accuracy is high.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention discloses a crop nitrogen fertilizer management system and method based on multi-agent reinforcement learning, and belongs to the technical field of intelligent agriculture. Comprising the following steps: collecting and preprocessing multi-source data of a target area, calibrating a crop growth-nitrogen cycle model based on the multi-source data, and constructing a dynamic simulation environment; constructing a nitrogen fertilizer application strategy model and a multi-target award function, and performing agent reinforcement learning training by adopting a centralized training-decentralized execution architecture; introducing a large language model, and updating a strategy network and / or a value network of each agent in a training process according to a reward adjustment signal and a decision constraint; when the index fluctuation ratio in the continuous evaluation period is smaller than a preset threshold value, it is judged that the nitrogen fertilizer application strategy model is converged, and an optimal nitrogen fertilizer application strategy model is obtained; and generating a nitrogen fertilizer application scheme based on the optimal nitrogen fertilizer application strategy model in combination with the real-time state data, and generating a natural language interpretation and risk assessment report based on a large language model.

The invention is suitable for the field of crop fertilization control, and provides a fertilization control method and system based on corn protective farming, and the method comprises the steps: synchronously collecting time-space multi-dimensional data of a target planting region, and constructing a multi-modal data cube; generating a seed fertilizer ratio decision vector; according to the seed-fertilizer ratio decision vector, controlling the multi-channel ratio of mechanical precision seed-fertilizer simultaneous sowing equipment, so as to realize the dynamic ratio and variable fertilization of the seed fertilizer; a multi-spectral image and soil moisture content data are collected through an unmanned aerial vehicle group, and a space-time continuous crop growth monitoring network is constructed; dynamically adjusting the topdressing amount and the water and fertilizer concentration of the unmanned aerial vehicle group based on the growth monitoring network data; according to the method, by constructing a multi-modal data cube, the dynamic proportioning and variable fertilization of seeds and fertilizers are realized, the proportioning precision is improved by utilizing fuzzy PID and machine vision, a space-time monitoring network is constructed by virtue of an unmanned aerial vehicle group, the dynamic adjustment of topdressing is realized, and a data-driven closed-loop fertilization system is formed.

The invention provides a multi-dimensional data fusion soil fertilizer application system and method, and relates to the technical field of soil fertilizer application, and the system comprises a data collection module which is used for collecting multi-source original data including soil, weather, crops and fertilizer in real time; the data preprocessing module is used for preprocessing the multi-source original data; the data fusion module is used for generating a comprehensive index reflecting the soil fertility state and the crop demand; the fertilization decision module is used for generating an optimal fertilization scheme; the execution control module is used for controlling the variable fertilizer applicator and the drip irrigation equipment to execute precise fertilization operation; and the effect monitoring and feedback module is used for evaluating the decision effect and optimizing the nutrient leaching model. According to the invention, through multi-objective optimization based on the nutrient leaching model and the genetic algorithm, the fertilizer cost, the environmental risk and the agricultural machinery operation constraint are considered while the fertilizer requirement rule of crops is met, and the optimal fertilization scheme considering both the economical efficiency and the ecological property is output.

Disclosed is a rice and wheat nitrogen nutrition multispectral diagnosis method for precise fertilization by unmanned aerial vehicles (UAVs), including: step 1, acquiring multispectral UAV remote sensing images and collecting agronomic parameters of rice and wheat, and establishing a nitrogen nutrition spectral diagnosis model; and step 2, calculating a critical nitrogen concentration in aboveground parts of rice and wheat based on a critical nitrogen concentration dilution model for amount of dry matter, and further obtaining a critical nitrogen uptake; calculating an actual nitrogen uptake in the aboveground parts of rice and wheat based on the nitrogen nutrition spectral diagnosis model; and obtaining a recommended nitrogen topdressing amount for rice and wheat during corresponding stages based on the critical nitrogen uptake and the actual nitrogen uptake. The present disclosure ensures stable yields of rice and wheat while reducing input of chemical fertilizers and labor, presenting a promising application prospect.

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 relates to a field water and fertilizer intelligent control method based on the Internet of Things, and the method comprises the steps: carrying out the crop type recognition and growth stage recognition through collecting crop images, soil water and fertilizer data and meteorological prediction data, and generating an initial water and fertilizer control scheme of a current time node; a soil water and fertilizer prediction model is created to predict the soil water and fertilizer change trend in a specified time period, the initial water and fertilizer control scheme of the current time node is adjusted according to the predicted soil water and fertilizer change trend, growth cycle characteristics corresponding to crop types are obtained based on big data, and a dynamic water and fertilizer control scheme is formulated. A water and fertilizer adjusting control scheme is executed at a current time node, a dynamic water and fertilizer control scheme is executed at a specified time node, the field water and fertilizer intelligent control method integrating Internet of Things sensing, intelligent identification, trend prediction, dynamic planning and closed-loop feedback is realized, the whole process intelligence from data acquisition to scheme execution is realized, and the water and fertilizer utilization efficiency is improved. The agricultural production cost is reduced.

A method of creating a wetland in a remote arid location includes identifying or establishing a channel of flowing water at the arid location, and installing therein a mat containing seeds, and / or seedlings, and a super absorbent polymer (SAP). Water is applied to the SAP, such that both edible and dam-constructing vegetation grows and matures from the mat. Beavers are then dropped into the arid location by parachute in containers that open upon landing. The beavers eat the edible vegetation and use the dam-constructing vegetation to construct at least one dam across the channel of flowing water, thereby forming a wetland that further encourages both the vegetation and the beavers to propagate. A perforated, transparent or translucent cover sheet can be placed on or suspended above the mat. A water barrier can be placed below the mat. The wetland vegetation cools and humidifies the environment by transpiration of water.

The invention relates to the technical field of intelligent fertilization, and discloses an agricultural precise fertilization method which comprises the following steps: step 1, synchronously collecting multi-modal real-time data of single wheat plants, and automatically identifying the current growth stage of the wheat plants; 2, searching historical fertility data of the position of the wheat; 3, fusing the real-time data, the historical fertility data and the growth stage label to generate an enhanced fusion feature vector; step 4, selecting a corresponding causal diagram model according to the current growth stage, and estimating an average processing effect of different fertilization schemes based on the enhanced fusion feature vector; and 5, inputting the enhanced fusion feature vector, the average processing effect estimated value and the growth stage label into the prediction model and the adjustment model. According to the system and the method, the fertilization accuracy and the crop response effect are improved, continuous optimization of the system is realized through a closed-loop intelligent decision framework, and a landing and evolvable efficient fertilization solution is provided for intelligent agriculture.

The invention discloses a head intelligent irrigation method and system based on mobile water and fertilizer integration, and relates to the technical field of intelligent irrigation, and the method comprises the steps: collecting meteorological data, soil data and growth period codes corresponding to crops, so as to generate a farmland water and fertilizer demand data set; dividing the target farmland area into n sub-areas, performing multi-parameter fusion matching processing on the water and fertilizer demand data set to obtain a feature vector, and inputting the feature vector into a water and fertilizer demand prediction model to obtain the required irrigation amount and the fertilization concentration of each sub-area of the farmland; generating a scheduling instruction set according to the required irrigation amount and the fertilization concentration of each subarea of the farmland, performing water source location movement and water source extraction based on each subset in the scheduling instruction set to obtain an original water body, performing three-stage filtration on the original water body to obtain a target clear water body, and performing mixed optimization treatment to obtain a water-fertilizer mixed solution; and injecting the water and fertilizer mixed solution into the drip irrigation pipe network and executing water and fertilizer irrigation of each sub-region of the farmland.

The invention belongs to the field of biological organic fertilizers, and discloses a special organic selenium-rich fertilizer for grapes as well as a preparation method and application thereof. The fertilizer is prepared from the following raw materials in percentage by mass: 0.05-0.2% of a selenium complex, 2-15% of a stabilizer, 15-25% of a microbial fermentation product, 0.5-2.0% of a natural plant extract and the balance of auxiliary materials, totaling 100%. The microbial fermentation product is microbial fermentation liquid obtained by fermenting bacillus tequilensis and cyanobacteria; the auxiliary material is obtained by mixing sodium alginate and maltodextrin; the natural plant extract is obtained by mixing azadirachtin, sophocarpidine, tea tree essential oil and cnidium lactone. Through the synergistic effect of the microbial fermentation product, the natural plant extract and the auxiliary materials, the industrial problem that in the prior art, a selenium fertilizer can only improve the selenium content of grapes, but cannot effectively improve the disease resistance and insect pest resistance of the grapes and slow down selenium element loss is solved, and the double improvement of the grape quality and the selenium content is achieved.

The invention relates to the technical field of selenium-rich white tea production, and discloses an intelligent control system and method for automatic production of selenium-rich white tea. The system comprises a selenium-rich white tea growth environment regulation and control module, a tea growth stage recognition module, a selenium content self-adaptive supply module, a production equipment cooperative control module and a quality closed-loop feedback module. The growth environment regulation and control module collects tea garden multi-region environment parameters and establishes a dynamic monitoring set; the growth stage identification module divides growth stage identifiers in combination with the environment parameters, the bud and leaf morphological image sequence and the growth cycle time sequence data; the selenium content self-adaptive supply module generates a supply scheme and an equipment control instruction set according to the growth stage identifier and the soil selenium content data; the production equipment cooperative control module is linked with related equipment to execute a cooperative operation time sequence control process; and the quality closed-loop feedback module generates a correction amount according to the finished product detection data, and feeds the correction amount back to the growth environment regulation and control module for parameter iterative updating.

The invention discloses a phyllostachys praecox forest fertilizer regulation and control and fertilization method, and relates to the technical field of fertilization regulation and control. A phyllostachys praecox forest fertilizer regulation and fertilization method comprises the following steps: collecting multi-source basic data of a target phyllostachys praecox forest area, identifying a growth stage, identifying an affordable stage, dividing a plurality of management sub-areas, obtaining an optimal fertilization parameter set, forming a differentiated area fertilization scheme, and performing fertilization target enhancement according to the affordable stage of the target. Fertilization management is carried out through the phyllostachys praecox forest intelligent multi-dimensional fertilization information system, and dynamic optimization of a fertilization scheme is carried out in combination with the land shortage degree of a target area. According to the method, the phyllostachys praecox forest is divided into a juvenile stage, a developmental stage, a mature stage and a felling stage by introducing an identification mechanism of an economic applicable stage and combining health data and historical yield data of the phyllostachys praecox forest, operation targets of different stages are determined, target enhancement is performed on a fertilization strategy on the basis, and the fertilizer utilization efficiency and output value are remarkably improved.

The invention discloses a water and fertilizer management method for transplanted high-yield indica type two-line hybrid rice, and belongs to the technical field of rice cultivation. Comprising the following steps: (1) applying citric acid chelated calcium magnesium phosphate fertilizer to soil before transplanting; (2) applying coated controlled-release urea and a silicon-zinc fertilizer on the third day after transplanting; (3) when the tiller number reaches 2650,000-2750,000 plants / hm < 2 >, starting intermittent light field drying for three times, and deeply applying potassium magnesium sulphate before field drying; (4) laterally and deeply applying urea and a boron-humic acid chelate when tips of two inverted leaves are exposed; (5) spraying an aqueous solution in a full heading stage, wherein the aqueous solution comprises monopotassium phosphate, chelated zinc and brassinolide. Through staged precise fertilization, special chelating / controlled release fertilizer and water and fertilizer-field drying cooperation, the method has the advantages that the fertilizer utilization rate is increased, the nutritional requirements of rice in each growth period are precisely matched, and water and fertilizer cooperative management is optimized.

The invention discloses a fertilization method and system based on a regional land parcel image, and belongs to the technical field of agricultural fertilization, and the method specifically comprises the steps: collecting a target regional land parcel image through an unmanned aerial vehicle, and after radiation correction and geometric correction, extracting fertilization information containing soil color, vegetation coverage, vegetation index and landform characteristics, integrating to form a fertilization feature data set; according to the fertilization feature data set, dividing regions through a decision tree classification algorithm, identifying and visually displaying by means of a geographic information system; collecting crop variety, planting density and regional division result data, making a fertilization scheme of each region in combination with a fertilization model, and applying fertilization by using automatic equipment; images are collected again after fertilization, the crop growth response evaluation effect is monitored, and the fertilization scheme is adjusted as required, so that the agricultural production benefits are effectively improved, the fertilizer waste is reduced, and the scientificity and rationality of fertilization are optimized.

The invention discloses an intelligent mango fertilizing method. The method comprises the following steps: arranging a drip irrigation pipeline connected with a water and fertilizer mixing device in a drip irrigation area; canopy images including visible light and near-infrared band information of leaves, flower buds, flowers and fruits are periodically collected through a multispectral imaging device; visual features and spectral features are extracted based on an image processing algorithm, the visual features comprise leaf color distribution, form thickness indexes, flower bud density, flower coverage rate and fruit volume ratio, and the spectral features are near-infrared reflectivity ratios of leaf and flower and fruit areas; matching a growth stage standard library according to the characteristic data, judging a flower bud differentiation stage, a flowering stage or a fruit expansion stage, and calling a corresponding stage judgment model to identify a nutrient and moisture deficiency state; and dynamically adjusting the nutrient concentration and dosage of the drip irrigation liquid according to the judgment result. The method is used for accurate water and fertilizer management of mango planting, a fertilization decision is optimized through multi-source data fusion and closed-loop regulation, and the response capability in a complex nutrient demand scene is improved.

The invention belongs to the technical field of unmanned aerial vehicle remote sensing and agriculture combination, and discloses an intelligent decision-making system and method for corn fertilization based on mechanism-data dual-drive fusion. The system comprises a mechanism simulation module, a data preprocessing module, a feature engineering module, a modeling module, a visualization module, a decision support module, a dynamic feedback correction module and a report generation and push module. According to the mechanism-data double-drive fusion-based intelligent decision-making system and method for corn fertilization, a large-area corn field block image is obtained in a short time through an unmanned aerial vehicle multispectral system, and the nitrogen diagnosis efficiency is improved; through a lightweight machine learning agent model, corn canopy leaf nitrogen nutrition parameters are accurately predicted, and a basis is provided for accurate fertilization; the system monitors the nitrogen nutrition status of the corn in real time, and provides possibility for dynamically adjusting a fertilization strategy; the multispectral remote sensing technology can perform nitrogen nutrition diagnosis under the condition of not damaging corn plants, and the corn growth environment is protected.

The invention discloses an irrigation area water and fertilizer coupling regulation and control method based on cross-scale collaborative optimization. The agricultural water and fertilizer management efficiency is improved through a systematic methodology. The method comprises the following steps: firstly, collecting meteorological and agricultural basic data, fitting a water and fertilizer production function by using a least square method, and establishing a relationship between crop yield and water and fertilizer input; on the farmland scale, a moisture balance model and a multi-objective optimization function are constructed, and an optimal irrigation and nitrogen application scheme of each field is solved by adopting NSGA-II and TOPSIS algorithms. In the irrigation domain scale, a water supply scheme with the target of subtracting the water and fertilizer cost from the total output value of crops is optimized, a local water supply boundary is formed, and collaborative iteration of the irrigation domain and the farmland is achieved through a dynamic feedback mechanism. And finally, performing global optimization evaluation on the scale of the irrigation area through a fairness model, and providing decision support for reasonable allocation of water resources. The economic benefit, the resource utilization rate and the environmental influence are comprehensively considered, the scientificity and sustainability of irrigation area water and fertilizer management are effectively improved, and a new thought is provided for intelligent development of modern agriculture.

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.