2149 results about "Crop yield" patented technology

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.

Provided herein are compositions and methods to improve plant resistance to abiotic and biotic stress, thereby improving crop yield. Provided herein are compositions including zinc, copper. Compositions described herein have plant priming activity where the plant's defenses against abiotic and biotic stress are boosted.

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 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 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 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 present invention relates to the technical field of agricultural irrigation. Disclosed are an intelligent irrigation strategy formulation method and system. The method comprises: determining the current irrigation strategy on the basis of a preset irrigation decision-making model and a Q-learning intelligent agent and according to the current agricultural state; on the basis of the current irrigation strategy, performing irrigation simulation on a target growing region to obtain a corresponding irrigation reward value and the next agricultural state, wherein the irrigation reward value is determined on the basis of the crop yield, the crop water demand, and the annual economic cost after irrigation, the current agricultural state, the current irrigation strategy, the irrigation reward value, and the next agricultural state form an empirical four-tuple, and a plurality of empirical four-tuples form an experience pool; and randomly sampling the empirical four-tuple from the experience pool as a training sample to train the Q-learning intelligent agent so as to obtain an optimal irrigation Q-value table which is used for determining a corresponding optimal irrigation strategy on the basis of any agricultural state of the target growing region. The present invention can efficiently obtain an accurate irrigation strategy.

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 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 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.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmospheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmospheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

The present disclosure provides guided microbial remodeling (GMR) methods for the rational improvement of plant-associated microbes to perform plant-beneficial functions. The GMR methods described herein allow for non-intergeneric genetic optimization of key regulatory networks within the microbes, which improve plant-beneficial functions over wild-type microbes but don't have the risks associated with transgenic approaches (e.g., unpredictable gene function, public and regulatory concerns, etc.). The present disclosure also provides remodeled microbes and compositions thereof. The utilization of remodeled microbes and compositions thereof will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived fertilizers.

The invention provides a high-standard farmland water and fertilizer integrated intelligent regulation and control method and system, and the method comprises the steps: obtaining terrain point cloud data and crop canopy water stress data of a high-standard farmland, generating an elevation gradient distribution feature set, and generating an elevation model according to the set; analyzing the crop canopy moisture stress data, and calculating a moisture stress index; performing spatial position matching on the terrain difference parameters and the micro-terrain difference parameters to generate a terrain compensation parameter set; establishing a time sequence optimization correlation model in the 5G edge computing node, and optimizing a pulse irrigation time sequence factor and a flow distribution weight by using the time sequence optimization correlation model according to the terrain compensation parameter set and the historical irrigation record so as to generate a multi-target irrigation control instruction; performing high-standard farmland water and fertilizer integrated intelligent regulation and control according to the multi-target irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the microtopography difference parameter; water and fertilizer are accurately distributed according to needs, and the irrigation uniformity, the resource utilization efficiency and the crop yield are improved.

The invention discloses a saline-alkali soil dynamic precise irrigation method based on a water-salt-carbon flux coupling model, and relates to the technical field of saline-alkali soil improvement and precise irrigation, and the method comprises the following steps: dividing soil into a surface layer, a middle layer and a deep layer, building the coupling model based on a Darcy law, a convection-dispersion equation and a first-order dynamic model, and simulating a water-salt-carbon flux dynamic state; collecting data such as soil water content, conductivity and organic carbon content through a layered sensor network, and inputting the data into the model after cleaning and fusing; the irrigation time, the irrigation amount and the water quality are optimized through a genetic algorithm in combination with the crop growth period requirements; irrigation is executed through an intelligent valve and a variable frequency water pump, and model parameters are adjusted based on real-time monitoring feedback. According to the method, multi-element cooperative regulation and control of saline-alkali soil irrigation are achieved, the soil ecological process can be accurately simulated, the water resource utilization efficiency is improved, the soil environment is improved, and crop yield increase is promoted.

A method for estimating crop yield of an analyzed area, which is a region of interest, according to which imagery data is acquired from one or more remotely sensed sources, using a remote sensing platform or one or more satellites. A remotely sensed LAI of the analyzed area is generated by fusing the acquired imagery data and the sowing date of the analyzed field is detected by processing the imagery data. The detected sowing date and a sowing date window are fed into a crop simulator (e.g., APSIM) and a set of predetermined parameters representing the state of the analyzed area is then generated, and fed into the crop simulator. The crop simulator generates a plurality of LAI simulations, each corresponding to a different combination of parameters. Only LAI simulations in which the simulated LAI best matches the remotely sensed LAI are identified and selected, while omitting all other LAI simulations. Finally, the yield prediction that corresponds to the simulation(s) with the best match, are selected.

The invention discloses an intelligent field management system based on an Internet of Things platform, and relates to the technical field of Internet of Things, and the intelligent field management system comprises a multi-source data fusion and analysis module, a waste recovery and resource utilization module, a knowledge graph and question and answer module, and a self-adaptive decision and regulation module. The multi-source data fusion and analysis module comprises a data acquisition sub-module and a data analysis and preprocessing sub-module; the data acquisition sub-module comprises a multi-source heterogeneous data acquisition unit and a real-time dynamic data acquisition unit; the multi-source heterogeneous data acquisition unit uses a wireless sensing technology, a GPS positioning technology, a sensor calibration algorithm and a data interpolation algorithm to acquire meteorological, soil, crop physiology and biological field data. By arranging the multi-source data fusion and analysis module, the problems that traditional agricultural data is difficult to obtain and not deep in analysis, precision agricultural development is difficult to support, and agricultural resources are wasted and crop yield and quality are unstable due to data quality problems can be solved.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmospheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

The invention discloses a planting optimization method and system based on crop yield estimation in a saline-alkali land area, and relates to the field of crop yield prediction and planting optimization, and the method comprises the steps: data collection and fusion processing; performing dynamic prediction on soil water and salt migration, and realizing water and salt space-time distribution prediction by adopting a space-time cubic modeling self-adaptive model combined with physical correction; crop salt tolerance modeling: establishing a multi-interval salt tolerance model based on physiological response fitting and threshold segmentation, and introducing covariable correction; carrying out migration salt tolerance coupling modeling, introducing a salinity memory factor and a stage sensitivity regulation factor, and dynamically simulating growth response of crops under salinity stress; and crop yield estimation and planting optimization: generating a regional yield prediction and sowing configuration scheme based on coupling model output, and forming an optimization strategy in combination with measures such as irrigation and fertilization. According to the method, model-driven integrated optimization from yield estimation to planting decision of crops in the saline-alkali region is realized, and the method has high precision and wide applicability.

The invention discloses a microbial agent for preventing and treating soil-borne diseases of food crops and a preparation method of the microbial agent, and belongs to the technical field of microorganisms. The microbial inoculum comprises three functional strains, namely pseudomonas guayensis, paenibacillus polymyxa and trichoderma koningiopsis, the three strains are mixed in equal proportion and cooperate with one another, so that the microbial inoculum has a remarkable disease-resistant effect, has an antagonistic effect on various diseases of wheat and corn, can be used for preventing and treating wheat root rot, take-all and banded sclerotial blight as well as corn stem rot and banded sclerotial blight, and has a wide application prospect. Particularly, the inhibition effect on the take-all disease of wheat is optimal. Meanwhile, the coating agent and the auxiliaries are used, so that the acting durability and stability of the microbial inoculum are improved. The microbial agent is matched with a conventional base fertilizer for use, use of chemical bactericides is reduced, growth of common pathogenic bacteria of food crops is effectively inhibited, and the yield of the food crops is increased.

The invention discloses an intelligent farmland irrigation decision-making method based on digital twinning, and the method comprises the following steps: S1, collecting multi-source data of a farmland environment, and generating a time sequence farmland environment data set; s2, constructing a farmland digital twinborn model, and updating node and edge attribute information in real time; s3, constructing a time sequence diagram based on the updated twinborn model, and inputting the time sequence diagram to a time sequence diagram neural network; s4, optimizing structural parameters of the neural network of the time sequence diagram by applying a red-mouth blue-magpie optimization algorithm; s5, predicting the future soil water potential and the crop water demand trend by using the optimized network; s6, generating an irrigation control instruction according to the prediction result in combination with water resource supply; and S7, executing irrigation, monitoring the humidity of the land parcel and the crop state in real time, and updating the farmland digital twinborn model. According to the invention, by fusing the digital twinning, the time sequence diagram neural network and the Red-billed-blue-magpie optimization algorithm, accurate prediction and intelligent irrigation control of the future water demand trend of the farmland are realized, and the water resource utilization efficiency and the crop yield are significantly improved.

The invention provides an automatic navigation and positioning method for agricultural equipment, and the method comprises the steps: obtaining the root system residue distribution data of a previous crop and the soil hardening difference, building a correlation model of the crop root system residue distribution and the soil hardening difference, and determining a target tillage depth plan for the sowing of a next crop according to the correlation model; the operation track and depth parameters meeting the soil texture improvement requirements are applied to an automatic navigation and positioning system of agricultural equipment, and the operation process of the agricultural equipment is automatically controlled and dynamically adjusted; and based on the adjusted operation track and depth parameters, analyzing the long-term influence of an automatic navigation and positioning system of agricultural equipment on soil texture improvement in different growing seasons by tracking soil quality indexes, crop yields and root system distribution.

The invention discloses a saline-alkaline tolerant strain as well as a culture method and application thereof, and relates to the technical field of agricultural biology. Comprising the following steps: S1, screening and culturing strains; s11, carrying out salt tolerance detection on primary strains stored in a laboratory, then carrying out first screening, and screening out strains with OD600 values greater than 1.0 as candidate strains; s12, inoculating the candidate strains into a culture medium, carrying out secondary screening, and screening out strains with the OD600 value greater than 1.0 as saline-alkaline tolerant strains; and S13, inoculating the saline-alkaline tolerant strain into the EPS-producing strain fermentation medium. The application of the saline-alkaline tolerant strain provided by the invention can obviously improve the yield and quality of crops; experiments show that after the HB-02 strain is inoculated, the prevention and treatment effect on wheat root rot reaches 80.2%, the yield of wheat in saline-alkali soil is increased to 703.13 g / m < 2 > from 516.61 g / m < 2 >, the yield is increased by 36.10%, and good disease prevention and growth promotion effects are shown.

A method of determining a crop yield of a field harvested by a windrower includes receiving, from a header sensor, header data indicative of a load experienced by a header of the windrower, the header including a cutter configured to cut plant material and a conditioning system configured to condition the plant material. The method further includes receiving, from a location sensor, location data indicative of a location of the windrower, and determining the crop yield at the location of the windrower based on the header data and the location data.

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%.

A remote-sensing yield estimation method is described that is applicable to a crop whole growth period and belongs to the field of data processing methods and photogrammetry technologies with prediction purpose. In the present disclosure, in combination with agricultural knowledge of crop yields and multi-source remote-sensing data, with the biomass estimation model of whole growth period, the biomass contribution rate curve and the crop harvest index as basis, a crop yield estimation model of whole growth period with stable spatiotemporal extensibility is constructed. The yield estimation model uses the relative accumulated temperature and the remote-sensing vegetation index as variable inputs. Further, based on genetic algorithm, the parameters of the model are optimized in a case of failing to directly construct the model with data fitting due to less sample points, so as to help solve the problems of poor spatiotemporal mobility of the yield estimation model.

The invention discloses a straw returning deep scarification cultivation regulation and control method based on soil temperature and humidity, and particularly relates to the technical field of cultivation regulation and control. A plurality of soil temperature and humidity sensors and meteorological sensors are arranged in a field, soil temperature and humidity and meteorological environment data are collected in real time, preprocessing and feature extraction are carried out on the data, then a data prediction model is constructed to evaluate the accuracy of monitoring data, and if the accuracy of the monitoring data is high, the system directly adjusts the operation time and depth; if the accuracy is low, the data acquisition frequency is automatically increased by analyzing the severity of the deviation, so that the monitoring precision and the farming decision are optimized, farming errors caused by misjudgment of the soil humidity are effectively avoided, the farming effect and the crop yield are improved, and meanwhile, the soil quality is improved.

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

The invention provides a saline-alkaline tolerant Bachu halomonas strain, which is characterized in that the saline-alkaline tolerant Bachu halomonas strain is named as halomonas sp. And is preserved in the China General Microbiological Culture Collection Center (CGMCC), the preservation number is CGMCC NO.28831, and the preservation date is November 01, 2023; the nucleotide sequence of the 16S rRNA is as shown in SEQ ID NO: 1. The invention further provides application of the strain, and the strain is used for improving saline-alkaline resistance of plants, reducing soil salinity and alkalinity and promoting plant growth and is used for preparing fertilizer with plant growth promoting and saline-alkali soil improving effects. The strain disclosed by the invention has the functions of resisting salt and alkali, reducing salt and alkali, fixing nitrogen, decomposing organic phosphorus, dissolving inorganic phosphorus, producing IAA, producing oxidase, producing catalase, producing volatile acidic substances and promoting plant growth. The strain obtained by the invention has important application values in the aspects of improving saline-alkali stress resistance of crops, improving physicochemical properties of saline-alkali soil, increasing crop yield and the like, can be prepared into a fertilizer with plant growth promoting and saline-alkali soil improving effects for use, is a great innovation in saline-alkali soil improvement and crop planting, and has a wide application prospect.

The embodiment of the invention provides a multi-crop yield prediction method and device based on remote sensing collaborative inversion, and is applied to the technical field of remote sensing images. The method comprises the following steps: performing time sequence remote sensing data reconstruction processing on acquired multi-source remote sensing image data to generate a time sequence remote sensing data set; generating crop area identification data according to the time sequence remote sensing data set based on a preset multi-crop area identification model; generating agricultural condition multi-parameter inversion data in the to-be-processed region according to the time sequence remote sensing data set and the crop area identification data based on a preset multi-agricultural condition multi-parameter inversion model; and based on a preset multi-crop yield prediction model, generating crop product prediction data according to the agricultural condition multi-parameter inversion data. In conclusion, a multi-stage linkage full-process model of'time sequence remote sensing data reconstruction + crop area identification + agricultural condition parameter inversion + crop yield prediction 'can be constructed, and a high-precision, high-timeliness and automatic integrated yield prediction solution is formed.