1280 results about "Crop yield" patented technology

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 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.

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 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 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 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 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.

The invention discloses a farmland soil humidity intelligent monitoring system based on the Internet of Things, and relates to the field of resource optimization decision crossing, a soil humidity monitoring module executes a soil humidity adjustment plan, monitors farmland soil humidity time sequence data in real time, and obtains a farmland soil humidity monitoring result through time sequence analysis and K-means clustering; obtaining an association rule between the soil humidity time sequence data and the crop yield; and the humidity monitoring report generation module is used for optimizing the soil humidity adjustment plan by tracking an optimal soil humidity threshold value in real time by utilizing a dynamic programming hybrid optimization algorithm according to the optimal soil humidity range, and generating a farmland soil humidity monitoring report. Through an optimization model coupled by a crop moisture production function and a multi-objective genetic algorithm, and in combination with Monte Carlo global sensitivity analysis, an optimal soil humidity range is identified, so that accurate quantitative modeling of a humidity-yield nonlinear relationship is realized, the defect of a static soil humidity threshold is avoided, and the sensitivity of moisture stress early warning is improved.

The invention provides a crop planting suitability dynamic evaluation method and system and a storage medium, and the method comprises the steps: S1, carrying out the time sequence processing of collected NDVI, meteorological data and static data; s2, dividing the phenological period of the target crop into a plurality of stages; s3, constructing a seasonal suitability function for the NDVI and the meteorological data to calculate scores of the data in each stage of a phenological period; s4, determining the score of the static data in each stage according to a preset membership score and grade division; s5, according to the historical NDVI, the meteorological data and the static data, the correlation coefficient of each data and the historical target crop yield and the entropy weight of each data are calculated respectively, and the proportion of the data and the historical target crop yield is adjusted through empirical parameters to obtain the dynamic weight corresponding to each data; and S6, performing weighted summation on the data based on the scores obtained in the steps S3 and S4 in combination with corresponding dynamic weights to obtain a crop planting suitability dynamic index. Therefore, the phenological accuracy of the planting suitability evaluation result of the target crop is improved.

The invention relates to the field of microorganisms, in particular to Rahnella and Burkholderia with growth promotion and biological control functions and a composition thereof, and the Rahnella comprises a 16S rDNA sequence shown in SEQ ID NO: 1 or a complementary sequence thereof or a sequence which is at least 85% identical with the sequence shown in SEQ ID NO: 1; the burkholderia comprises a 16S rDNA sequence as shown in SEQ ID NO: 2 or a complementary sequence of the 16S rDNA sequence or a sequence which is at least 85% identical with the sequence as shown in SEQ ID NO: 2. The new strains Rahnella victoriana Rahn.R5, Burkholderia ubonensis Burk.R2 and the application of the composition of the Rahnella victoriana Rahn.R5 and the Burkholderia ubonensis Burk.R2, which are provided by the invention, in promoting crop growth and / or preventing and treating plant diseases, have important significance on improving the crop yield and promoting agricultural green production.

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, by mitigating or eliminating the need for exogenous nitrogen-containing fertilizers. The remodeled microbes have unique colonization and nitrogen fixation abilities, which enable the microbes to deliver nitrogen to a cereal plant in a spatially targeted (e.g. rhizospheric) and temporally targeted (e.g. during advantageous stages of plants life cycle) manner. The microbes are able to replace the standard agricultural practice of sidedressing and enable a more environmentally sustainable form of farming. The present disclosure also provides methods of using non-intergeneric remodeled microbes, for example, to fix atmospheric nitrogen by reducing or eliminating the need for exogenous nitrogen-containing fertilizers, to increase yield, and to reduce infield variability in the yield.

The invention relates to the field of crop cultivation, and particularly discloses a multi-crop interplanting method with a high land utilization rate. According to the method, a multi-crop interplanting land distribution scheme is designed on the basis of user planting requirements, planting molds are built on land parcels with serious soil problems, and the planting molds are filled with crop growth supporting materials to serve as planting soil; planters can interplant various crops in the planting soil according to a multi-crop interplanting land distribution scheme, crops with serious soil-borne diseases, such as solanaceae or medicinal materials, can be planted on the same land for many years, planting can be carried out in land parcels with serious soil problems, the land utilization efficiency is improved, and the planting cost is reduced. The crop yield per unit land area can be increased in an interplanting mode, land resources are fully utilized, meanwhile, the planting production cost is reduced, and the economic output per unit area is increased.

The invention relates to the technical field of microorganisms, in particular to a disease-resistant, growth-promoting and yield-increasing strain and a microbial agent and application thereof. The strain is a strain bacillus velezensis NB21 (CCTCC (China Center for Type Culture Collection) NO: M2024979) which is found autonomously; the strain can grow at low temperature, is strong in ecological environment adaptability, can be used for preparing a single microbial agent or a compound microbial agent, and can also be used for forming a composition with other functional organic and inorganic substances. The prepared microbial agent can significantly prevent and treat diseases, promote root development of crops such as rice and corn, enhance photosynthesis, improve system resistance, increase crop yield, enhance nutrient supply and improve soil micro-ecology. The strain is unique, strong in colonization ability and diversified in function, the prepared microbial agent fermentation production technology is mature, the cost is low, the application range is wide, the yield increasing and quality improving effects are outstanding, and the microbial agent has good application prospects.

The invention relates to a farmland irrigation decision-making method and device based on multi-source data, equipment and a medium. The method comprises the steps of obtaining multi-source heterogeneous data of a farmland in real time, and obtaining synchronized soil data, meteorological data and crop growth data based on the multi-source heterogeneous data; the multi-source heterogeneous data comprises soil data, meteorological data and crop growth data; obtaining a multi-source fusion irrigation index based on the synchronized soil data, meteorological data and crop growth data; obtaining an irrigation strategy based on a preset multi-target optimization model according to the multi-source fusion irrigation index; and obtaining an irrigation scheme based on the irrigation strategy. By adopting the method, the water resource utilization efficiency can be improved, and the irrigation water consumption is reduced; the crop yield is improved and the production cost is reduced; the adaptability of the system to climate change is enhanced, and the robustness of the strategy is improved.

The invention relates to the technical field of microorganisms, in particular to application of bacillus glucans and a complex microbial inoculant thereof in preparation of bio-organic fertilizers, the strain Mh85 is separated from the root system of apple rootstock Malus hupehensis treated by a research group through trichoderma TB2, has good IAA production, nitrogen fixation, inorganic phosphorus decomposition and siderophore production capacities, and can be used for preparing bio-organic fertilizers. The strain Mh85 not only can directly promote plant growth, but also has a good growth-promoting effect on trichoderma TB2, and when the strain is compounded with the trichoderma TB2 to form a complex microbial inoculant, tomato seedling pot experiments are carried out. The complex microbial inoculant shows a synergistic effect, effectively overcomes the problems of unstable growth promoting effect, insufficient synergistic effect among strains and the like of an existing single microbial preparation, provides an efficient and stable technical scheme for improving plant growth, improving root development and increasing crop yield, and is a good raw material for preparing a bio-organic fertilizer.

Method and corresponding system for estimating crop yield from vegetation index data. At least one multispectral image (201) including a field is obtained. The part of the multispectral image (201) that represents the agricultural field (301) is delineated, and vegetation indices for locations within the agricultural field (301) are derived from the multispectral image (201). Samples of actual yield data representing yield measurements for locations within the agricultural field (301) are obtained as measured by a yield monitor on a combine harvester (208) used to harvest selected areas of the agricultural field (301). By correlating the vegetation indices with the yield data, a relationship between respective vegetation index values and corresponding absolute yield estimates is determined. The determined relationship can be used to obtain estimates of actual yield from different parts of the field, and this information can be used to identify field maintenance needs, fertilization rates, and more.

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 provides a tobacco area nitrogen management and efficiency evaluation system, which comprises a data acquisition module for acquiring meteorological data, soil nitrogen data, crop growth data and historical fertilization data in real time; the data preprocessing module is used for carrying out noise filtering, space-time alignment and multi-source data fusion; the dynamic nitrogen prediction module is coupled with the soil nitrogen migration and crop absorption model to generate a prediction value; the optimization decision module is used for generating an application strategy based on a dynamic programming algorithm and calculating the optimal application amount and the fertilization time; the efficiency evaluation module is used for calculating nitrogen utilization efficiency (NUE) and environmental benefit index (EBI) by combining the application amount, the environmental loss amount and the crop yield; the self-adaptive feedback module is used for dynamically correcting prediction model parameters according to the deviation degree of the NUE and the EBI; and the visual output module is used for generating a multi-dimensional evaluation report and a fertilization decision map. The nitrogen utilization efficiency and the environmental benefit index can be improved, the environmental risk is reduced, and the tobacco yield and quality are improved.

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

The invention belongs to the technical field of crop planting, and discloses a Yellow River Delta winter wheat salt-tolerant variety screening and cultivated land quality improving method, which comprises the following steps: step 1, indoor culture test, namely influence of different salinity concentrations on winter wheat budding, step 2, field positioning test, and step 3, field positioning test. The invention relates to screening of salt-resistant suitable-growth high-yield varieties of winter wheat in mild saline-alkali cultivated land, and influences of organic materials with different carbon sources on soil quality and crop yield. According to the invention, the physiological status and agronomic traits of the current Yellow River Delta slight saline-alkali soil winter wheat variety in different growth periods are explored, and a theoretical basis is provided for screening the salt-resistant, suitable-to-grow and high-yield winter wheat variety; the influence of different carbon source materials on the physicochemical properties of the saline-alkali soil is analyzed, the microbial enhancement action mechanism in the saline-alkali soil is explored, 4-5 salt-tolerant winter wheat varieties are screened for the theoretical basis for improving the quality and productivity of the saline-alkali soil of the Yellow River delta, the physicochemical properties of the saline-alkali soil are improved, and the soil microbial community structure and diversity are enhanced.

The invention relates to a microbial strain and application thereof, in particular to bacillus brevis, a microbial agent prepared from the bacillus brevis and application of the microbial agent. The invention provides the cold-resistant bacillus subtilis which has multiple functions of generating capsular polysaccharide, being high in salt resistance and cold resistance, promoting growth and disease resistance of plants in saline-alkali soil and the like. The bacterial strain can generate capsular polysaccharide, experiments prove that the bacterial strain has high capabilities of dissolving phosphorus, fixing nitrogen and producing siderophore, IAA, ACCD and cytokinin, and has strong antagonistic action on fusarium, curvularia and microplasmodium, and the generated capsular polysaccharide can promote salt tolerance of plants, so that the yield of the plants is increased, and the yield of the plants is increased. The bio-fertilizer is suitable for production of multifunctional bio-fertilizers with disease prevention, growth promotion, salt and alkali resistance, cold resistance and the like, is applied to production of saline-alkali soil grain crops, pasture and greenhouse commercial crops, has remarkable effects of promoting cold damage resistance, root development, prevention and control of root diseases, improvement of crop yield and the like, can reduce the use of chemical fertilizers and pesticides, and has a wide application prospect.

The invention discloses a method for preparing a fertilizer special for spring corn crops under straw returning, and belongs to the technical field of corn planting. According to the method, soil nutrient testing and the yield increasing effect of fertilization on crops are combined, and the nutrient supply level of soil is truly reflected; the habitual fertilization of farmers is optimized under the condition of comprehensively considering the nutrients brought in by the straws of previous crops, the nutrient release rate and the nutrient effectiveness of the straws, the relatively high carbon-nitrogen ratio under straw returning, and the apparent balance of phosphorus and potassium in the soil of a producing area. Compared with traditional farmer habitual fertilization, the optimized recommended fertilization formula provided by the invention not only contains previous crop straw nutrients, but also provides a special fertilizer formula for a main growth stage. The formula of the special crop fertilizer for the spring corn planting system under straw returning not only can improve the yield of crops, but also can improve the utilization efficiency of nutrients.

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 relates to the crossing field of agricultural intelligent algorithms and intelligent equipment technologies, in particular to an environment regulation and control model algorithm and algorithm model platform fusing crop physiology. According to the model algorithm, through four-step calculation of spatio-temporal heterogeneity adaptation, multi-source data dynamic coupling, stress self-adaptive adjustment and self-learning iterative optimization, a partition precise regulation and control decision value is generated; the algorithm model platform comprises a data acquisition layer (a partition sensor and a multi-source interface), a feature processing layer (a three-dimensional feature library and data preprocessing), a model algorithm layer (deploying the model algorithm) and a platform application layer (partition regulation and control and closed loop feedback), and full-link cooperation of algorithm calculation, platform execution, effect feedback and algorithm iteration is achieved. The crop yield is increased by 15-20%, water resources are saved by 25-30%, and the method is suitable for multi-crop facility agriculture scenes.