33 results about "Crop species" patented technology

The invention belongs to the field of computer vision, and discloses a plant disease identification method and system based on a three-branch deep neural network, and the method comprises the steps: firstly extracting global, local and middle-layer multi-scale positioning features through employing Vision Transformer, ResNet50 and YOLO in parallel; secondly, aligning and fusing the three paths of features, carrying out interactive enhancement through Transform block, and then respectively predicting crop types and disease types through a double-end stacking classifier; and finally, by setting a proper loss function combination and a joint training strategy, performing end-to-end optimization on the whole network, so as to improve the recognition capability of difficult-to-judge samples and small-scale lesions. According to the invention, accurate identification of plant diseases in a natural scene can be realized. Experimental results show that the method provided by the invention obtains classification performance equivalent to or even better than that of the most advanced plant disease identification method in the prior art.

A crop species identification system based on satellite telemetry data is disclosed, which includes: a receiver module, receiving plural telemetric vegetation indexes of a target area; a data cleaning module, cleaning anomalous data in the telemetric vegetation indices, to correspondingly generate cleaned index data; a feature extraction module, including at least two different convolution kernels for mapping the cleaned index data into at least two feature scale mapping data which respectively correspond to the convolution kernels, performing a pooling operation of the feature scale mapping data to generate a pooled data, and concatenating the at least two feature scale mapping data and the pooled data into concatenated data; and a classification module, including a fully-connected layer, for extracting features of the concatenated data, to generate a species classification result for the target area.

The invention provides a saline-alkali soil crop multi-water-source irrigation control method and system, and belongs to the technical field of agriculture, and the method comprises the steps: determining an irrigation water salinity target threshold value and a target total irrigation amount according to soil salinity data and crop physiological parameters; determining a water distribution strategy based on the salinity concentration data, wherein the water distribution strategy at least comprises irrigation priorities and collocation of different types of water sources; based on the target irrigation total amount, the irrigation water salinity target threshold value and the water distribution strategy, the target water taking amount of each type of water source is determined; and generating an irrigation control instruction according to the target water intake amount. According to soil salinity, crop types, crop physiological parameters and salinity concentration of multiple water sources, the irrigation water salinity target threshold value and the irrigation water total amount are dynamically and accurately determined; and meanwhile, a priority water distribution strategy is formulated, so that flexible allocation of irrigation among multiple water sources and fine management of the salinity concentration of crop irrigation water can be realized, and soil salinization and crop salinity stress are effectively avoided.

The application provides a crop disease precision identification method and system, and relates to the technical field of crop disease identification. The method comprises the following steps: establishing standard lesion feature image datasets of different crops and different organs; constructing a background feature database of common crop diseases; forming a crop type and organ identification algorithm model and an organ lesion identification algorithm model; determining the crop type and the organ of the crop in an image to be identified based on the crop type and organ identification algorithm model; determining the disease range of the image to be identified based on the organ of the crop and using the organ lesion identification algorithm model corresponding to the organ of the crop, and outputting a lesion disease with similar features; outputting a matched crop disease type based on the background feature database of common crop diseases; and obtaining a common disease type in the determined lesion disease and the determined crop disease type, so as to realize the precision identification of the disease. The application classifies and grades to form a structured disease identification process, and improves the identification precision.

The invention discloses a rice field robot cluster remote cooperative control system based on edge computing, and relates to the field of program control, and the system comprises a master control module which serves as a central control end, issues a control instruction to distributed edge nodes, and receives state feedback; the crop identification module is deployed at an edge node and acquires attribute data of crop types, growth stages and distribution density of each region of the target rice field through an image sensor; the environment acquisition module is used for being deployed at an edge node and acquiring rice field meteorological data and soil environment data in real time; the crop growth environment and the machine state are monitored and responded in real time, when an abnormal environment event is detected, crop identification data can be rapidly integrated to generate an influence event and evaluate an influence coefficient, and a nearby automatic machine is immediately matched and scheduled for intervention, so that the timeliness of operation is ensured, crop loss can be effectively prevented, and the working efficiency is improved. And the efficiency of agricultural production is improved.

The invention relates to the technical field of laser illumination, in particular to a laser diode agricultural illumination system and method based on a combined spectrum, and the system comprises a light source module, an illumination intensity adjustment module, an illumination time control module, a spectrum monitoring module and a feedback adjustment module. According to light quality requirements of leaf vegetables and solanaceous crops in different growth stages, a spectrum combination proportion is determined, a specific wavelength spectrum is supplemented, illumination intensity is dynamically adjusted according to a plant growth state, illumination time is set according to crop types and growth stages, the spectrum is monitored in real time, optimal parameters are compared, and a spectrum combination is adjusted according to deviation. Spectrum, light intensity and illumination time are accurately matched with crop requirements, light energy waste is reduced, it is guaranteed that crops obtain proper illumination conditions in all growth stages, photosynthesis efficiency is promoted to be improved, and efficient and accurate development of agricultural production is promoted.

The disclosure relates to isolated microorganisms—including novel strains of the microorganisms, microbial consortia, and agricultural compositions comprising the same. Furthermore, the disclosure teaches methods of utilizing the described microorganisms, microbial consortia, and agricultural compositions comprising the same, in methods for imparting beneficial properties to target plant species. In particular aspects, the disclosure provides methods of increasing desirable plant traits in agronomically important crop species.

The invention provides a method for biomimetic directional construction of crop growth-promoting synthetic bacteria and application, and belongs to the technical field of microbial screening. According to the method, optimized microbial communities are naturally selected based on wild sibling species, a species composition feature table is determined through high-throughput sequencing data analysis, core bacteria are calculated, and core bacteria composition feature tables of amplicons and metagenomes are obtained respectively; and then a hybrid correction strategy of a machine learning algorithm is adopted to obtain a high-quality core bacterium composition feature table of the wild related species of the crops, and growth-promoting synthetic bacteria are designed according to the core bacterium composition feature table, so that candidate microorganisms are directionally screened from the wild related species of the crops in a targeted manner, and the growth-promoting synthetic bacteria of the cultivated species of the crops are constructed. According to the method disclosed by the invention, interaction between crop species specificity and synthetic bacteria is considered, the method has the advantages of high efficiency, accuracy, orientation and the like, and construction of crop synthetic bacteria composition is converted from artificial random combination to bionic oriented construction.

The application provides an end-cloud cooperative farmland condition image acquisition system based on 5G communication and machine learning, relates to the technical field of end-cloud cooperation of 5G communication and machine learning, and uses a data acquisition end to acquire multispectral image data of a farmland to be observed, carries out compression preprocessing, reduces data transmission burden, and then sends the compressed image to a data processing module; the module carries out denoising and feature extraction on the image, constructs a feature data vector and transmits the feature data vector to a cloud server; the cloud server analyzes the feature data vector by using a random forest algorithm model, judges crop types and carries out classification, and finally returns the analysis result to a data display module; the data display module uses a Flask framework to generate a dashboard, directly and intuitively displays farmland condition data, including key indexes such as crop types and coverage, and realizes end-cloud cooperation architecture through 5G communication for data transmission, so that efficient and real-time data transmission and processing are realized.

The utility model discloses an agricultural planting three-dimensional planting frame which comprises a planting frame and a planting auxiliary frame, the planting auxiliary frame is arranged below the planting frame, and connecting columns are installed at the four corners of the planting frame. According to the three-dimensional planting rack for agricultural planting, through the design and use of the planting auxiliary frames, the planting frames and the connecting columns, when crops are planted, growers can reasonably adjust and utilize the planting space according to seasons and crop types, so that the high space utilization rate is achieved, and the economic benefits are improved; by means of the planting auxiliary frame, the planting frame and the connecting columns, double-layer combined planting can be achieved, namely, the fences are placed on the edges of the planting auxiliary frame, the planting frame is kept transparent, and therefore the planting auxiliary frame can be filled with deep soil, and the planting requirements of different crops are met.

The utility model relates to the technical field of temperature and humidity monitoring, in particular to a temperature and humidity monitoring device for a greenhouse, which is characterized in that one side of the upper part and the lower part of a side frame are respectively connected with two fixing plates in a sliding manner, the inner side of the side frame is connected with a supporting plate in a sliding manner, and the bottom of the supporting plate is fixedly connected with the temperature and humidity monitoring device through a bolt; the top of the supporting plate is rotationally connected with a threaded rod through a rotating shaft, the top end of the threaded rod penetrates through the top of the side frame through a threaded groove, mounting plates are fixedly connected to one side of the upper portion and one side of the lower portion of the bearing plate, and sliding blocks are fixedly connected to one side of the upper portion and one side of the lower portion of the side frame. The temperature and humidity monitoring equipment can be flexibly adjusted in the transverse position according to crop types, planting areas and environment changes, the problem that a traditional fixed installation mode cannot adapt to multi-point-position and dynamic monitoring requirements is solved, the application range of the equipment is widened, and the representativeness of monitoring data is improved.

The present disclosure relates to materials and methods useful for improving the efficacy of a plant breeding program such as, for example, the method for producing hybrid seeds in a facultative apomictic crop species, which in turns are useful for, for example, commercial production of highly uniform hybrid progeny. Hybrid seeds produced by such improved breeding methods, and plant grown from such hybrid seeds are also within the scope of the present invention. The disclosure further relates to processes for making a plant-derived product derived from any of the foregoing hybrid plants, and plant-derived products produced by such processes.

The present application relates to agriculturally beneficial microorganisms, microbial compositions and consortia. The present disclosure relates to isolated microorganisms, novel strains including microorganisms, microbial consortia, and agricultural compositions comprising the same. In addition, the present disclosure teaches methods of utilizing the microorganisms, microbial consortia, and agricultural compositions comprising the same in methods of conferring beneficial properties to target plant species. In particular aspects, the disclosure provides methods of enhancing desired plant traits in agronomically important crop species.

The present disclosure relates to edited (genetically modified) microorganisms comprising consortia of microorganisms, and to agricultural compositions comprising said microorganisms, intended to increase the tolerance of plants to nematode pests and / or to directly reduce nematode populations in soil or root systems. In addition, the present disclosure teaches methods of utilizing the described microorganisms, microbial consortia, and agricultural compositions comprising the same in methods of conferring beneficial properties to target plant species. In particular aspects, the present disclosure provides methods for improving desired plant traits and reducing undesired pests that affect crop performance in agronomically important crop species.

The invention provides a crop whole-growth-period drought monitoring and early warning method based on an adaptive water shortage index. The method comprises the following specific steps: establishing a regression relationship between a crop coefficient and a leaf area index; calculating the daily potential evapotranspiration; establishing an effective rainfall experience calculation formula, and calculating day-by-day effective rainfall according to the rainfall observed by the meteorological station; constructing adaptive day-by-day-scale crop water shortage indexes considering different crop types and drought-enduring characteristics in different growth stages; setting a crop drought grade division standard based on the adaptive water shortage index, carrying out crop drought identification and grade research and judgment, and achieving the monitoring and early warning of the whole process of crop drought occurrence, development and ending. According to the method, multi-source data such as remote sensing data, meteorological data and crop characteristic data are fused, the problem of water shortage sensitivity difference of different crop types and different growth stages is considered, the adaptive day-by-day scale crop water shortage index is constructed, and the whole drought process of the crop growth stage can be monitored.

The application provides a saline-alkali soil crop multi-water source irrigation control method and system, and belongs to the field of agricultural technology, and comprises the following steps: determining an irrigation water salt content target threshold value and a target total irrigation amount according to soil salt content data and crop physiological parameters; determining a water distribution strategy based on the salt content concentration data, wherein the water distribution strategy at least comprises irrigation priorities of different types of water sources and collocations; determining a target water intake amount of each type of water source based on the target total irrigation amount, the irrigation water salt content target threshold value and the water distribution strategy; and generating an irrigation control instruction according to the target water intake amount. According to the soil salt content, the crop type, the crop physiological parameters and the salt content concentration of the multi-water source, the irrigation water salt content target threshold value and the total irrigation amount are dynamically and accurately determined; meanwhile, a priority water distribution strategy is formulated, so that flexible allocation of irrigation among the multi-water sources and fine management of the salt content concentration of the crop irrigation water can be realized, and soil salinization and crop salt stress can be effectively avoided.

The application relates to the technical field of rainwater recycling, in particular to a rainwater intelligent recycling system and method for facility agriculture. The system comprises a shed body covering a planting area, a rain guiding and adjusting device installed at the top of the shed body and used for guiding and adjusting the flow direction of rainwater, a rainwater driving device used for driving the rain guiding and adjusting device to act, a rainwater collecting device, a sensing unit used for detecting environmental parameters in and outside the shed in real time, and a processor. The processor is provided with the information about the types of planted crops and the requirements of different growth stages of the crops, is used for calculating the utilization requirement parameters of rainwater according to the information about the types of crops and the growth stages and the environmental parameters detected by the sensing unit, and controls the rainwater driving device based on the parameters to adjust the working state of the rain guiding and adjusting device, so that the rainwater is collected in the shed body area as required. The reasonable distribution of the collection of rainwater and the soil flushing of crops in the shed is solved.

The invention belongs to the technical field of agricultural irrigation, and relates to an intelligent irrigation scheme recommendation method based on terrain soil crop collaborative analysis. A coupling rule knowledge base integrating terrains, gradients, soil types and crop types is constructed, a rule engine is used for matching multivariate parameters input by a user, so that the most suitable irrigation mode is intelligently recommended, a crop water demand model, soil water retention characteristics and gradient correction factors are integrated, and the irrigation efficiency is improved. Quantitative reference flow capable of being directly used for engineering design is obtained through calculation, and key design parameters and key points are output in an associated mode. According to the method, automatic and quantitative output from environment and agronomic parameters to irrigation system model selection, hydraulic calculation and design guidance is realized, the problems of high experience dependence and insufficient multi-factor collaborative consideration in the prior art are effectively overcome, and the method has the characteristics of scientific recommendation result, high adaptability and offline deployment support, and is suitable for popularization and application. The method is suitable for irrigation system planning and design of various agricultural scenes such as high-standard farmlands, orchards, hills and mountains and the like.

The invention relates to a crop classification method and system based on coded aperture snapshot calculation spectral imaging, and the method comprises the following steps: S1, capturing an agricultural scene through an optical encoder and an imaging element based on compressed sensing, and obtaining an output result of the optical encoder; s2, constructing a data set of an output result of the optical encoder and a real label of the crop variety, designing a deep learning decoder, constructing a deep learning framework, respectively carrying out training, testing and verification, and finally obtaining a weight file of the model through gradient descent; and S3, collecting compressed data through an optical encoder, and obtaining a crop classification result of an agricultural scene and a hyperspectral image of the corresponding scene in real time through network reasoning by using the obtained weight file. According to the crop classification method and system, an integrated method of optical hardware coding and decoding based on the deep learning network is used for crop classification, the classification speed is greatly improved, and the data storage cost is reduced.

The invention provides a smart agricultural knowledge base system and method based on an AI large model, and relates to the technical field of smart agricultural knowledge bases. The method comprises the steps of collecting agricultural historical big data, and performing data clustering processing for different crop types to form crop clustering data; performing parameter similarity analysis aiming at operation content on different crop clustering data to form a crop operation similarity model; and obtaining real-time parameters of the target crop, performing similarity analysis according to the crop operation similarity model, and determining corresponding crop operation content. According to the method, agricultural big data is reasonably analyzed and processed, and a more intelligent data matching result is provided to meet operation requirements of different planting and breeding conditions.

The invention relates to the technical field of agricultural land planning, in particular to a land utilization efficiency analysis method and system, and the method comprises the following steps: obtaining planting time sequence data: obtaining historical planting time sequence data of a target land parcel, the data at least comprising the variety of crops planted each time, the corresponding sowing date and the corresponding harvesting date; neutral period calculation: calculating the length of a field neutral period between two adjacent planting in the historical planting time sequence according to the sowing date and the harvesting date; analysis: comparing each field neutral period length obtained by calculation with a preset reasonable neutral period threshold range for a specific combination of preceding and succeeding crops; based on the comparison results, a land utilization efficiency diagnostic report is generated that at least explicitly lists specific crop rotation locations diagnosed as unreasonable. The system comprises a processor and a memory in communication connection with the processor. According to the invention, the compactness and rationality of time utilization can be diagnosed.

The present application relates to agriculturally beneficial microorganisms, microbial compositions and consortia. The present disclosure relates to isolated microorganisms, novel strains including microorganisms, microbial consortia, and agricultural compositions comprising the same. In addition, the present disclosure teaches methods of utilizing the microorganisms, microbial consortia, and agricultural compositions comprising the same in methods of conferring beneficial properties to target plant species. In particular aspects, the disclosure provides methods of enhancing desired plant traits in agronomically important crop species.

The invention discloses an irrigation scheduling method based on remote sensing fusion and automatic linkage of an unmanned aerial vehicle, and the method comprises the steps: building a spatial incidence relation among a canal system, a measurement and control integrated gate, a farming land parcel and crop planting distribution; specific association can be realized between crop species in different farming land parcels and the control relation of the measurement and control integrated gate to the specific farming land parcels. The unmanned aerial vehicle is used for collecting low-altitude remote sensing data, the evapotranspiration inversion model and the field moisture dynamic balance model are combined, the actual crop evapotranspiration and the net irrigation water demand are calculated for the land parcel level water demand control unit, and the limitation that in the prior art, water demand prediction depends on fixed parameters, and spatial heterogeneity is difficult to reflect is overcome. The water distribution scheduling model is constructed based on the land parcel level net irrigation water demand and the management attribute, and the optimal scheme is executed through the integrated gate, so that the problems that the water distribution scheme is disjointed with actual execution and automatic linkage is lacked in the prior art are solved, and the overall efficiency and the collaboration of irrigation scheduling are improved.

A fertilizer is provided to promote plant growth that provides needed nutrients to a crop at planting that has a depressed freezing temperature relative to conventional liquid fertilizers. The fertilizer has a first fertilizer product including a bio-available nitrogen source, a bio-available phosphorus source, and a bio-available potassium source that is blended with a second fertilizer product including an amount of ammonium acetate. The first and second fertilizer products being present in synergistically effective amounts, such that the coordinated amounts of the first and second fertilizer products give crop yields greater than a mere additive effect obtainable through the use of the respective first and second fertilizer products alone in the same amounts. The synergistically effective amounts of the first and second fertilizer products are directed to particular crops, and depend upon the crop in question, soil conditions, ambient weather, and other factors.

Transgenic methods and constructs enable efficient genotype-independent in planta transformation of cercal crop species. A method of transforming cereals in planta includes co-incubating, post-anthesis, a cereal inflorescence of developing embryos with an inoculation medium comprising a transgene-modified Agrobacterium; collecting, upon the cereal inflorescence reaching maturity, seeds produced from at least a portion of the developing embryos; and selecting one or more transgenic seeds from among the collected seeds.

The disclosure relates to isolated microorganisms—including novel strains of the microorganisms, microbial consortia, and agricultural compositions comprising the same. Furthermore, the disclosure teaches methods of utilizing the described microorganisms, microbial consortia, and agricultural compositions comprising the same, in methods for imparting beneficial properties to target plant species. In particular aspects, the disclosure provides methods of increasing desirable plant traits in agronomically important crop species. 20 26 20 47 60 19 J un 2 02 6 2 0 2 6 2 0 4 7 6 0 1 9 J u n 2 0 2 6

The invention discloses a method and system for optimizing an agricultural carbon sequestration, emission reduction, efficiency improvement and water conservation mode in an arid region, and belongs to the technical field of agricultural resource management. Water consumption data, social and economic data, agricultural element investment data and greenhouse gas emission data corresponding to main crop types in a research region are acquired, a data set is constructed, and the data set is stored in a database; establishing a target function corresponding to maximum yield, maximum economic benefit, minimum irrigation water consumption and minimum clean greenhouse gas emission, obtaining a current water-saving mode configuration scheme, determining various weights of the target function through an analytic hierarchy process, obtaining the weighted target function, and determining the optimal water-saving mode configuration scheme according to the optimal water-saving mode configuration scheme. Introducing constraint conditions of a water source allowable water supply amount constraint, a water demand constraint, a planting area constraint, a grain safety constraint and a non-negative constraint, constructing a multi-target collaborative optimization model, and solving to obtain a plurality of optimal irrigation systems and optimal crop planting layouts meeting various constraint conditions. According to the method, multi-objective optimization of agricultural water saving, carbon sequestration, emission reduction and efficiency improvement in the arid region can be realized.