72 results about "Crop species" patented technology

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

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.

The invention provides an intelligent diagnosis and decision-making method and system for crop diseases in intelligent agriculture, and the method comprises the steps: extracting spatial features through employing a convolutional neural network based on standardized remote sensing image data in a unified multi-source data set, generating preliminary positioning data of a scab region, and carrying out the preliminary positioning of the scab region; the scab area preliminary positioning data comprises scab position coordinates and range boundaries; inputting the key time weighted sequence data and the scab area preliminary positioning data into a space-time attention mechanism, and fusing spatial position coordinates and time environment features through attention weight distribution to generate comprehensive disease feature vector data; and based on the preliminary prevention and control prescription data, adopting a drug resistance evaluation model to analyze the drug use frequency, the disease type and the crop variety in the historical drug use data, generating optimized prevention and control prescription data, and adjusting the drug variety or dosage to reduce the drug resistance risk.

The invention discloses a self-adaptive power generation optimization control system for a greenhouse photovoltaic ceiling, relates to the technical field of ceiling power generation control, and is used for solving the problem that photosynthetic efficiency and yield are affected due to the fact that crops are in a state of insufficient illumination or excessive illumination in a growth stage. The method comprises the following steps: calculating illumination deviation by combining an illumination demand library and an illumination sensor; judging an illumination level according to deviation and photosynthetic response characteristics; collecting a leaf potential and a transpiration rate to calculate photosynthetic efficiency; comprehensively judging whether to adjust the angle of a photovoltaic panel, outputting an execution instruction, setting different control mechanisms, and executing a balance optimization strategy when the power generation efficiency is high. And when the power generation efficiency is low, an intelligent switching strategy is executed, the productivity is preferentially improved, the crop illumination tolerance is considered, the angle is dynamically adjusted, collaborative optimization of agriculture and energy is realized, and the adaptability and the operation efficiency of the system are improved.

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 belongs to the technical field of seedling cultivation, and discloses a cadmium telluride power generation glass three-dimensional multilayer seedling raising greenhouse and an illumination regulation and control system. Comprising a layered illumination sensing module which constructs an interlayer illumination distribution map and an interlayer illumination profile map based on the photosynthetically active radiation density of each seedling raising layer; the light-requiring threshold generation module is used for generating a target illumination intensity upper and lower limit curve of each seedling raising layer based on seedling raising crop types, growth stages and environmental parameters; the illumination comprehensive judgment module is used for identifying seedling raising layers with insufficient illumination or excessive illumination based on the interlayer illumination distribution map and a target illumination intensity upper and lower limit curve, and generating a dimming priority sequence in combination with the interlayer illumination profile map; the illumination intelligent control module is used for generating and executing a distributed dimming instruction set by combining the dimming priority sequence based on the output power of the cadmium telluride power generation glass and the consumed power of the seedling raising greenhouse; according to the invention, distributed self-adaptive light supplement is realized in the seedling raising process in the cadmium telluride power generation glass seedling raising greenhouse.

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 relates to a crop disease identification method based on wavelet transform and residual network fusion. According to the method, a CBAM attention mechanism, wavelet transform and a residual network are fused, and a disease identification model (CropNet) for non-specified crop types is provided. According to the CropNet, firstly, Haar wavelets are utilized to perform four-stage decomposition on disease images, frequency domain features are deeply extracted, and the frequency domain features and spatial features extracted by a residual network are continuously fused; then different weights are given to the fused feature layer by using CBAM, and the attention of the model to a disease area is increased; and finally, a dual transfer learning training model is utilized to improve the accuracy and generalization of the model for identifying diseases of non-specified crop types. The identification accuracy of the CropNetA is 99.76%, the identification accuracy of the CropNetA is 99.85%, and the identification accuracy of the CropNetA is 99.86% on the PlantVillage data set, the identification accuracy of the CropNetA is 99.85% on the AI Challenger 2018 data set and the identification accuracy of the CropNetA on the self-built data set. The result shows that the method can obtain clearer and more sufficient disease characteristics while reducing the noise, improves the disease recognition precision, and provides reference for intelligent agriculture and precise recognition, prevention and control of crop diseases.

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 application discloses a method for evaluating rice field and dry land crop removal of soil heavy metal flux based on model estimation, relates to the field of agricultural environmental science and technology, and determines the planting mode characteristics and the estimation range of the crop removal of soil heavy metal flux under various planting modes in a target region through a standardized field test, and constructs a standardized crop heavy metal absorption parameter database, wherein the planting modes include single-cropping rice, double-cropping rice, rice-oil rotation, rice-tobacco rotation and leafy dry land crops, and the planting mode characteristics include planting cycles and crop types. Through the construction of the standardized crop heavy metal absorption parameter database, the combination of the planting mode characteristics and the dynamic change data of the heavy metal form of rhizosphere soil, the removal ability of crops to soil heavy metals can be more accurately quantified, meanwhile, the crop heavy metal absorption parameter database integrates the biomass, heavy metal concentration and flux calibration data, eliminates the interference of environmental variation, and forms a reliable heavy metal removal flux estimation range.

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.

A method for identifying plant diseases and their severity based on a multi-label strategy includes: 1. constructing a training set, a validation set, and a test set; 2. building a multi-label classification network; 3. selecting a batch of crop disease images and inputting them into a multi-label feature extraction module. Feature extraction is performed on the crop disease images to obtain features containing disease spatial information. These features are input into an information fusion and communication module for modeling to obtain disease semantic features. The disease semantic features and spatial features are fused through a multi-scale information fusion module and input into a multi-label prediction and decoding module to obtain a multi-label classification result; 4. iteratively training the multi-label classification network; and 5. testing the multi-label classification network. This invention can equally identify crop species, diseases, and severity. The multi-label classification approach combines the advantages of a multi-task network with the low classification complexity of a single-task network and the lack of additional branches.

The invention provides an unsupervised domain adaptive crop variety identification method based on information entropy and a remote sensing device, and relates to the technical field of crop fine segmentation, and the method comprises the steps: constructing an unsupervised domain adaptive crop variety identification network comprising a generator, a main domain discriminator and an auxiliary domain discriminator; the generator obtains spatio-temporal characteristics of the source domain and the target domain based on the four-dimensional time sequence remote sensing images of the source domain and the target domain, predicts a probability graph, and calculates a weighted self-information distribution graph based on information entropy according to the prediction probability graph; sending the weighted self-information distribution map into a main domain discriminator, and sending the spatial-temporal characteristics into an auxiliary domain discriminator to obtain a domain prediction probability result and a domain prediction confidence coefficient; and optimizing the identification network, the main domain discriminator and the auxiliary domain discriminator to realize domain adaptation. According to the method, the information entropy thought and the two domain discriminators are utilized to minimize the difference of inter-domain feature distribution, so that the common representation of the same kind of crops in different domains is more accurately captured and utilized, and the crop variety identification performance under the condition of no label in the target domain is improved.

The invention discloses a crop determination method and system based on combination of a scene recognition model and target detection, and belongs to the technical field of agricultural information, and the method specifically comprises the steps: collecting crop image data, recording time-space information, and carrying out the preprocessing of a crop image; the method comprises the following steps: extracting crop image features, constructing a scene classification model and a target crop detection model, taking a to-be-detected image as input, firstly determining a scene category through the scene classification model, and then loading corresponding target crop detection model optimization parameters for detection; and carrying out weighted fusion on confidence scores of target detection and scene classification, matching with a crop growth cycle model database in combination with spatio-temporal information, and finally determining a crop variety and outputting a result. And the accuracy and efficiency of crop identification are improved.

The utility model relates to agricultural ploughing technology field, and disclose a kind of ploughing equipment for agricultural planting, the ploughing equipment for agricultural planting, including trolley, the top end surface of trolley is fixedly connected with hydraulic cylinder, the output of hydraulic cylinder is fixedly connected with flat plate, the bottom of flat plate is provided with ploughing assembly.This ploughing equipment for agricultural planting is set up ploughing assembly, ploughing roller carries out ploughing to soil, and soil is pushed apart to both sides by soil frame, while ploughing, fertilizer cylinder is matched with rotary drum, just can synchronously scatter fertilizer into ditch, realize ploughing and fertilization integration, substantially reduce the repeated labor of farmer, improve field operation speed, change the volume of containing cylinder, realize the adjustment of fertilizer spreading amount, farmer can flexibly adjust according to crop type, soil fertility, fertilization demand, accurately control the amount of fertilizer, can avoid overfertilization once, also can avoid too little, adapt to different crops and growth stage.

The invention relates to the technical field of information, in particular to an aerial image-based scattered crop planting area identification method and system. The method comprises the following steps: receiving a plurality of aerial images shot by an unmanned aerial vehicle, and aligning the aerial images to a regional map; according to the identification of the aerial image, partitioning the regional map and marking the purpose type of each partition to obtain the area of the partition; reading blocks corresponding to application types and planted crops, obtaining image areas of a plurality of aerial images corresponding to the blocks, and trying to identify crop types; when the crop types corresponding to the blocks are not identified, obtaining a plurality of possible crop types corresponding to the blocks; reading a pre-configured identification model corresponding to the various possible crop types, and inputting the image region into the identification model to obtain the crop types of the image region; and aggregating the identified blocks of the same crop species to obtain the planting area of each crop species.

The present application relates to the technical field of agricultural ecological accounting, in particular to a farmland system phosphorus balance accounting method and system, wherein the farmland system phosphorus balance accounting method comprises the following steps: S1: obtaining farmland system phosphorus input and phosphorus output data; S2: based on the farmland system phosphorus input and phosphorus output data, establishing a phosphorus input, phosphorus output and phosphorus balance accounting database; S3: based on the accounting database, evaluating the farmland system phosphorus balance. In view of the problems of weak phosphorus management of farmland systems in many regions and insufficient research on utilization efficiency, the method and system can estimate the phosphorus balance of farmland systems in different regions, different years and different crop types, which has important significance for scientific management of farmland system phosphorus input, improvement of phosphorus fertilizer utilization efficiency and control of agricultural non-point source pollution.

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 invention relates to the technical field of rainwater recycling, in particular to an intelligent rainwater recycling system and method for facility agriculture. Comprising a greenhouse body covering a planting area. The rain guiding and adjusting device is mounted at the top of the shed body and used for guiding and adjusting the flow direction of rainwater; the rainwater driving device is used for driving the rainwater guide adjusting device to act; a rainwater collecting device; the sensing unit is used for detecting environmental parameters inside and outside the greenhouse in real time; the processor stores types of planted crops and demand information of different growth stages, and the processor is used for calculating utilization demand parameters of rainwater according to the crop types, the growth stage information and the environmental parameters detected by the sensing unit and controlling the rainwater driving device based on the parameters. The working state of the rain guide adjusting device is adjusted so that rainwater can be collected in the shed body area according to needs. Rainwater collection and reasonable distribution of soil scouring of crops in the greenhouse are achieved.

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 invention relates to a crop seed high-frequency pulse processing device and method. The crop seed high-frequency pulse processing device comprises a conveying frame. The electric rollers are rotatably mounted at the two ends of the conveying frame, the variable-frequency and variable-speed motor is mounted on the front side face of the conveying frame, the belt supporting roller is rotatably connected to the inner side of the conveying frame, the conveying belt is arranged on the surfaces of the two electric rollers in a sleeving mode, the sealing cover is arranged at the upper end of the conveying frame, and the two hanging rods are mounted on the sealing cover. The leveling assembly is mounted on the closed hood; the cabin door automatic opening and closing assemblies are mounted at openings in the left end and the right end of the closed hood; a high-intensity alternating electric field, trace ozone sterilization and full-spectrum illumination are organically combined in the closed treatment cabin, the physiological activity of seeds is activated through the synergistic effect of multiple physical fields, the water absorption and stress resistance are enhanced, the germination rate and the seedling emergence consistency are improved, and by matching with an intelligent operation interface, the seed germination efficiency is improved. One-key selection of crop types, automatic calling of processing parameters and remote monitoring of operation states are achieved, and the purpose of efficient, accurate and convenient modernized seed processing is achieved.

The invention relates to the technical field of agricultural ecological accounting, in particular to a farmland system phosphorus balance accounting method and system. The phosphorus balance accounting method for the farmland system comprises the following steps: S1, acquiring phosphorus input and phosphorus output data of the farmland system; s2, establishing a phosphorus input, phosphorus output and phosphorus balance accounting database based on phosphorus input and phosphorus output data of a farmland system; and S3, evaluating the phosphorus balance of the farmland system based on the accounting database. Aiming at the problems of weak phosphorus management, insufficient utilization efficiency research and the like of farmland systems in many regions at present, the method and the system can estimate the phosphorus balance of the farmland systems in different regions, different years and different crop types, and have important meanings for scientifically managing the phosphorus investment of the farmland systems, improving the utilization efficiency of phosphate fertilizer, controlling agricultural non-point source pollution and the like.

The invention discloses a drought monitoring method and system based on coupling of crop types and multi-dimensional drought characteristics. The method comprises the following steps: acquiring and preprocessing multi-source remote sensing data; based on high-resolution remote sensing data, farmland plots are extracted in combination with terrain and road network data; combining optical remote sensing data to extract crop time sequence characteristics of the farmland plot; inputting the crop time sequence characteristics into a pre-trained crop identification model; constructing a specific three-dimensional drought index model of different types of crops based on the spatial distribution of various types of crops; and according to the specific three-dimensional drought index model, fusing a soil moisture dynamic threshold value and remote sensing drought loss inversion data to generate a crop specific drought risk grading map. According to the method, different crop types can be accurately distinguished, the difference of different crops in the aspects of water demand characteristics and drought response is fully reflected, and refined monitoring and evaluation of drought conditions of different crops are realized.

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 utility model relates to the technical field of transmission cases, and discloses a braking multi-gear transmission case and a harvester. The multi-gear transmission case capable of being braked comprises a case body, an input flange plate, an input shaft assembly, a sliding gear, a gear shifting mechanism assembly, an output shaft, a plurality of variable gears and a brake, the multi-gear transmission case capable of being braked and the harvester break through the limitation of fixed output of a traditional transmission case, different output gears and neutral gear settings are introduced, and the transmission case capable of being braked and the harvester can be braked more stably. According to the transmission case, a driver can flexibly regulate and control the output speed of the transmission case directly through gear shifting operation in a cab according to actual operation requirements, the adaptability of the harvester under different crop types, soil conditions and operation stages is greatly improved, the operation complexity is effectively reduced, and the working efficiency and the operation safety coefficient are improved.

The application relates to the field of crop data management, and discloses a crop growth data management method and device, a computing device and a storage medium. The method comprises the following steps: acquiring an initial fertilization area division scheme; adjusting the initial fertilization area division scheme according to demand data corresponding to a target crop type to obtain a final fertilization area division scheme; determining target fertilization data of a plurality of fertilization areas; collecting target crop growth data of the plurality of fertilization areas fertilized according to the final fertilization area division scheme; recording the target fertilization data and the target crop growth data, and adjusting a fertilization plan according to the target crop growth data. According to the application, different fertilization areas are divided according to soil conditions and crop demand, the fertilization data and the crop growth data of the fertilization areas are analyzed and processed, the fertilization plan is further adjusted, and the effect of fertilization is enhanced and the yield of crops is improved through planning and management.