58 results about "Corn field" patented technology

The invention discloses a corn field weed detection method and device based on improved YOLOv11. The method comprises the following steps: acquiring and constructing a weed image data set for model training; replacing a backbone network of the YOLOv11n baseline network with a double-flow visual network, and taking the replaced network as a first optimized network; replacing the original pyramid pooling module with an enhanced receptive field module at the tail end of the backbone network of the first optimized network to obtain a second optimized network; in a neck feature fusion layer of the second optimization network, a self-adaptive context guide fusion module is used for replacing traditional splicing, and a third optimization network is obtained; and taking the third optimization network as a final lightweight high-precision weed detection model DEA-YOLO11, and detecting field weeds based on the model. According to the method, the problem of low detection precision caused by insufficient global context sensing capability, fine-grained feature loss and low multi-scale feature fusion efficiency of a weed detection model in a complex agricultural scene in the prior art is solved.

The invention discloses a corn disease recognition method based on an improved generative adversarial network. The corn disease recognition method comprises the steps that S1, an initial low-resolution image of a corn field is collected and obtained through an unmanned aerial vehicle; s2, performing super-resolution reconstruction on the initial low-resolution image by using an improved generative adversarial network model; the model construction comprises the following steps: S2.1, constructing a shallow feature extraction layer; s2.2, constructing a deep feature extraction network based on a plurality of RRDB nested residual dense blocks; s2.3, constructing an attention module based on a space and channel dual attention mechanism; s2.4, a multi-scale texture enhancement module is constructed through multi-scale convolution and smooth branches; s2.5, constructing a global residual connection layer; s2.6, constructing an adaptive hybrid up-sampling module based on transposed convolution and stable up-sampling; s2.7, performing mapping output on the features after up-sampling; and S3, carrying out disease prediction on the high-resolution reconstructed image. According to the method, details such as spatial resolution and texture of the unmanned aerial vehicle high-altitude flight remote sensing image are improved, and then the corn disease monitoring precision is improved.

The invention discloses a nicosulfuron-containing herbicidal composition and application thereof. The herbicidal composition is prepared from the following components: nicosulfuron, amicarbazone, and dimethyltetrachloroisooctyl ester as well as a dispersant, an emulsifier, a wetting agent, a thickening agent, an antifreeze, and a dispersion medium, wherein the dosage form belongs to a dispersed oil suspension agent. Through the combination of the three active ingredients, the herbicidal composition has an obvious synergistic effect, the use amount of pesticides is reduced, the cost and environmental pollution are reduced, the drug resistance of corn field weeds can be delayed by the application of the composition, and the control spectrum is expanded.

The utility model relates to a corn field operation seeder capable of adjusting plant spacing. The device comprises a rack, a ditching mechanism, a sowing mechanism, a soil covering disc and a pressing mechanism, the ditching mechanism is installed at the front end of the rack, and seed ditches can be formed through the ditching mechanism; the seeding mechanisms are mounted on the rear sides of the ditching mechanisms and are in one-to-one correspondence with the ditching mechanisms; the soil covering disc is mounted on the rear side of the seeding mechanism, and soil covering can be performed on the seed furrows through the soil covering disc; the pressing mechanism is installed on the rear side of the soil covering disc, and covering soil can be pressed through the pressing mechanism. The scheme provided by the utility model is simple in structure, low in manufacturing cost and suitable for small-scale corn seeding, and manual seeding is replaced, so that the labor intensity of workers is reduced, and the seeding plant spacing can be adjusted.

The dropper fertilization device comprises a filtering assembly, the filtering assembly comprises a water storage tank, the top of the water storage tank is connected with a feeding port, a filtering cylinder is installed on the inner wall of the top of the water storage tank, a plurality of sets of filtering holes are formed in the filtering cylinder in sequence, and the feeding port is matched with the filtering cylinder in position. The top of the water storage tank is connected with a water adding opening which is matched with the filter cartridge in position, an adjustable-speed motor is installed on the top of the water storage tank, and the output end of the bottom of the adjustable-speed motor is connected with a transmission shaft. The filter cartridge arranged on the filter assembly can be used for filtering when a fertilizer mixed solution is mixed, impurities can be effectively filtered, the problem that a dripping hole is blocked when the fertilizer mixed solution is conveyed due to the fact that the impurities cannot be filtered out without a filter device in a traditional mode is solved, and normal fertilization is prevented from being influenced; the scraping plate is attached to the inner wall of the filter cartridge to rotate, so that the filter cartridge can be cleaned, impurities attached to the inner wall of the filter cartridge are scraped, and the condition that filter holes are blocked is avoided.

The invention discloses a corn field nitrogen element visualization method and platform based on an unreal engine, and relates to the technical field of intelligent agriculture. The method comprises the following steps: firstly, obtaining and processing nitrogen element three-dimensional data of a target plot; establishing a three-dimensional space model of the target plot in the unreal engine; and importing the three-dimensional data of the nitrogen elements into the three-dimensional space model, and respectively driving particle systems which represent different colors of spatial distribution conditions of different types of nitrogen elements to realize visualization of the nitrogen elements in the three-dimensional space model. According to the method, the multi-category nitrogen element three-dimensional data is obtained through obtaining and processing, and visual and diversified interactive presentation is performed based on the unreal engine, so that comprehensive and efficient utilization of the collected nitrogen element data is realized, and beneficial effects of visualization in aspects of data analysis and management decision are exerted.

The invention discloses a compound herbicide and a preparation method thereof and relates to the technical field of pesticides. The compound herbicide is compounded by taking rimsulfuron, pyroxsulam and fluroxypyr-mepthyl as effective components and supplementing a safener, a surfactant, an emulsifier, a stabilizer and a solvent. The action mechanisms of the three active ingredients are complementary and synergistic, and annual gramineous weeds and broadleaf weeds in a corn field can be effectively prevented and removed. Through scientific compounding, the application dosage of a single component is remarkably reduced, pesticide residues in the environment and crops are reduced, meanwhile, potential phytotoxicity to the crops is effectively relieved by adding the safener, and the bactericidal composition has the advantages of being efficient, safe and environmentally friendly.

The application belongs to the technical field of pesticide weeding, and discloses a weeding composition containing icafolin and application thereof. The weeding composition contains active ingredient A and active ingredient B. The active ingredient A is icafolin, the active ingredient B is any one of benzenesulfonamides or thiophenesulfonamides, and the mass ratio of the active ingredient A to the active ingredient B is 1:2-200:4.5. The weeding composition provided by the application has high weeding activity, and the mixture of the two has superimposition effect, complementary mechanism, and an expanded weed spectrum. The weeding composition has good weed control effect in corn fields, wheat fields, peanut fields and soybean fields.

The disclosed apparatus, systems and methods relate to a physical stalk sensing system comprising at least one resilient member. Sensors having the resilient member or members are able to estimate the size of the stalks of row crops as they pass through a field, such as a corn field. The sensors can be mounted on a corn head and the results can be analyzed and visualized. A system for predicting crop yields in real-time or near real-time. A system for adjusting a stripper plate gap.

The invention discloses a crop growth detection system for agricultural cultivation, and relates to the technical field of growth detection, and the system comprises a triangular ridge line generation module which is used for generating a triangular ridge line based on a remote sensing image of a furrow-ridge film-covered corn field; the wet printing point extraction module is used for generating wet printing points based on the remote sensing image and defining a pseudo line center line based on the wet printing points and the triangular ridge line; the forbidden band construction module is used for constructing a forbidden band region based on the pseudo row center line; the seedling candidate detection module is used for determining candidate seedling points based on the prohibited zone area; and the target seeding line determination module is used for determining a target seeding line based on the triangular ridge line, the wet printing points and the candidate seedling points. According to the invention, the stability and spatial purity of crop seedling point detection are improved.

The utility model relates to the technical field of fertilization and irrigation, in particular to a double-channel fertilizer mixing drip irrigation corn field combined application device, which is characterized in that fertilizer conveying pipes are respectively and fixedly arranged at the bottoms of two fertilizer storage barrels, and flow control valves are arranged on the fertilizer conveying pipes and are in electric control connection with a controller; the mixing cylinder is fixedly arranged on the supporting frame and located in the middle position below the two fertilizer storage cylinders, one ends of the two fertilizer conveying pipes are connected with the mixing cylinder, the bottom of the mixing cylinder is connected with a drip irrigation pipeline through a conveying pipeline, the motor is fixedly arranged on the upper portion of the mixing cylinder, and a stirring shaft is rotatably inserted into the inner top wall of the mixing cylinder through a bearing. A first stirring paddle is fixedly arranged at the lower end of the stirring shaft, and an efficient mixing mechanism connected with the stirring shaft is arranged in the mixing cylinder, so that precise mixing and combined application of two fertilizers can be realized, the utilization rate of the fertilizers is increased, the growth requirements of corn are met, and the fertilizers can have better mixing effect and efficiency.

The invention belongs to the technical field of agricultural machinery technology and agricultural technology popularization and application, and discloses a pest control device for agricultural technology popularization, which comprises a vehicle body, a stirring device is arranged in the vehicle body, and a swing structure and an automatic steering device are arranged on the outer side of the vehicle body. According to the invention, an additional stirring motor is not needed, the advancing power of the driving wheel is directly utilized for linkage stirring, and the energy consumption of the equipment is reduced by about 20%; the two-way reverse rotating stirring rod can avoid precipitation and layering of missible oil and suspending agents, the mixing uniformity is improved by 25% compared with one-way stirring, it is ensured that the concentrations of the pesticides at all parts of crops are consistent after spraying, and the pest control effective rate is improved by 15%; the spraying head swings by 35 degrees in a reciprocating manner, so that the covering area of single spraying is increased by 40% compared with that of a fixed spraying head; in the operation of 1 mu of corn field, the consumed time is shortened from 20 minutes to 12 minutes, and the operation efficiency is improved by 40%; and through the limiting design of the arc-shaped groove, the swing track deviation of the sprinkler head is smaller than or equal to 2 degrees, the spraying leakage rate of an edge area is reduced to 5% or below, repeated operation is avoided, and actual application and operation are facilitated.

The application discloses a corn field pest identification method based on underground pest image feature recognition. The method effectively suppresses uneven internal soil illumination through homomorphic filtering, highlights pest edge details, improves detection stability, introduces causal feature decoupling and intervention learning, makes the model learn the morphological characteristics of pests rather than background pseudo-correlation, maintains high recognition rate under different soil types and humidity conditions, and fuses near-infrared texture features, uses the spectral difference between biological tissues and soil in the near-infrared wave band, and enhances the distinguishing ability of pests and sundries with similar colors.

The invention provides a lightweight corn-weed accurate detection algorithm based on improved YOLOv10s, and the algorithm comprises the steps: collecting corn seedling and weed images, and constructing a corn field weed data set; an improved lightweight corn-weed detection model of YOLOv10s is constructed, a D-PP-HGNet lightweight network is constructed to replace an original trunk part of the YOLOv10s model, and an improved RCS-OSA module is introduced into a neck network. Using the corn field weed data set to train and evaluate the improved lightweight corn-weed detection model of YOLOv10s, and using the improved lightweight corn-weed detection model of YOLOv10s after training and evaluation to perform classification detection on to-be-detected corn seedlings and weed images. The technical problems that an existing algorithm is low in recognition accuracy, high in calculation intensity and difficult to deploy at a mobile terminal are solved.

The utility model provides a portable device for releasing noctuid black egg bees in a corn field, which comprises a release box and a vertically arranged support rod, a box cap is arranged above the release box, and the box cap is connected with the release box through a screw fixing port; one corner of the box cap is movably installed on the supporting rod and can move up and down along the connecting rod. According to the portable device for releasing the noctuid black egg bees in the corn field, the problems that when the noctuid black egg bees are released in a large scale in the field, the eclosion rate of the noctuid black egg bees is reduced due to the damage of rainfall, direct sunlight and other natural enemy organisms and the over-high field temperature are solved; secondly, the device can be repeatedly used for 5-10 years, so that the cost is reduced; in addition, the device is small in size, light in weight and convenient to carry, transport and store.

This invention discloses a corn planting machine, aiming to solve the problems of high labor costs, low operating efficiency, poor planting accuracy, and low seedling survival rate in traditional corn planting. The planting machine mainly consists of a seed box, a push handle, transmission gears, a press wheel, a pulley assembly, a furrow opener, and traveling wheels. The push handle allows the operator to grip and control the entire machine. The traveling wheels provide walking power and transmit force to the transmission gears, which drive the pulley assembly to operate the seed dispensing mechanism inside the seed box. The furrow opener opens seed furrows at the front, and the press wheel compacts the soil at the rear. This invention, through the coordinated operation of its components, achieves orderly operation throughout the entire corn planting process, reduces manual labor intensity, improves planting accuracy and efficiency, ensures a good foundation for seed growth, and is suitable for large-scale corn field planting scenarios.

The embodiment of the invention provides a construction method and system of a knowledge graph of silage corn and a storage medium, and belongs to the technical field of knowledge graphs. The method comprises the following steps: extracting a context statement of a candidate entity, and obtaining a confidence coefficient of the candidate entity for a certain entity type; calculating the vector similarity between the vector of the context statement and the corresponding entity type vector; performing weighted calculation on the confidence coefficient and the vector similarity to obtain an output probability that the type of the candidate entity is a certain entity type, and selecting the candidate entity corresponding to the maximum value of the output probability as an output entity; and mapping the output entities and the relation mapping rule base to obtain an entity relation, and obtaining the knowledge graph according to the output entities and the entity relation. Through the method, the entity recognition capability and extraction performance of the entity extraction model in the field of silage corn are effectively improved.

The application discloses a silage corn moisture content online detection system and method, and belongs to the technical field of online detection of agricultural equipment, machine vision and near-infrared spectrum analysis. The system comprises a near-infrared spectrum acquisition module, a visual perception module, a synchronous corresponding module and a data processing output module. The method is to synchronously acquire near-infrared spectrum and image data of materials in a throwing pipeline of a silage harvester and to time-align the data. A stem and leaf mask is extracted through an instance segmentation network to calculate a component area ratio, and a leaf yellowing rate is extracted based on an HSV color space. After being preprocessed and batch corrected, the spectrum is input into a first prediction model to obtain an initial moisture content. The component ratio and the yellowing rate are used as correction features, and a two-stage regression correction model is used to fuse and output a final moisture content. The application solves the problem of large detection error caused by non-homogeneous materials, maturity difference, batch drift and dust interference, has high detection precision and strong robustness, and is suitable for online real-time detection of silage corn in a field.

The invention provides a corn drought image classification method and device based on an improved Swin Transform network, and the method comprises the steps: S1, obtaining a multi-source corn field visible light image, and constructing a corn drought image data set containing different drought stress degrees and different growth stage combinations; s2, a Swin Transform network is improved, and an MDFA module is connected in series, so that an SWT-MDFA classification network is constructed; s3, training the SWT-MDFA classification network by using the corn drought image data set to obtain a trained corn drought image classification model; and S4, inputting a to-be-classified corn field visible light image into the corn drought image classification model, and outputting a pair result. The method solves the problems that an existing corn drought image classification method is difficult to consider phenotypic differences of different growth stages of corn and cooperative discrimination of leaf detail changes and plant overall states in field images, and is interfered by complex field environments such as large scale differences of multi-source field images, illumination changes and disordered backgrounds; and therefore, the technical problem of insufficient identification accuracy and stability can be solved.

The invention provides a corn field density gradient test design method, which comprises the following steps of: firstly, setting a circular total test area with a radius of R, the lowest planting density Dmin and the highest planting density Dmax according to test conditions and requirements, and sequentially arranging an inner blank area, an inner protective belt, an effective test area and an outer protective belt from the center of a circle to the outside, then calculating the radius r of the inner blank area and the outer row spacing HR of the effective test area, delimiting the inner blank area according to r, delimiting the effective test area into a plurality of planting rows along the radius direction according to HR, and finally sowing along the planting rows at equal plant spacing. According to the method, the perfect density gradient with constant row spacing and gradually reduced row spacing along with the geometric structure is designed by utilizing the annular geometric structure with wide outside and narrow inside, and the method can be widely applied to the corn close planting related theoretical research and the accurate measurement and calculation of the most suitable popularization density of new varieties.

The invention discloses a saline-alkali soil corn growth index dynamic monitoring method based on unmanned aerial vehicle multispectral remote sensing, relates to the technical field of unmanned aerial vehicle remote sensing, and aims to realize efficient monitoring of corn growth conditions by synchronously acquiring multispectral images and three-dimensional point cloud data by using an unmanned aerial vehicle remote sensing platform. The method comprises the following steps: acquiring saline-alkali grade and growth period information of a target corn field block, calculating a salinity index and a vegetation index based on a multispectral image, and extracting canopy structure parameters through three-dimensional point cloud data. A corresponding target monitoring model is called from a preset monitoring model library, a corn growth index prediction value is generated in combination with salt and alkali grades and growth period information, and finally a growth condition grading early warning graph based on the prediction value is formed. According to the method, the growth difference of the corn under different saline-alkali soil conditions can be accurately identified, effective decision support is provided for farming intervention, and the method has high practical application value.

The application provides a corn field phenotype group data dynamic acquisition method and system, and relates to the corn planting data acquisition technical field.The method comprises the following steps: identifying macroscopic phenotype changes indicated in macroscopic phenotype data;querying a preset corn phenotype characteristic map according to the identified macroscopic phenotype changes;determining specific data acquisition parameters or parameter combinations for obtaining diagnostic microscopic characteristics according to the query result;adjusting sensor configuration and acquisition platform behavior of an automatic data acquisition device by driving the automatic data acquisition device according to the specific data acquisition parameters or parameter combinations to obtain diagnostic microscopic characteristic data;analyzing the diagnostic microscopic characteristic data, and classifying the macroscopic phenotype changes according to a preset diagnostic characteristic mode in the corn phenotype characteristic map.The method of the application overcomes the inefficiency of the existing general fine acquisition mode, and significantly improves the effectiveness and diagnostic accuracy of field phenotype group data acquisition.

The invention relates to the technical field of corn canopy nitrogen content monitoring, and discloses a corn canopy nitrogen content monitoring method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source remote sensing data of a target corn field area, the multi-source remote sensing data comprises a multi-spectral image and a hyperspectral reflectivity, the method comprises the following steps: constructing a multispectral vegetation index and a texture index based on a multispectral image, constructing a hyperspectral vegetation index, constructing a mixed feature data set by using the multispectral vegetation index, the hyperspectral vegetation index and the texture index, and carrying out feature screening on the mixed feature data set by adopting an LASSO regression algorithm to obtain an optimal feature combination; and training a random forest model through a training sample set formed by the optimal feature combination and a corresponding corn leaf nitrogen concentration measured value to form a corn canopy nitrogen content monitoring model, and obtaining a corn canopy leaf nitrogen content predicted value of the target corn field area through the corn canopy nitrogen content monitoring model. And the nitrogen condition of the corn plant in the target area can be effectively estimated.

This utility model relates to the field of drip irrigation technology and discloses a drip irrigation device for corn fields. It includes two drip irrigation pump boxes, with a drip irrigation pipe positioned between them. Multiple drip heads are located at the bottom of the drip irrigation pipe, and multiple collars are fitted around the outside of the pipe. A fixing component is located at the bottom of each collar for securing the drip irrigation device. An anti-clogging component is located at the inner end of the drip irrigation pipe to filter impurities in the water and prevent them from clogging the filtration equipment. In this utility model, a filter screen and impeller cleaning brush effectively prevent impurities in the water from clogging the drip heads and filter plates, ensuring filtration efficiency. Multiple fixing components are installed at the bottom of the drip irrigation pipe. An outer cylinder is inserted into the ground, causing the insertion rod to expand, enhancing the pipe's fixation and preventing bending due to gravity after prolonged use, thus ensuring stable operation of the equipment.

The invention belongs to the technical field of pesticide weeding, and discloses a clomazone-containing weeding composition and application thereof.The weeding composition comprises pyroxasulfone serving as an active ingredient A, clomazone serving as an active ingredient B and prometryn serving as an active ingredient C. The mass ratio of the active ingredient A to the active ingredient B to the active ingredient C is (1-5): (1-60): (1-60). The weeding composition disclosed by the invention has a remarkable synergistic effect on weeds in soybean or corn fields, effectively reduces the pesticide application frequency, saves the weeding cost, delays the drug resistance of the weeds, and is beneficial to controlling rapid replacement of dominant species of field weed communities and maintaining agricultural ecological balance.

The embodiment of the present disclosure provides a high-efficiency corn irrigation device, comprising: a nozzle angle adjusting arm for adjusting the angle of a nozzle, wherein the nozzle angle adjusting arm has a telescopic structure, the bottom of the nozzle angle adjusting arm is provided with a rotatable connecting part, and one side of the rotatable connecting part is provided with a fixed locking mechanism; a water flow distributor arranged on the nozzle angle adjusting arm; a fixed support frame connected below the nozzle angle adjusting arm; an adjustable nozzle arranged at the top of the nozzle angle adjusting arm; and a connecting hose for connecting the water flow distributor and the adjustable nozzle; wherein the connecting hose is made of an elastic bellows pipe and is connected with multiple reinforcing rings in the middle; and the rotatable connecting part of the nozzle angle adjusting arm has a ball joint structure, and the ball joint structure comprises a spherical head and a spherical seat matched with the spherical head. Through the scheme of the embodiment of the present disclosure, the corn field of different terrains can be adapted.

The application relates to the technical field of manipulator control, in particular to an arm servo control method and system for corn field weeding robots, which are used to solve the technical problems of the existing corn field weeding robot arm pointing deviation and low weeding precision. The method comprises the following steps: acquiring time-synchronized field image time series and robot motion data; determining a first position of a weeding target and a collection time based on the field image time series; performing spatial position compensation on the first position based on the robot motion data between the collection time and a target time to obtain a second position; determining a relative motion trend of the weeding target and the robot based on the field image time series, and determining a scene consistency factor based on scene structural features in the field image time series; correcting the second position based on the relative motion trend and the scene consistency factor to obtain a final target position; and generating a servo control instruction to drive the robot to perform weeding work.

The invention relates to the technical field of intelligent irrigation control, in particular to an intelligent irrigation control device and method for a corn field, and the method comprises the following steps: 1, data collection; 2, water demand calculation; step 3, irrigation decision making; step 4, executing irrigation; and step 5, feedback optimization. The device comprises an acquisition module, a processing module, an execution module and a correction module. According to the method, the illumination cumulant is calculated through numerical integration, and the illumination cumulant is used as the input of the digital twinborn model, so that the calculated ET0 is closer to the actual energy receiving condition of the corn canopy, real on-demand irrigation is realized, and excessive waste of irrigation water is reduced.

The invention discloses a weeding composition, the effective components of the weeding composition comprise an active component A, an active component B and an active component C. The active component A is pyroxasulfone, the active component B is isoxaflutole, and the active component C is amicarbazone or terbuthylazine. The weeding composition can be used for corn fields, has a remarkable control effect on various annual or perennial weeds in the corn fields, and has the advantages of being wide in use period, long in lasting period and safe to afterreap crops.

The present application discloses a kind of corn field's herbicidal water dispersible granule, active ingredient includes mesotrione, ametryn and dichlorprop. Compared with the existing single active ingredient preparation, the herbicidal water dispersible granule of the present application adopts triple action mechanism, in addition to having significant herbicidal effect, but also can expand the herbicidal spectrum;Water dispersible granule has high suspension rate, strong hard water resistance, suitable for different regions corn field a variety of malignant weed control, such as barnyard grass, crabgrass, green dogtail grass, senna, amaranthus retroflexus, chenopodium etc., stable efficacy, long persistence, less preparation residue, can significantly reduce the resistance of weed EC 50 , and no phytotoxicity to crops, good safety, in line with the safety requirements of pesticide preparation.