27 results about "Field plot" patented technology

The invention relates to the technical field of intelligent fertilization, and discloses an organic fertilizer fertilization method and system, and the method comprises the steps: obtaining crop fertilizer demand data and soil fertilizer supply data of a target field, and fertilizer supply data and mineralization characteristic parameters of candidate organic fertilizers; performing dynamic nutrient balance analysis on the crop fertilizer demand data and the soil fertilizer supply data to obtain nutrient demand gap data; according to the mineralization characteristic parameters and soil environment factors in the soil fertilizer supply data, deducing a release rate and an accumulated release amount of nutrients in the candidate organic fertilizer converted to an effective state along with growth time so as to fit nutrient release dynamic curve data of the candidate organic fertilizer; performing space-time matching analysis on the nutrient demand gap data and the nutrient release dynamic curve data so as to determine the optimal application amount and the fertilization opportunity; adaptively adjusting the input amount and the application time point of the candidate organic fertilizer; a specific fertilization arrangement is determined and integrated into an organic fertilizer fertilization scheme; the precise fertilization efficiency of the organic fertilizer can be improved.

The invention relates to the technical field of grain loss monitoring, in particular to a wheat harvesting loss monitoring system and method, and the system comprises an unmanned aerial vehicle monitoring module, a multi-mode sensor array, a signal processing module, an intelligent control module and a man-machine interaction terminal. The unmanned aerial vehicle monitoring module is used for scanning a field parcel, identifying a lodging area and a high-density area and generating a loss risk thermodynamic diagram, the loss risk thermodynamic diagram is imported into a harvester navigation system, and the high-risk area is harvested preferentially; the multi-mode sensor array comprises a piezoelectric grain sensor and an image sensor; the piezoelectric type grain sensor is mounted at the tail part of the cleaning sieve, adopts a vibration isolation device (such as a rubber pad) to reduce mechanical vibration interference, and detects a grain impact signal through a piezoelectric element; the image sensor is arranged on a header and a threshing outlet and is used for identifying missed cutting of wheat ears and damaged grains based on a deep learning algorithm; the intelligent control module comprises a loss rate calculation unit and a parameter optimization unit; the precision and the real-time performance are improved.

The application discloses a reverse making method of terraced rice picture and belongs to the field of straw picture, and comprises the following steps: S1, breeding color rice varieties with different color and plant height, and carrying out planting and management according to the characteristics of terraces; S2, designing a plane pattern by using PS and AI; S3, importing the plane pattern into CAD and adjusting the angle and perspective; S4, measuring the position and range of the rice field picture area field plot and the height difference of the field ridge; S5, discharging the whole pattern in the reverse direction of the field ridge by using the Ovi interactive map, and adjusting the position and angle of the field ridge in the picture to be optimal; and S6, placing the converted pattern in the rice field by using a positioning instrument and placing and planting. The terraces are curved, the field ridge is high, the field plot surface is small, and the rice field picture cannot be made like the basin or the plain, the application adopts a reverse terraced picture making technology, the technology overcomes the disorder and the broken feeling caused by the large height difference of the terraces, the small field plot and the large bending degree, and improves the overall viewing effect of the picture.

The invention relates to the technical field of plant breeding, in particular to an asexual high-yield coffee breeding method which comprises the following steps: screening superior plants to obtain a core stock plant, and preparing an asexual propagation material based on the core stock plant; carrying out seedling culture on the vegetative propagation material to obtain primary clonal seedlings, and carrying out seedling stage identification on the primary clonal seedlings to obtain healthy clonal seedlings; performing a field plot test on the healthy clonal seedlings to obtain candidate clones, and performing a multi-point regional test on the candidate clones to obtain stable clones; according to the method, asexual propagation technical parameters are optimized, composite hormone treatment is combined with a temperature control intermittent spraying technology for cutting seedling raising, and the rooting rate is larger than or equal to 80%; the tissue culture technology adopts an optimized culture medium formula, the propagation coefficient is greater than or equal to 5 times, the consistency is greater than or equal to 99%, and the problems of low propagation coefficient and low rooting survival rate of the traditional asexual propagation technology are solved.

The application discloses a rice and wheat lodging three-dimensional attribute detection and distribution map construction method for a combine harvester, an electronic device and a computer readable storage medium. The method uses a vehicle-mounted binocular vision to obtain field images and depth information, segments a lodging area through a deep learning model, and then calculates three-dimensional attributes such as the area, position, angle and direction of the lodging area. Through displacement-based key frame screening and overlap degree judgment, multiple frame detection results are fused in a global coordinate system, and finally a spatial distribution map reflecting the overall lodging condition of a field plot is constructed, thereby providing a precise sensing basis for intelligent operation of the combine harvester.

The invention provides a tobacco field straw dissolving progress monitoring method, electronic equipment and a storage medium, and relates to the field of data processing, and the method comprises the steps: obtaining the preceding crop information of a tobacco field plot according to a first image sequence, obtaining the irrigation information of the tobacco field plot according to a second image sequence, and obtaining the tobacco field dissolving progress according to the preceding crop information and the irrigation information; and field dissolving reminding is carried out on the tobacco field parcels meeting the preset conditions, so that the straw field dissolving process is monitored, and the monitoring efficiency is improved.

1. Name of the product in this design: Field Ridge. 2. Purpose of this design: For the longitudinal and transverse division of field plots. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 1.

The invention provides a tobacco plant missing positioning method, electronic equipment and a storage medium, and relates to the field of data processing, and the method comprises the steps: obtaining a vector point set of tobacco plants and vector line boundary data of a to-be-detected tobacco field plot, vector points in the vector point set being vector points corresponding to each tobacco plant in the to-be-detected tobacco field plot; obtaining a vector point total set according to the vector point set; the vector point closest to each vector point in the vector point total set and the vector point closest to the vector point in the opposite direction are connected to generate vector lines, the length of each vector line is obtained, if the length of the vector line is larger than a length threshold value, it is considered that the position where the vector line is located is the tobacco plant missing position, and the searching efficiency is improved.

The invention discloses a Chinese wolfberry cultivation method for improving paddy field soil, which comprises the following steps: a, paddy field soil improvement: selecting a paddy field plot, digging a planting ditch, deeply digging for 40-100cm downwards, loosening soil, mixing organic fertilizer, and breaking the original plow pan of the paddy field; after loosening the soil, fermenting for 25-35 days; b, soil preparation and fertilization: before transplanting the wolfberry seedlings, leveling the fermented land, raking the land to be fine, ridging, and applying a ternary compound fertilizer at the same time; an operation ditch is arranged between the two ridges; every two ridges form a group, a drainage ditch is arranged between every two adjacent groups, and surrounding ditches are arranged around; c, transplanting Chinese wolfberry seedlings: transplanting the cultivated Chinese wolfberry seedlings into each ridge according to the plant spacing of 0.8-1.2 m, and thoroughly watering after field planting; d, drip irrigation cultivation: performing drip irrigation with water every 3-5 days; applying microelements to promote rooting during the period; the fruit can be obtained in about half a year. According to the wolfberry cultivation method, the improved paddy field soil is adopted for planting, the survival rate is high, the cultivation period is short, cost is low, and the problem that in the prior art, paddy field soil is not suitable for wolfberry growth is solved.

The application provides a tobacco field rice straw field dissolving progress monitoring method, an electronic device and a storage medium, and relates to the field of data processing. The method comprises the following steps: obtaining information of a previous crop of a tobacco field plot according to a first image sequence, obtaining irrigation information of the tobacco field plot according to a second image sequence, and reminding the tobacco field plot that meets a preset condition to dissolve the field according to the information of the previous crop and the irrigation information. The method realizes monitoring of the rice straw field dissolving progress and improves the monitoring efficiency.

The invention discloses a grassland field quadrat investigation device and method. The investigation device comprises a plant three-dimensional information acquisition box and a camera. The plant three-dimensional information acquisition box comprises a camera bracket and four groups of light-blocking illumination curtains; the camera support is of a frame structure. Each group of light-blocking illumination curtains comprises a side wall shading outer sleeve, a top end shading outer sleeve, a light-emitting part and a dodging lining; the top end shading outer sleeve, the light-emitting piece and the dodging lining are sequentially connected to form a top film. The top film is connected with the side wall shading outer sleeve; the four side wall shading jackets sequentially cover the four side faces of the camera support, and the four top films cover the top end of the camera support. The camera is arranged in the plant three-dimensional information acquisition box; the survey device provided by the invention can reduce the professional skill threshold, improve the survey efficiency of the grassland quadrat, and ensure the accuracy of the collected data, and the collected data can be archived and rechecked for a long time.

The invention provides a corn base fertilizer variable fertilization prescription map generation method, system, device and medium, and the method comprises the following steps: obtaining multi-source spatial data of a target field, rasterizing the multi-source spatial data, and generating a plurality of influence factor grid layers corresponding to spatial positions; performing standardization processing on the plurality of influence factor grid layers, and adopting an information entropy-based weighting method to distribute an initial weight for each influence factor; dynamically correcting the initial weight based on the spatial correlation between each grid unit and the neighborhood thereof by using a spatial domain Kalman filtering algorithm, and generating an evaluation grid map; and carrying out space partitioning based on the comprehensive evaluation grid map to obtain a fertility management partition map. According to the technical scheme provided by the invention, accurate distribution of the fertilizer according to needs can be realized.

This invention discloses a method, apparatus, and device for field extraction based on multi-task deep learning. The method includes: acquiring remote sensing time-series images of the phenological stages of agricultural crops within a region to be identified; inputting the remote sensing time-series images of the phenological stages of agricultural crops within the region to be identified into a trained farmland field extraction framework for prediction, thereby obtaining farmland field identification results; wherein, the farmland field extraction framework is trained based on a remote sensing time-series image training set, which is a dataset within the study area after data preprocessing. This invention solves the problems of poor accuracy and low efficiency in field identification.

Provided is an unmanned radish harvesting system, relating to the field of agricultural artificial intelligence technologies. The system, using an unmanned radish harvesting control subsystem, an unmanned radish harvesting operation subsystem, and a radish agronomic parameter configuration subsystem, allows for intelligent control of the movement of a radish harvester and optimal operation path planning by integrating the shape and terrain of a vegetable field plot and planting agronomic practices. On the basis of various parameter configurations, a computer vision algorithm is employed to accurately identify radish fruits and radish tops. The unmanned radish harvesting system realizes more intelligent, delicate, and accurate operations of the unmanned radish harvester, thereby saving human resources, material resources, and time, and improving the efficiency and quality of unmanned radish harvesting as well as the green and intelligent levels of unmanned radish harvesting.

The invention relates to the technical field of agricultural sowing equipment, and discloses a single coated seed sowing device and a seed-by-seed sowing method. The device comprises a seed cylinder, a feeding hopper, a sliding block type grain separating mechanism, a seed separating disc mechanism, a discharging hopper, a shell and a circuit module which are sequentially arranged from top to bottom, and the problems that coated seeds are prone to adhesion and blockage and multiple seeds are sown are solved in a targeted mode through the integrated design of filtering and screening, two-time grain separating and automatic control. The method comprises the orderly steps of screening, primary grain separation, secondary grain separation, real-time counting and single grain output. The device is compact in structure, portable, easy to use, high in seeding precision, high in efficiency, capable of counting in real time and suitable for laboratory germination rate detection, field plot tests and other scenes, the labor intensity is effectively reduced, and the small-batch high-precision seeding requirement is met.

The invention discloses a tobacco leaf yield and quality evaluation method and system fusing unmanned aerial vehicle multispectral and a WOFOST model. The method comprises the steps of obtaining unmanned aerial vehicle multispectral data of a tobacco leaf growing period; preprocessing the unmanned aerial vehicle multispectral data to obtain canopy characteristic parameters of the tobacco leaves; a WOFOST model is calibrated through the canopy feature parameters of the tobacco leaves, and the calibrated WOFOST model is obtained; inputting meteorological data and field management data of the target field into the calibrated WOFOST model to obtain growth process parameters output by the model; inputting the growth process parameters into a pre-trained mapping model to obtain a tobacco yield and quality evaluation result output by the model; according to the method, the unmanned aerial vehicle multispectral data is combined with the WOFOST model, so that the growth condition and the quality change of the tobacco leaves are comprehensively reflected, the defect that the growth condition of the tobacco leaves is difficult to comprehensively reflect by single remote sensing data is overcome, and the accuracy and the comprehensiveness of tobacco leaf yield and quality evaluation are improved.

The application discloses a zero-sample field plot segmentation method for complex farmland landscape, and relates to the technical field of remote sensing image intelligent interpretation and agricultural informatization. The method comprises the following steps: acquiring a target region RGB remote sensing image and cutting the image into a preset size image set, segmenting the image set through a SAM method to obtain a ground feature mask and a confidence score, constructing a field plot sample set, dividing the sample set to train an instance segmentation model, and finally applying the trained model to target region field plot segmentation. The application realizes zero-sample field plot segmentation, breaks through the limitations of traditional deep learning sample dependence and poor cross-region migration, supports regional modeling, and balances segmentation accuracy and efficiency, thereby providing a new technical solution for complex terrain region agricultural remote sensing application.

The application discloses a kind of agricultural pollution source investigation method and system based on stratified random sampling, it is related to agricultural environmental monitoring technical field, which comprises: the planting production pollution load and livestock breeding production pollution load of target area are calculated respectively;Based on the planting production pollution load and livestock breeding production pollution load of target area, the pollution load grade of each to be investigated field plot and each to be investigated breeding farm in target area is determined;Load area grade is high load, medium load or low load;The minimum sample size sampling method is used to stratified sampling to the to be investigated field plot or to be investigated breeding farm under different pollution load grades, and the final sampling survey sample size of the to be investigated field plot and the final sampling survey sample size of to be investigated breeding farm under different pollution load grades are obtained;The stratified sampling includes: first-level stratified sampling and second-level stratified sampling.The application improves the accuracy and scientificity of agricultural pollution source investigation result.

The invention discloses a soil mechanical compaction prediction method for a field scale, belongs to the technical field of agricultural machinery scheduling and precision agriculture, and aims to solve the technical problems that soil mechanical compaction space distribution is difficult to quickly and accurately predict under the field scale and a low compaction operation path is difficult to plan according to the soil mechanical compaction space distribution. The method comprises the following steps: collecting field boundary coordinates and mechanical operation parameters; classifying the field blocks into small, medium and large field blocks based on the area and gradient of the field blocks, and calculating the characteristics of breadth, furrow density, tire grounding area and the like for classification modeling; calculating the mechanical-soil interaction strength to quantify the compaction driving force; inputting the multi-source features into a decision tree model, and synchronously outputting the prediction of the soil compaction risk level and the physical parameter variation; and finally, constructing a cost map according to the prediction level, carrying out cost sensing sampling and self-adaptive step length extension by adopting an improved fast extension random tree algorithm, and generating an agricultural machinery operation path with the minimum accumulated compaction cost.

The application provides a field plot level crop planting area early monitoring method, comprising the following steps: acquiring satellite image data and crop sample data set of a target area, and preprocessing the satellite image data to obtain reconstructed time series satellite data; determining classification features for crop classification based on a feature screening algorithm; based on an image segmentation algorithm, segmenting the satellite image data according to plot data to obtain object units; performing spectral aggregation on each object unit to obtain aggregated images; training and evaluating an initial machine learning classifier according to the aggregated images, the classification features and the crop sample data set to obtain a trained crop classification model; inputting remote sensing images of a target year into the crop classification model to obtain field plot level crop early monitoring results of the target year. The application can significantly reduce the uncertainty caused by pixel-based classification, and improve the accuracy and timeliness of crop classification.

The application discloses a kind of based on diffusion model remote sensing image plot extraction method and device, belong to deep learning, remote sensing satellite, image processing and other technical fields, the method can accurately capture the boundary information of field plot by the powerful feature learning and generation ability of diffusion model, effectively avoid the adhesion between adjacent field plots, realize the independent, accurate extraction of each field plot, Even in the similar or complex farmland scene of field plot feature, it can also guarantee the clarity and accuracy of field plot boundary, meet the demand of large-scale, fine farmland resource management;The method introduces cross attention mechanism and other technologies, directly fuses original image information in the denoising process, so that the generated field plot boundary is more pure and fine, without relying on complex post-processing operation to obtain high-quality extraction result, thereby greatly simplifying the entire field plot extraction process;The method has stronger robustness to different light conditions, seasonal changes and image noise.

The application relates to the technical field of intelligent agriculture, and discloses a field plot extraction method and system based on boundary extraction and breakpoint connection post-processing, which comprises the following steps: collecting farmland remote sensing image data and constructing a labeled data set. A boundary extraction model comprising a connectivity attention module is constructed. Image data in the labeled data set is input into the boundary extraction model to output a boundary probability graph and a directional connectivity probability graph. Double-threshold boundary post-processing is performed on the boundary probability graph to obtain a boundary region binary graph, and center line extraction is performed on the boundary region binary graph to obtain a first boundary extraction result. Breakpoint detection is performed on the first boundary extraction result, and elongation processing is performed on the breakpoints until no breakpoint is detected on the boundary line, so that a second boundary extraction result is obtained. Vector conversion is performed on the second boundary extraction result to obtain a farmland field plot extraction result. The application can obtain a complete farmland field plot extraction result, and improves the precision of farmland field plot extraction.

The application discloses a screening method and application of a phosphorus-accumulating and capacity-increasing material for a weak-acid and poor-slope cultivated red soil, and the screening method comprises the following steps: (1) establishing an annual cumulative increase of effective phosphorus of different phosphorus fertilizer varieties on the poor-slope cultivated red soil, and establishing a database; (2) establishing a crop uniform land potential database; (3) determining the spatial variation of the effective phosphorus of the slope cultivated red soil; and (4) determining the phosphorus-accumulating and capacity-increasing phosphorus fertilizer variety for the weak-acid and poor-slope cultivated red soil. The application solves the problems that the spatial variation of the phosphorus of the slope cultivated red soil is large, the effect of the phosphorus fertilizer on the phosphorus-accumulating and capacity-increasing of the slope cultivated red soil is covered, the expected effect of the phosphorus-accumulating and capacity-increasing of the red soil cannot be achieved in a field plot test for several years or even for ten years or more, and the screening method for the phosphorus-accumulating and capacity-increasing material for the weak-acid and poor-slope cultivated red soil is lacked.

The application discloses a wheat scab prediction method based on multispectral remote sensing data and an XGBoost algorithm, and comprises the following steps: acquiring field wheat scab disease sample data of a target area, and constructing a disease sample database; acquiring multispectral remote sensing images covering the target area, and generating multispectral vegetation index images; extracting vegetation index attribute values of each sample point from corresponding multispectral vegetation index images according to the disease sample database, and constructing a feature database for model training and prediction; performing optimization on hyperparameters of an XGBoost model by using a differential optimization (DO) algorithm, and obtaining an optimal XGBoost prediction model; inputting the multispectral vegetation index images into the optimal XGBoost prediction model, performing pixel-by-pixel scab disease level prediction, and generating a scab disease level spatial distribution map of the target area at a field plot level.

The utility model belongs to the technical field of crop planting auxiliary equipment, and particularly relates to a field test plot lineation device. The device comprises a rack, a spiral conveying mechanism, a walking mechanism and a hopper, and the spiral conveying mechanism is arranged at the bottom of the rack and provided with a discharging port; the walking mechanism is in transmission connection with a conveying shaft of the spiral conveying mechanism and used for driving the conveying shaft of the spiral conveying mechanism to rotate. The hopper is arranged on the machine frame, and a discharging port of the hopper is connected with a feeding port of the spiral conveying mechanism. The marking device is used for solving the current situation that scientific research test fields lack suitable marking devices, and overcoming the limitations that high-cost navigation equipment is poor in applicability and low in input-output ratio, and commercially available low-cost replacement tools are unsmooth in discharging, discontinuous in marking and the like. The walking mechanism is used for driving the spiral conveying mechanism, synchronous discharging is achieved when the lineation device moves forwards, it is guaranteed that powder is smoothly thrown, and the efficiency, precision and economical efficiency of plot lineation layout in a breeding test are improved.

The application discloses a farmland plot dynamic updating method and product, relates to the farmland right field, and comprises the following steps: acquiring a remote sensing image data pair of a rural farmland history and corresponding farmland boundary data, and constructing a data set; according to the data set, constructing a super-resolution-plot joint model based on a super-resolution network and a plot segmentation network; determining current super-resolution data and current plot data of the rural farmland according to current medium-resolution remote sensing image of the rural farmland and based on the trained super-resolution-plot joint model; further determining current plot vector data of the rural farmland; and associating and matching the current plot vector data with a right ownership database, and righting the rural farmland. The application can improve land righting accuracy and efficiency, and dynamically updates the farmland plot regularly.

The invention relates to rotary tillage equipment for tobacco field plots after harvesting, and belongs to the technical field of tobacco field planting equipment. The rotary tillage equipment for the tobacco field plot after harvesting comprises an agricultural machine and a rotary tillage mechanism, wherein the rotary tillage mechanism comprises an installation frame and an input shaft which are connected to the agricultural machine, the top end of the installation frame is rotationally connected with two first belt wheels and a second belt wheel, and the two first belt wheels and the second belt wheel are distributed in a delta shape; the bottom ends of the two first belt wheels penetrate through the mounting frame and are fixedly connected with rotary tillage cutter sets, the top end of the mounting frame is fixedly connected with a power box, the input shaft is rotationally connected to the inner wall of the power box, and the end, away from the inner wall of the power box, of the input shaft is fixedly connected with a first bevel gear; the rotary tillage mechanism can effectively crush soil of a tobacco field, the crushed soil can be distributed in a scattered mode, pathogens and eggs in the soil of the tobacco field can be better killed, and the effect is better when germs are killed in the low-temperature environment of sunlight and winter in the later period.