8 results about "Field plot" patented technology

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 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.

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 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.

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 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 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.