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Related Concept Videos

Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Design Example: Measuring Distance Between Two Points with Obstructions01:10

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Methods of Obtaining Topography01:25

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Field Procedure for Staking Out Curves01:26

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Staking out curves is an essential process in construction to ensure the accurate alignment of structures along a curved path. This task involves positioning stakes at calculated locations corresponding to the curve's design, effectively translating plans into physical markers in the field. The process begins by determining the geometric parameters of the curve, including the radius, central angle, and tangent distances. These parameters are critical for identifying key points such as the...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Research on the visual location method for strawberry picking points under complex conditions based on composite

Hehe Xie1, Zhijie Zhang1, Kailiang Zhang1

  • 1College of Engineering, China Agricultural University, Beijing, China.

Journal of the Science of Food and Agriculture
|June 26, 2024
PubMed
Summary

This study introduces a visual method for locating strawberry picking points using composite AI models. The approach enhances accuracy and efficiency for robotic harvesting, addressing current limitations in complex environments.

Keywords:
fruit detectionpeduncle segmentationpicking point locationstrawberry

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Area of Science:

  • Agricultural Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Strawberry harvesting is labor-intensive and requires precise picking point identification for automation.
  • Existing methods for locating picking points suffer from inaccuracies and lack effectiveness in complex scenarios.

Purpose of the Study:

  • To develop an accurate and efficient visual location method for strawberry picking points.
  • To improve the performance of strawberry harvesting robots through precise guidance.

Main Methods:

  • A composite model approach using object detection (YOLOv8s) and instance segmentation (YOLOv8s-seg-CBAM) was developed.
  • The Convolutional Block Attention Module (CBAM) was integrated into YOLOv8s-seg to enhance detection.
  • Various object detection and instance segmentation models were evaluated to find the optimal combination.

Main Results:

  • The composite model achieved a peduncle detection accuracy of 86.2%.
  • The system demonstrated an efficient inference time of 30.6 milliseconds per image.
  • The YOLOv8s and YOLOv8s-seg-CBAM combination proved effective for identifying picking points and inclination.

Conclusions:

  • The proposed visual location method balances accuracy and efficiency for automated strawberry harvesting.
  • This method provides more precise guidance for robotic harvesting operations.
  • The composite model shows significant potential for advancing agricultural robotics.