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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Automated weed segmentation with knowledge based labeling for machine learning applications.

Thuan Ha1, Kathryn Aldridge2, Eric Johnson2

  • 1Department of Plant Sciences, University of Saskatchewan, Saskatoon, S7N5A8, Canada. thuan.ha@usask.ca.

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|January 26, 2026
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Summary
This summary is machine-generated.

An automated workflow accurately labels landscape features in Unmanned Aerial Vehicle (UAV) imagery for precision agriculture. This reduces manual effort in creating datasets for machine learning weed detection.

Keywords:
Automated labelingECognitionImage segmentationPrecision agricultureUAV RGB imageryWeed detection

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Computer Vision

Background:

  • Accurate landscape feature classification is vital for precision agriculture, including weed control and variable-rate applications.
  • Machine and deep learning models offer potential for real-time weed detection but require extensive labeled datasets, which are labor-intensive to create.

Purpose of the Study:

  • To develop and evaluate an automated feature-labeling workflow for Unmanned Aerial Vehicle (UAV) RGB imagery.
  • To reduce the manual effort and time required for generating labeled datasets for agricultural machine learning applications.

Main Methods:

  • An automated workflow was developed using eCognition software (v9.5) integrating segmentation, line detection, distance mapping, convolution filtering, morphological operations, and thresholding.
  • High-resolution UAV RGB imagery (0.088 cm) of a research field with various weed species (kochia, wild oat, wild mustard, false cleavers) and wheat was utilized.
  • Vegetation indices, including the Colour Index of Vegetation and Excess Green Index, were employed to differentiate between crops, weeds, and soil.

Main Results:

  • The automated workflow achieved 87% overall accuracy with a kappa coefficient of 0.81 in classifying landscape features without manual training labels.
  • The combination of spatial algorithms and vegetation indices effectively separated crops and weeds from the soil background.
  • Randomly distributed labeling points and a confusion matrix were used for performance evaluation.

Conclusions:

  • The developed automated workflow shows significant potential for accelerating the creation of labeled datasets for machine learning and deep learning in agriculture.
  • This approach can substantially reduce manual effort while maintaining high classification accuracy for weed detection and other precision agriculture tasks.
  • Future research will focus on enhancing the workflow's transferability across different fields, acquisition dates, and experimental conditions.