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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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.

Scientific Reports

|January 26, 2026

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 labeling ECognition Image segmentation Precision agriculture UAV RGB imagery Weed 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.