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A UAV image dataset for object detection with annotations generated using LabelImg and Roboflow.

Anindita Das1, Vinitha Hannah Subburaj1, Yong Yang1

  • 1West Texas A&M University, Canyon, TX 79016, USA.

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|February 12, 2026
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Summary

This dataset of drone images aids precision agriculture and machine learning for crop-weed detection. It supports automated monitoring and sustainable farming, advancing AI in agriculture.

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AnnotationComputer visionImage datasetLabelImgObject detectionRemote sensingRoboflowUAVYOLOv7

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Environmental Science

Background:

  • Precision agriculture requires accurate crop-weed differentiation for effective management.
  • Machine learning models for weed detection need large, diverse datasets for training and validation.

Purpose of the Study:

  • To introduce a novel dataset of drone imagery from cotton fields.
  • To facilitate the development of object detection models for distinguishing crops from weeds.
  • To establish a benchmark for evaluating the performance of AI models in agricultural settings.

Main Methods:

  • Acquisition of high-resolution drone imagery across various cotton field conditions.
  • Annotation of images to identify and label crop and weed instances.
  • Dataset curation to ensure quality and suitability for machine learning tasks.

Main Results:

  • A comprehensive dataset enabling robust model training for crop-weed detection.
  • A standardized resource for comparative analysis of different AI algorithms.
  • Foundation for improved automated agricultural monitoring systems.

Conclusions:

  • The released dataset is crucial for advancing AI-driven precision agriculture.
  • It supports the development of sustainable farming practices through enhanced weed management.
  • This resource contributes to the broader field of AI applications in agriculture and environmental stewardship.