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This study presents a new dataset of weed images for precision agriculture. The dataset aids in developing AI models for early weed detection, reducing herbicide use and enhancing sustainable farming.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Data Science

Background:

  • Early weed identification is crucial for precision agriculture and sustainable crop production.
  • Site-specific weed management reduces herbicide use and environmental impact.
  • Developing accurate AI models requires high-quality, annotated datasets.

Purpose of the Study:

  • To introduce a novel, publicly accessible dataset of annotated RGB images for weed species identification.
  • To support the training and evaluation of deep learning models for early-stage weed detection.
  • To advance the development of image-based monitoring systems in precision agriculture.

Main Methods:

  • A curated dataset of 1217 high-resolution RGB images (5184 × 3888 pixels) was collected from commercial tomato fields.
  • Images were captured during the 2021 and 2022 growing seasons in Badajoz, Spain.
  • The dataset contains 21,208 manually annotated instances of weed species, divided into two subsets by year.

Main Results:

  • The dataset comprises 1217 images with 21,208 labelled weed instances.
  • Subset 1 (2021): 938 images, 9060 instances.
  • Subset 2 (2022): 278 images, 11,931 instances.

Conclusions:

  • The public release of this dataset facilitates research in AI-driven weed detection.
  • This resource will accelerate the development of efficient and accurate weed classification models.
  • The dataset supports advancements in sustainable precision agriculture through improved weed management strategies.