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The semi-field and field data work in tandem to overcome their individual limitations. The extensive, automatically annotated semi-field data can be leveraged to train annotation helper models, which then aid in the annotation of the more limited, real-world field data. This approach addresses one of the most challenging and expensive tasks in deep learning model development: image labeling.
Target Users
Researchers: Scientists in computer vision, artificial intelligence, and agricultural disciplines seeking data to develop and refine algorithms for weed management, crop monitoring, and image-based phenotyping.
Developers: Software engineers building applications for automated weed detection, plant trait analysis, and precision agriculture tools.
Agronomists: Agricultural professionals interested in utilizing computer vision and AI-powered tools for decision-making in weed management and crop production.
Industry Professionals: Representatives from agricultural companies, technology providers, and input suppliers seeking data-driven solutions for improving agricultural practices.
Crop Groups: Organizations focused on specific crops, that invest in research and development to enhance crop production and sustainability.
An Agricultural Image Repository for the United States
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