Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks
<|reference_start|>Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks: Computer vision in agriculture is game-changing with its ability to transform farming into a data-driven, precise, and sustainable industry. Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets. This remains a bottleneck as manual labeling is error-prone, time-consuming, and expensive. The lack of efficient labeling approaches inspired us to consider self-supervised learning as a paradigm shift, learning meaningful feature representations from raw agricultural image data. In this work, we explore how self-supervised representation learning unlocks the potential applicability to diverse agriculture vision tasks by eliminating the need for large-scale annotated datasets. We propose a lightweight framework utilizing SimCLR, a contrastive learning approach, to pre-train a ResNet-50 backbone on a large, unannotated dataset of real-world agriculture field images. Our experimental analysis and results indicate that the model learns robust features applicable to a broad range of downstream agriculture tasks discussed in the paper. Additionally, the reduced reliance on annotated data makes our approach more cost-effective and accessible, paving the way for broader adoption of computer vision in agriculture.<|reference_end|>
@article{sornapudi2024self-supervised,
    title={Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks},
    author={Sudhir Sornapudi (1), Rajhans Singh (1) ((1) Corteva Agriscience,
  Indianapolis, USA)},
    journal={arXiv preprint arXiv:2403.15248},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15248},
    primaryClass={cs.CV cs.AI eess.IV}
}