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Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks
March 25, 2024, 4:45 a.m. | Sudhir Sornapudi (Corteva Agriscience, Indianapolis, USA), Rajhans Singh (Corteva Agriscience, Indianapolis, USA)
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
abstract agriculture analyze arxiv availability computer computer vision cs.ai cs.cv data data-driven datasets deep learning diverse eess.iv farming framework game industry sustainable tasks type vast vision visual visual data
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