  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.
