Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks

TL;DR

This paper introduces a self-supervised learning framework using SimCLR to pre-train a ResNet-50 backbone on unannotated agricultural images, enabling diverse vision tasks without large labeled datasets, thus reducing costs and increasing accessibility.

Contribution

It presents a lightweight self-supervised framework that effectively learns transferable features for various agricultural vision tasks without requiring extensive annotations.

Findings

The model learns robust features applicable to multiple agriculture tasks.

Reduces dependence on large annotated datasets, lowering costs.

Enhances accessibility of computer vision in agriculture.

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 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.