查看文章

Agriculture emerges as a prominent application domain for advanced computer vision algorithms. As much as deep learning approaches can help solve problems such as plant detection, they rely on the availability of large amounts of annotated images for training. However, relevant agricultural datasets are scarce and at the same time, generic well-established image datasets such as ImageNet do not necessarily capture the characteristics of agricultural environments. This observation has motivated us to explore the applicability of self-supervised contrastive learning on agricultural images. Our approach considers numerous non-annotated agricultural images, which are easy to obtain, and uses them to pre-train deep neural networks. We then require only a limited number of annotated images to fine-tune those networks in a supervised training manner for relevant downstream tasks, such as plant classification …