  Perception datasets for agriculture are limited both in quantity and
diversity which hinders effective training of supervised learning approaches.
Self-supervised learning techniques alleviate this problem, however, existing
methods are not optimized for dense prediction tasks in agriculture domains
which results in degraded performance. In this work, we address this limitation
with our proposed Injected Noise Discriminator (INoD) which exploits principles
of feature replacement and dataset discrimination for self-supervised
representation learning. INoD interleaves feature maps from two disjoint
datasets during their convolutional encoding and predicts the dataset
affiliation of the resultant feature map as a pretext task. Our approach
enables the network to learn unequivocal representations of objects seen in one
dataset while observing them in conjunction with similar features from the
disjoint dataset. This allows the network to reason about higher-level
semantics of the entailed objects, thus improving its performance on various
downstream tasks. Additionally, we introduce the novel Fraunhofer Potato 2022
dataset consisting of over 16,800 images for object detection in potato fields.
Extensive evaluations of our proposed INoD pretraining strategy for the tasks
of object detection, semantic segmentation, and instance segmentation on the
Sugar Beets 2016 and our potato dataset demonstrate that it achieves
state-of-the-art performance.
