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.

翻译：农业领域的感知数据集在数量和多样性上均存在局限，这阻碍了监督学习方法的高效训练。自监督学习技术虽可缓解此问题，但现有方法未针对农业领域的密集预测任务进行优化，导致性能下降。针对这一局限，本文提出注入噪声判别器（INoD），该方法利用特征替换与数据集判别原理实现自监督表示学习。INoD在卷积编码过程中交错处理两个独立数据集的特征图，并将预测最终特征图的数据集归属作为前置任务。本方法使网络能够学习某个数据集中目标的明确表征，同时观察其与独立数据集中相似特征的关联性，从而促使网络推理目标对象的高层语义，提升多项下游任务性能。此外，我们发布了包含16,800余张图像的Fraunhofer Potato 2022新数据集，专用于马铃薯田目标检测。基于Sugar Beets 2016数据集与本马铃薯数据集，对INoD预训练策略在目标检测、语义分割及实例分割任务中的全面评估表明，该方法达到了当前最优性能。