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不足，这制约了监督学习方法的有效训练。自监督学习技术能缓解该问题，但现有方法并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则进行自监督表示学习。INoD在卷积编码过程中交错融合两个无交集数据集的特征图，并预测生成特征图的数据集归属作为预文本任务。该策略使网络在观察一个数据集中目标与另一数据集相似特征共同出现的模式时，学习到目标的明确表征。这使网络能够推理蕴含目标的高层语义，从而提升各类下游任务的性能。此外，我们引入了包含超过16,800张图像的新型Fraunhofer Potato 2022数据集，用于马铃薯田目标检测。在Sugar Beets 2016及本马铃薯数据集上，对提出的INoD预训练策略进行目标检测、语义分割和实例分割任务的广泛评估表明，该方法达到了最先进的性能水平。