In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying productive fields is often a time-consuming and error-prone task. Previous studies explore different methods to detect crop fields using advanced machine learning algorithms, but they often lack good quality labeled data. In this context, we propose a high-quality dataset generated by machine operation combined with Sentinel-2 images tracked over time. As far as we know, it is the first one to overcome the lack of labeled samples by using this technique. In sequence, we apply a semi-supervised classification of unlabeled data and state-of-the-art supervised and self-supervised deep learning methods to detect productive crop fields automatically. Finally, the results demonstrate high accuracy in Positive Unlabeled learning, which perfectly fits the problem where we have high confidence in the positive samples. Best performances have been found in Triplet Loss Siamese given the existence of an accurate dataset and Contrastive Learning considering situations where we do not have a comprehensive labeled dataset available.

翻译：在精准农业中，高产农田检测是一项关键实践，它使农民能够分别评估作业绩效，并比较不同种子品种、农药和肥料的效果。然而，人工识别高产农田通常耗时且易出错。以往的研究探索了利用先进机器学习算法检测农田的不同方法，但往往缺乏高质量的标注数据。在此背景下，我们提出一个由机器作业结合随时间追踪的哨兵二号影像生成的高质量数据集。据我们所知，这是首个利用该技术克服标注样本匮乏问题的数据集。随后，我们应用半监督分类处理未标注数据，并采用最先进的监督与自监督深度学习方法来自动检测高产农田。最终结果表明，正无标记学习取得了高精度，完美契合了我们对正样本高度自信这一问题的场景。在拥有精确数据集的情况下，三元组损失孪生网络表现最佳；而在缺乏全面标注数据集的情形下，对比学习则表现出色。