Productive Crop Field

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 time-consuming, costly, and subjective. Previous studies explore different methods to detect crop fields using advanced machine learning algorithms to support the specialists’ decisions, 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.

在精准农业（precision agriculture）领域，识别高产农田是一项核心实践，可帮助农户单独评估运营绩效，并对比不同种子品种、杀虫剂与肥料的使用效果。然而，人工识别高产农田往往耗时耗力、成本高昂且带有主观性。既往研究探索了多种利用先进机器学习算法识别农田的方法，以辅助专业人员决策，但此类研究往往缺乏高质量的标注数据。在此背景下，本研究提出了一套高质量数据集，该数据集由农机作业数据与长期追踪的哨兵二号（Sentinel-2）卫星影像结合生成。据我们所知，该数据集是首个通过该技术解决标注样本匮乏问题的数据集。随后，本研究应用未标注数据半监督分类方法，以及当前最优的监督式与自监督深度学习方法，实现高产农田的自动识别。最后，研究结果显示，正例未标注学习（Positive Unlabeled Learning）取得了较高精度，该方法完美适配对正样本具有高置信度的研究场景。在拥有精准数据集的场景下，三元损失孪生网络（Triplet Loss Siamese）表现最优；而在缺乏全面标注数据集的场景中，对比学习（Contrastive Learning）则展现出最佳性能。