Productive Crop Fields Dataset

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.

在精准农业领域，检测具有生产力的作物田块是一项至关重要的实践，它使得农民能够分别评估作业性能并比较不同的种子品种、农药和化肥。然而，手动识别生产力田块往往耗时费力，且具有主观性。先前的研究探索了使用先进的机器学习算法来检测作物田块的不同方法，以支持专家的决策，但这些方法往往缺乏高质量的标注数据。在此背景下，我们提出了一种通过机器操作与时间序列上的 Sentinel-2 图像相结合生成的高质量数据集。据我们所知，这是首个采用该技术克服标注样本不足的问题的数据集。随后，我们应用了半监督分类对未标注数据进行处理，并采用最先进的监督和自监督深度学习方法自动检测生产力作物田块。最终，结果表明在正样本无标签学习方面具有较高的准确率，这完美契合了我们对于正样本高度自信的问题情境。在存在准确数据集的情况下，Triplet Loss Siamese 表现最佳，而在没有全面标注数据集可用的情况下，对比学习表现突出。