Productive Crop Field Detection
Abstract
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
framework for productive crop field detection based on 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 combination of
techniques. In sequence, we present three methods, based on state-of-the-art supervised
and self-supervised methods, selected according to the dataset characteristics, to detect
productive crop fields. Finally, we demonstrate high accuracy results in Positive Unlabeled
learning, which perfectly fits the problem where we have high confidence in the positive
samples. Finally, best performances have been found with Contrastive Learning, given its
ability to augment data, allowing the model to be trained with a larger dataset considering
the artificially created samples.
Collections
The following license files are associated with this item: