TITLE: Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results 

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 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.
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.
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
http://arxiv.org/abs/2305.11990v2