mlsanityData to 14 Oct 2026

Self-supervised learning -- A way to minimize time and effort for precision agriculture?

arXiv 2204.02100 ↗First posted 5 Apr 2022cs.LGcited by 4

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Abstract (arXiv)

Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricultural land. Up to now, the main focus was on the enlargement of data which were evaluated by means of supervised learning methods. Nevertheless, the need for labels is also a limiting and time-consuming factor, while in contrast, ongoing technological development is already providing an ever-increasing amount of unlabeled data. Self-supervised learning (SSL) could overcome this limitation and incorporate existing unlabeled data. Therefore, a crop type data set was utilized to conduct experiments with SSL and compare it to supervised methods. A unique feature of our data set from 2016 to 2018 was a divergent climatological condition in 2018 that reduced yields and affected the spectral fingerprint of the plants. Our experiments focused on predicting 2018 using SLL without or a few labels to clarify whether new labels should be collected for an unknown year. Despite these challenging conditions, the results showed that SSL contributed to higher accuracies. We believe that the results will encourage further improvements in the field of precision farming, why the SSL framework and data will be published (Marszalek, 2021).

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

15 of 32 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  3. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
  4. 2021With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representationscited by 326
  5. 2015A Random Forest Guided Tourcited by 168
  6. 2019BreizhCrops: A Time Series Dataset for Crop Type Mappingcited by 34
  7. 2019Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural networkcited by 33
  8. 2021Self-supervised pre-training enhances change detection in Sentinel-2 imagerycited by 21
  9. 2021TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimationcited by 21
  10. 2022Generalized Classification of Satellite Image Time Series with Thermal Positional Encodingcited by 14
  11. 2020Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)cited by 10
  12. 2020SAR2SAR: a semi-supervised despeckling algorithm for SAR imagescited by 6
3 more
  1. 2021Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagerycited by 5
  2. 2020A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mappingcited by 2
  3. 2022Deep Metric Learning-Based Semi-Supervised Regression With Alternate Learningcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Label-Efficient Learning in Agriculture: A Comprehensive Reviewcited by 16
  2. 2024Deep Learning for Satellite Image Time Series Analysis: A Reviewcited by 13
  3. 2023INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fieldscited by 6
  4. 2025Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Datasetcited by 1