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

arXiv: https://arxiv.org/abs/2204.02100 · HTML: https://mlsanity.com/paper/2204.02100/

## 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 (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2104.14548/) (2021)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [BreizhCrops: A Time Series Dataset for Crop Type Mapping](https://mlsanity.com/paper/1905.11893/) (2019)
- [Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network](https://mlsanity.com/paper/1910.12023/) (2019)
- [Self-supervised pre-training enhances change detection in Sentinel-2 imagery](https://mlsanity.com/paper/2101.08122/) (2021)
- [TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation](https://mlsanity.com/paper/2111.02682/) (2021)
- [Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding](https://mlsanity.com/paper/2203.09175/) (2022)
- [Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)](https://mlsanity.com/paper/2004.12880/) (2020)
- [SAR2SAR: a semi-supervised despeckling algorithm for SAR images](https://mlsanity.com/paper/2006.15037/) (2020)
- [Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery](https://mlsanity.com/paper/2108.05484/) (2021)
- [A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping](https://mlsanity.com/paper/2005.11930/) (2020)
- [Deep Metric Learning-Based Semi-Supervised Regression With Alternate Learning](https://mlsanity.com/paper/2202.11388/) (2022)

## Built on by (most cited first, 4 of 4)

- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024)
- [INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields](https://mlsanity.com/paper/2303.18101/) (2023)
- [Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset](https://mlsanity.com/paper/2504.11022/) (2025)

## 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).