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

TL;DR

This paper investigates the use of self-supervised learning to improve crop type classification in precision agriculture, especially under challenging conditions with limited labeled data, demonstrating higher accuracy than supervised methods.

Contribution

It introduces the application of SSL to agricultural data, showing its effectiveness in scenarios with scarce labels and environmental variability, which is novel in this context.

Findings

SSL achieved higher accuracy than supervised methods under challenging conditions

Unlabeled data can effectively be used for crop classification

SSL reduces the need for extensive labeled datasets in agriculture

Abstract

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).