论文标题
自我监督的学习 - 一种最大程度地减少精确农业的时间和精力的方法？
Self-supervised learning -- A way to minimize time and effort for precision agriculture?
论文作者
论文摘要
机器学习，卫星或本地传感器是对农业进行可持续和节省资源优化的关键因素，并证明了其对农业土地管理的价值。到目前为止，主要重点是通过监督学习方法评估的数据扩大。然而，对标签的需求也是一个限制和耗时的因素，而相比之下，持续的技术开发已经在提供越来越多的未标记数据。自我监督的学习（SSL）可以克服这一限制，并包含现有的未标记数据。因此，将作物类型数据集用于使用SSL进行实验，并将其与监督方法进行比较。我们从2016年到2018年的数据集的一个独特功能是2018年的气候条件不同，可降低产量并影响植物的光谱指纹。我们的实验重点是使用无需SLL或一些标签来预测2018年，以阐明是否应收集新标签，以收集未知年度。尽管有这些挑战性的条件，但结果表明SSL有助于更高的精度。我们认为，结果将鼓励在精确农业领域的进一步改善，为什么SSL框架和数据将发布（Marszalek，2021年）。
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).
