论文标题
利用无监督的学习来改善草皮内容预测和草本质量估计
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
论文作者
论文摘要
草种的构成估计是一个繁琐的估计。必须在田间收集草药，并将手动分成成分，干燥并称重以估计物种组成。在先前的工作中，已经使用了使用神经网络的深度学习方法来提出更快，更具成本效益的替代此过程的替代方法，通过估算仅仅是牧场领域的生物量信息。但是，深度学习方法已经努力概括到遥远的地理位置，并需要进一步的数据收集以在不同的气候中进行重新训练和最佳性能。在这项工作中，我们通过减少训练神经网络时对接地图像（GT）图像的需求来增强深度学习解决方案。我们证明了如何在Sward组成的预测问题中使用无监督的对比度学习，并与丹麦收集的公共Grassclover数据集的最先进以及来自爱尔兰的最新数据集进行了比较，在那里我们解决了草药质量和高度估计。
Sward species composition estimation is a tedious one. Herbage must be collected in the field, manually separated into components, dried and weighed to estimate species composition. Deep learning approaches using neural networks have been used in previous work to propose faster and more cost efficient alternatives to this process by estimating the biomass information from a picture of an area of pasture alone. Deep learning approaches have, however, struggled to generalize to distant geographical locations and necessitated further data collection to retrain and perform optimally in different climates. In this work, we enhance the deep learning solution by reducing the need for ground-truthed (GT) images when training the neural network. We demonstrate how unsupervised contrastive learning can be used in the sward composition prediction problem and compare with the state-of-the-art on the publicly available GrassClover dataset collected in Denmark as well as a more recent dataset from Ireland where we tackle herbage mass and height estimation.
