基于深度卷积对抗网络的茶叶克隆识别特征学习

Endang Suryawati, Vicky Zilvan, R. S. Yuwana, A. Heryana, D. Rohdiana, H. Pardede
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引用次数: 4

摘要

茶叶是一种在印尼经济中具有战略性作用的商品。为了保持优质商品,增加茶叶产量和/或提高茶叶质量,茶树的种植变得非常重要。在茶园管理系统中,对田间种植的无性系品种进行识别是至关重要的。但是,这需要人类专家区分不同类型的克隆。自动克隆识别的存在有望使识别简单,快速,准确,方便普通农民使用。在这项工作中,我们提出了一种基于深度卷积生成对抗网络(DCGAN)的无监督特征学习算法,用于茶叶克隆的自动识别。使用无监督学习使我们能够利用未标记的数据。实验结果表明,该方法对茶叶无性系的检测任务是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Convolutional Adversarial Network-Based Feature Learning for Tea Clones Identifications
Tea is a commodity has a strategic role in the Indonesian economy. The cultivation of tea plants becomes very important in order to maintain the superior commodity, with respect to increase the production and/or improve the quality of tea. In a tea plantation management system, it is essential to identify the types of tea clones planted in the field. But, it requires human experts to distinguish one types of clones with another. The existence of an automatic clones identification is expected to make the identification easy, fast, accurate, and easily accessible for common farmers. In this work, we propose an unsupervised feature learning algorithm derived from Deep Convolutional Generative Adversarial Network (DCGAN) for automatic tea clone identification. The use of unsupervised learning enable us to utilize unlabeled data. Our experiments suggest the effectiveness of our method for tea clones detection task.
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