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.