Tea clone classification using deep CNN with residual and densely connections

Abstract

Tea clone of Gambung series is a superior variety of tea that has high productivity and quality. Smallholder farmers usually plant these clones in the same areas. However, each clone has different productivity or quality, so it is difficult to predict the production quality in the same area. To uniform the variety of clones in an area, smallholder farmers still need experts to identify each plant because one and other clones share the same visual characteristics. We propose a tea clone identification system using deep CNN with skip connection methods, i.e., residual connections and densely connections, to tackle this problem. Our study shows that the proposed method is affected by the hyperparameter setting and the combining feature maps method. For the combining method, the concatenation method on a densely connected network shows better performance than the summation method on a residual connected network.