Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network

Dikdik Krisnandi (Research Center for Informatics (P2I) - Indonesia Institute of Sciences (LIPI))
Hilman F. Pardede (Research Center for Informatics (P2I) - Indonesia Institute of Sciences (LIPI))
R. Sandra Yuwana (Research Center for Informatics (P2I) - Indonesia Institute of Sciences (LIPI))
Vicky Zilvan (Research Center for Informatics (P2I) - Indonesia Institute of Sciences (LIPI))
Ana Heryana (Research Center for Informatics (P2I) - Indonesia Institute of Sciences (LIPI))
Fani Fauziah (Research Institute for Tea and Cinchona - Indonesian Agency for Agricultural Research and Development)
Vitria Puspitasari Rahadi (Research Institute for Tea and Cinchona - Indonesian Agency for Agricultural Research and Development)

CommIT (Communication & Information Technology)
Vol. 13 No. 2 (2019): CommIT Journal

Plant diseases can cause a significant decrease in tea crop production. Early disease detection can help to minimize the loss. For tea plants, experts can identify the diseases by visual inspection on the leaves. However, providing experts to deal with disease identification may be very costly. The machine learning technology can be implemented to provide automatic plant disease detection. Currently, deep learning is state-of-the-art for object identification in computer vision. In this study, the researchers propose the Convolutional Neural Network (CNN) for tea disease detections. The researchers focus on the implementation of concatenated CNN, namely GoogleNet, Xception, and Inception-ResNet-v2, for this task. About 4727 images of tea leaves are collected, comprising of three types of diseases that commonly occur in Indonesia and a healthy class. The experimental results confirm the effectiveness of concatenated CNN for tea disease detections. The accuracy of 89.64% is achieved.