Deep Convolutional Neural Networks-Based Plants Diseases Detection Using Hybrid Features

Budiarianto Suryo Kusumo (Research center for Informatics. Indonesian Institute of Sciences)
Ana Heryana (Research center for Informatics. Indonesian Institute of Sciences)
Dikdik Krisnandi (Research center for Informatics. Indonesian Institute of Sciences)
Sandra Yuwana (Research center for Informatics. Indonesian Institute of Sciences)
Vicky Zilvan (Research center for Informatics. Indonesian Institute of Sciences)
Hilman F Pardede (Research center for Informatics. Indonesian Institute of Sciences)

ComEngApp : Computer Engineering and Applications Journal
Vol 9 No 3 (2020)

With advances in information technology, various ways have been developed to detect diseases in plants, one of which is by using Machine Learning. In machine learning, the choice of features affect the performance significantly. However, most features have limitations for plant diseases detection. For that reason, we propose the use of hybrid features for plant diseases detection in this paper. We append local descriptor and texture features, i.e. linear binary pattern (LBP) to color features. The hybrid features are then used as inputs for deep convolutional neural networks (DCNN) Support and VGG16 classifiers. Our evaluation on Based on our experiments, our proposed features achieved better performances than those of using color features only. Our results also suggest fast convergence of the proposed features as the good performance is achieved at low number of epoch.