Improving Rice Disease Diagnosis Using Ensemble Transfer Learning Techniques

Abstract

Early diagnosis of the disease in crop opens the door to potential care and treatment, which in turn improves the yield. Automated detection of rice plant disease from plant images is an emerging research field that is gaining prominence due to the rising interest in it from machine learning researchers. Convolution neural network-based learning techniques are widely used by the research community in accurately handling these types of tasks. In current study, transfer learning models like InceptionV3, ResNet152V2, MobileNetV2, Xception, DenseNet201, InceptionResNetV2 and VGG19 are used for the diagnosis of rice plant diseases on two datasets, which are publicly available in Mendeley and Kaggle. To improve the performance of the diagnosis system, ensembling of Transfer Learning (TL) models has been introduced in this work. All possible combinations of the TL models are designed and experiment is carried out using all of them. It is observed that most of the ensembled models are superior to individual TL models. The work demonstrates that some of the ensemble models are superior in performance than the advanced learning model like Convolution-XGBoost.