Classifying enset based on their disease tolerance using deep learning

Abstract

Even though agriculture remains the main source of Ethiopia’s population economy, not identifying enset based on their disease tolerance level is an obstacle for the production of enset.This paper’s main objective is to automatically identify the disease resistance levels of enset plants through digital image. The researcher followed the design science research method to achieve the objective listed above. Besides, the researcher has attempted to get valuable information about the type and the nature of these classes from the domain expert through interviews, document analysis, and observation from the fields. The total number of images used for experimentation purposes was 3000. The Contaharmonic filtering technique was implemented to remove noise due to its highest entropy recorded. A deep learning-based approach with training from scratch and transfer learning convolutional neural network methods were applied. Based on this, the researcher made experimentation for transfer learning by using two different pre-trained models, namely VGG-19 and VGG-16. Finally, the developed classifier model’s performance was assessed using accuracy, precision, recall, and the F1 score. According to the interpretation of the results, the proposed model’s training from scratch method achieves 92.6%. On the other way, the accuracy obtained with the transfer learning method, VGG-16 achieves 98.5%, and VGG-19 achieves 93.9%. Hence, transfer learning, specifically the VGG-16 model revealed an effective and robust performance for classifying enset based on their disease tolerance level based on the researcher’s number of images.