Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach

Abstract

Bacterial Wilt disease is the most determinant factor as it results in a serious reduction in the quality and quantity of food produced by Enset crop. Therefore, early detection of Bacterial Wilt disease is important to diagnose and fight the disease. To this end, a deep learning approach that can detect the disease by using healthy and infected leave images of the crop is proposed. In particular, a convolutional neural network architecture is designed to classify the images collected from different farms as diseased or healthy. A total of 4896 images that were captured directly from the farm with the help of experts in the field of agriculture was used to train the proposed model. The proposed model was trained using these images and data augmentation techniques was applied to generate more images. Besides training the proposed model, a pre-trained model namely VGG16 is trained by using our dataset. The proposed model achieved a mean accuracy of 98.5% and the VGG16 pre-trained model achieved a mean accuracy of 96.6% by using a mini-batch size of 32 and a learning rate of 0.001. The preliminary results demonstrated that the effectiveness of the proposed approach under challenging conditions such as illumination, complex background, different resolutions, variable scale, rotation, and orientation of the real scene images.