Agricultural and Biological Research

Enset (Enset ventricosum) Plant Disease and Pests Identification Using Image Processing and Deep Convolutional Neural Network

Tsegaye Yibgeta, Million Meshesha and Muktar Bedaso*

Enset is a monocarpic perennial crop that belongs to the Schistaminae order and the Musaceae family. Enset is a significant food security crop in Southern Ethiopia, where almost 20 million people depend on it for survival. Plant leaf diseases and damaging pests are foremost challenges in Enset production. This study looks into the use of deep learning to detect bacterial wilt disease and Enset mealy bug pest, where data is obtained in small amounts and collected under minimally controlled conditions. We employed data augmentation to get over the limits of the dataset size. The proposed approach is divided into four stages. The initial part entails gathering healthy and diseased Enset images with the support of agricultural experts, from various farms and research institutes. Then image processing tasks, resizing and segmentation are applied on the collected dataset in order to get an enhanced (simpler) image and to extract region of interest from the dataset images. Finally, using the collected dataset, the created model is trained and evaluated, and it is compared to the state of the art pre-trained convolutional neural network models: AlexNet, ResNet-50, Inception v3, DenseNet-201, VGG16 and EfficientNetB3. The proposed approach is implemented using Google collaboratory or "colab" for short. To detect and classify Enset diseases, the model has an accuracy of 99.68% for training and 98.87% for testing.