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

Abstract

Enset is a monocarpic perennial crop which belongs to the Schistaminae order and the Musaceae family. Enset is a key food security crop in Southern Ethiopia, where almost 20 million people rely on it for survival. One of the most difficult aspects of Enset production is the necessity for precise and early diagnosis of its diseases. Plant leaf diseases and destructive pests are a major challenge in Enset production. Limited amount of research has been conducted to automate Enset disease detection. The studies conducted were concentrated on bacterial wilt disease detection, the detection of Enset mealybug pests is a forgotten subject but it is a major constraint on Enset production. This thesis looks into the use of deep learning to detect bacterial wilt disease and Enset mealybug pest, where data obtained is small 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, such as 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. The next step was to design a convolutional neural network that can categorize a given image as healthy, bacterial wilt or mealybug. Finally, using the collected dataset, the created CNN model was trained and evaluated, and it is compared to the state-of-the-art pre-trained convolutional neural network models: AlexNet, ResNet50, Inceptionv3, DenseNet201, VGG16 and EfficientNetB3. The proposed approach was 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. We offer the most effective method for segmenting only the ROI section of Enset leaves. Sigatoka, leaf speckle, and cordana are some of the other diseases found in Enset. Due to a lack of data, we were only able to detect healthy, bacterial wilt, and root mealybug in this research. As a result, we urge that the aforesaid diseases be detected in future studies.