Abstract:
Most Ethiopian people use it as a main food by preparing bread from its stems and roots by using
traditional fermenting techniques. Enset plant is a multi-objective traditional crop cultivated and widely
used in the south and southwestern part of Ethiopia. Generally, the Enset plant has a predominant role in
people living in the southern part of Ethiopia. The Enset plant is vulnerable to different diseases like
Bacterial wilt, Sigatoka, and others. This leads to enforce farmers to replace Enset with other crops. These
diseases have a significant impact on Enset production. Even though farmers can stop and prevent such
diseases at an early stage, they are unable to determine which particular disease affects the crop. Based
on our survey, not only farmers but also some pathologists cannot easily identify which disease affects
the crop. Therefore, to address this issue, we created an AI model that automatically recognizes and detects
the diseases that affect the plant. This model aims to support farmers and pathologists to recognize and
detect these diseases in the early stage. We have collected infected and normal image datasets from Sheka,
Bench-Sheko, and Keffa districts of Enset cultivation fields. Our model includes various computer vision
techniques such as image preprocessing, segmentation, feature extraction, and classification. As a
comparison, we have used different convolutional neural network-based models such as Alex Net, VGG
Net, and Res Net. A ResNet model with SoftMax classifier achieved 76% testing accuracy. Alex Net
CNN-based model achieved 90% testing accuracy. The other CNN-based pre-trained model is VGG Net
which achieved 94% testing accuracy. Even though the pre-trained models have promising results, they
need to maximize the prediction accuracy and minimize lose value.So, we have built a new CNN-based
architecture to control overfitting and increase classification accuracy and it achieves 98.2%
