Abstract:
Ethiopia is one of the world's developing countries, and its economy depends on both
agriculture and industry. Among agricultural products, the banana crop is one of the most
important foods for both humans and animals. Banana diseases are natural factors that can
severely affect banana crops, ultimately reducing product quality and reducing yield.
Manually, through human vision, categorization, classification, and management of banana
leaf diseases is challenging work because the similarity of disease symptoms makes different
farmers and domain experts diagnose the same diseases as different diseases, and vice versa.
Previously conducted research mainly focused on tasks of identification. So, to overcome the
problem, this research work was conducted. The main objective of this work is to develop a
banana plant leaf disease classification model using deep learning. From different banana
diseases, this study primarily focuses on common diseases such as black sigatoka, yellow
sigatoka, and Cordina leaf spot that affect the leaves of banana plants. In order to conduct an
experiment, researchers collected and preprocessed a total of 4020 banana leaf images from
four different classes. This study followed an experimental research design with a mixed
research approach by incorporating both quantitative and qualitative approaches, for collecting
and analyzing the existing state of banana diseases through the Python programming language
for model development. Researchers trained and conducted hyper-parameter tuning through a
grid search algorithm. In this work, researchers selected four algorithms, such as CNN,
VGG19, EfficientNetB2, and ResNet50, based on a deep study conducted of previous related
work to identify the best-performing classifier. The experimentation was conducted using
selected models through a data split of 70/20/10 based on different evaluation metrics and
evaluated models’ classification performance. The last output shows that the EfficientNetB2
model outperforms the remaining models through test accuracy of 0.99, 0.99, and 0.99 in
training, validation, and testing, respectively. At the end, depending on the results obtained, a
proposed model prototype was developed by researchers to classify banana leaf diseases.
