Abstract:
In South Asia, a large number of people consume betel life every day and it becomes a part
of the daily life of this reason for many years. Betel leaf is also used as a remedy and, it's
a great source of nutrition. In the international market demands for betel leaf is increasing
incredibly. Plants of betel leaf are highly sensitive, so its cultivation is very laborious work.
Like other South Asian countries, farmers of Bangladesh also face a great loss for the
diseases of betel leaf every year. Diseases of betel plants decrease leaves quality.
Recognition of betel leaf diseases became a crucial task to meet the rising demand in local
and international markets and increase the quality of leaf. We will recognize the diseases
of betel leaf using deep learning. First, we study some online articles, journals, and, related
papers then we talk to local farmers and visit their lands; we find two common diseases of
betel leaf which are foot rot and, leaf rot. Then we collect images of healthy and affected
betel leaves. We also collected some images of unknown diseases. After data collecting
images, we resized images and create a dataset of four classes. We applied deep learning
models in our dataset to recognize the disease of betel leaf. Now deep learning is used in
various recognition related systems. We have proposed a CNN model named BLCNN,
which achieved 90.75% training accuracy and 89.44% test accuracy. Besides BLCNN we
have also modified 3 pre-trained models for this study. Among these, EfficientNet B0 has
performed better than other models. It has achieved 96.77% accuracy in classifying new
images.
