Cassava Leaf Disease Prediction Using Efficientnet-B0 Model

Abstract

The FAO estimates that 60 percent of the world’s population makes their living from agriculture. The rapid increase in the global populations demand for
food is also quite fast. In this case, plant diseases pose a substantial threat to
the agricultural industry. Therefore, deep learning algorithms are applied to
spot them at an early stage as a move towards protecting farmers against such
losses while increasing crop yield. We applied CNNs in developing a technique
for identifying different diseases of cassava leaf which lead to low yields. We
created a cost-effective model that will help farmers to save costs and
special- ize in farming operations. Early diagnosis of these diseases is
proposed by EfficientNet-B0, which may serve well since they provide a
remedy for minor cases of cassava leaf illnesses. This may lead to better
cassava crop health, and therefore more food security especially in some
particularly vulnerable places.