An Application of Deep Transfer Learning To Detect Lychee Leaf Disease

Abstract

Bangladesh is a primarily agricultural nation. The majority of people depend on agriculture. Our nation of Bangladesh also heavily relies on agriculture. In the current state of affairs, it is crucial that we increase the yields of our crops and fruits in order to grow them. Bangladeshi people and farmers are fighting to grow their crops and fruits in a crucial way despite the country's extreme and changeable climate. Since Bangladesh is an agricultural nation, it is a sad fact that the quality and quantity of our fruits are declining due to various diseases. People in our nation are discovering numerous new rare diseases in our native fruits, but we are failing to recognize these diseases, and the severity of this issue is growing daily. So, in order to combat this issue, proper treatment or recovery is required. As Bangladeshis, it is very difficult for us to identify this rare disease and we require classification of these issues. Since we live in a technological age, it goes without saying that technology can be extremely helpful in identifying these diseases. It is crucial to first identify leaf disease because growing a healthy plant depends on the plant's leaves. As a result, we can maintain a healthy environment for both the leaves and the fruits. In our research, we are trying to identify leaf diseases. Research into litchi leaf disease is something we are very interested in since it is the most popular fruit in Bangladesh. Therefore, by preventing disease in our litchi fruit, we can contribute to the Bangladeshi economy. We use cutting-edge image processing tools that are very beneficial to us in order to guarantee the freshness of the leaves. By simply looking at the leaves, it is very difficult to identify any disease. Our technology uses a cutting-edge method called image processing. We are employing CNN (Convolutional Neural Network) and machine vision- based image processing for this purpose.