Recognition of Tomato Leaf Disease Using 10-Layered DCNN

The primary causes of the detrimental effects on crops and plant life are majorly plant disease and leaf disease. For the agricultural unit, this is the main risk. Food scarcity is causing agony for millions of people. Farmers' ability to make a living is severely impacted by crop damage caused by damaged leaves. Crops are not receiving a good diagnosis, which has an impact on plant growth, due to ignorance about the type of illness and pesticide usage. Food security is seriously threatened by crop diseases. It might be difficult to diagnose a disease at an early stage in many places of the world. Early recognition and diagnosis of the disease is the solution to improve the overall health of the crop and thus reduce the scarcity of the food. To help farmers, a smart agricultural framework is designed by using CNN. In this paper a 10- DCNN is implemented for the identification and diagnosis of tomato leaf disease. The proposed framework attained 95.4% of training accuracy and 93.01% of testing accuracy.