The financial system in India is heavily dependent on agriculture, making it the foundation of the country’s economy. In India, an agricultural nation, more than 55% of people depend on agriculture for their livelihood. The attack of various diseases brought on by bacteria and pests that cannot be seen with the naked eye is causing a lot of problems for the agriculture industry today. In the existing system, the CNN model is built to classify the diseases of paddy crops with 94% accuracy. In this study, an app was created utilizing CNN, which detects the diseases and provides appropriate pesticides. A model that makes use of convolution neural network architecture has been created to make it easier to identify crop diseases from images of leaves. Paddy crop, which is mostly produced in India; tomato crop; and cotton crop, which holds a unique position among all crops and is also referred to as “white gold.” These three crops have been taken into consideration for this study. The dataset included six types of diseases. Each crop has two types of diseases. The proposed model achieves 96% accuracy.