Agriculture is the backbone of human civilization since it is a requirement of
every living entity. Paddy agriculture is extremely important to humans, particularly in
Asia. Farmers are currently facing a deficit in agricultural yield owing to a variety of
factors, one of which is illness. The composition of paddy crop diseases is complicated,
and their presentation in various species is highly similar, making classification
challenging. These agricultural infections must be discovered and diagnosed as soon as
feasible to avoid disease transmission. The disease significantly impacts crop
productivity, and early detection of paddy infections is critical to avoiding these
consequences. These issues arise as a result of a lack of awareness regarding health.
Identifying the disease needs the best expertise or previous knowledge to regulate it.
This is both difficult and costly. To address the aforementioned problem, a Deep
Learning (DL) model was created utilizing a Convolutional Neural Network (CNN)
and the transfer learning approach. The model is trained using an image of a paddy
crop as input. By comparing metrics like accuracy and loss, the optimum technique is