Abstract

The field of agriculture greatly influences our lives. The most significant economic sector in our country is agriculture. A profit in agricultural products is the result of proper management. Farmers produce less because they lack knowledge about leaf disease. Identifying plant leaf diseases is significant because manufacturing determines profit and loss. The method for classifying and detecting leaf diseases is CNN. The primary goal of this study is to identify leaf diseases in tomato, potato, grape, apple, and corn plants. Plant leaf diseases are tracked throughout wide agricultural fields with the purpose of detecting crop diseases. As a result, certain disease features are automatically identified and treated accordingly. The suggested Deep CNN model has been contrasted with well-known transfer learning techniques like VGG16. Plant. Leaf disease detection has several uses in a variety of industries, including agriculture institutes and biological research. One of the necessary study topics is plant leaf disease detection, since it has the potential to be useful in monitoring vast agricultural fields and automatically identify disease symptoms as soon as they manifest on plant leaves.