Due to the nation's rapid population expansion and rising food consumption, agriculture is essential to India's economy and people. Consequently, agricultural yields need to rise. Crops produce so little as a result of diseases brought on by bacteria, viruses, and fungi. Adopt techniques for identifying plant diseases to stop it. To diagnose diseases since they lay a high emphasis on the data themselves and provide significance to the results of certain tasks using algorithms. Here, a method for employing CNN to find plant diseases has been proposed. To ascertain the size of the sick area and the time complexity, simulation studies and analyses are conducted on sample photos. It is accomplished using image processing methods. There are 32 different classes, including three courses focused on plants (apple, potato, and tomato), have been fed to the model. The total number of sick plant leaves is 24, including those that are impacted by diseases like Apple Rust and others. Different performance matrices are built and jointly trained using the ResNet architecture to categorize the images of sick and healthy leaves for the same produced datasets of leaves. When compared to other models like the Random Forest and SVM, the model's output exhibits superior accuracy, which is 99.2. © 2023 IEEE.