This Research highlights the issues, kinds of the illness and investigations performed to tackle the challenges linked to the diseases utilising different deep learning algorithms. Generally, we can recognise the plants that are damaged by specific illnesses, but away from our vision, it is challenging to detect. Without supplying the proper treatment and early activities, the complete cultivated ground might change into a disease afflicted region; otherwise all plants which are a neighbour to one another can be impacted by means of spreading. So, to identify the plant illnesses in advance and to detect the diseases with the use of contemporary computer technology, a model is developed to effectively discriminate plant diseases. The dataset utilised here comprises of different species of plants of both damaged and healthy, and all these photos are acquired from various publicly accessible sources. An accuracy of 97% in plant classification and over 96% in disease classification utilising VGG and ResNet architecture is obtained. To identify apple, grape, and potato leaf diseases, a graphical user interface (GUI) was devised. The technology identifies leaf problems as well as cures for such diseases, which is valuable to farmers.

VGG16, ResNet, Adam, SGD, RMSprop