Investigating Profound Learning Systems for Potato Disease Characterization

Given the swift advancements in agricultural technology and the escalating integration of artificial intelligence in identifying plant diseases, there is a pressing need for conducting practical research to further the progress of sustainable agriculture. To preserve food safety and enhance product quality, it is crucial to identify and classify potato flaws. Identifying the early stages of potato leaf diseases can be difficult because of the differences among crop species, variations in symptoms of crop diseases, and the influence of environmental factors. Detecting potato leaf diseases in their initial stages presents a challenge due to these variables. In the proposed model The early and late blight on potato leaves must be identified using a deep learning technique. In the proposed model the plant village datasets are used which comprise 2152 photos that were downloaded and included in the collection for potato leaves. The accuracy of the proposed model is 98%.