In this procedure, we recommend the primary causes of a significant reduction in grape production are grape diseases. Therefore, it is critical to create a technique for diagnosing diseases of grape leaves. We use deep learning methods to identify grape diseases because they have recently demonstrated impressive success in a variety of computer vision problems. A Convolutional Neural Network (CNN) architecture built on an integrated approach is suggested in this procedure. The architecture of the suggested CNNs is intended to identify leaves that have common grape diseases. As a result, the performance of the suggested technique will yield better outcomes. The primary goal of this procedure is to use grape leaves to predict plant leaf disease on the other side, to improve the functionality of the procedure

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