Precision agriculture aims to improve agricultural productivity by combining technology and farming. The productivity of the cotton is mostly effected by leaf diseases, if we predict these diseases at early stage it helps the farmers to improve productivity. We put forward a novel method for cotton leaf disease detection that works on hybrid dataset which compromises of images from the kaggle dataset and real-time images. Deep learning models VGG16 and VGG19 are applied on this hybrid dataset for disease detection of cotton leaves. This research will not only contribute to improve crop health but also be a valuable resource for farmers. A systematic comparison of the VGG16 and VGG19 models reveals their functional differences in disease detection. VGG16 and VGG19 has achieved accuracy of 94% and 95% in disease detection