Grapevine leaf species and disease detection using DNN

Abstract

The cultivation of grapes is one of India’s most important produce industries. The grapes comprise 1.2% of the country’s total produce production area. This accounts for 2.8% of the nation’s total fruit production. Maharashtra produces approximately 80% of India’s grapes, followed by Karnataka and Tamil Nadu. However, grape leaf maladies have impeded the growth of the grape industry and resulted in significant economic losses. Disease and pest control experts have, therefore, given considerable thought to identifying and analyzing grapevine leaf maladies. This article examines the image dataset of grapevine foliage. The dataset contains images of grapevine leaves infected with three distinct diseases: black, Esca (Black Measles), and leaf blight (Isariopsis Leaf Spot). This paper examines the efficacy of CNN-based algorithms for grapevine species identification and disease detection. The experimental findings demonstrate that the proposed model can accurately identify grape leaf varieties and their associated diseases