A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning.

ABSTRACT: Esca is one of the most common disease that can severely damage grapevine. This disease, if not properly treated in time, is the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Esca is carried out manually through visual surveys usually done by agronomists, requiring enormous amount of time. Recently, image processing, computer vision and machine learning methods have been widely adopted for plant diseases classification. These methods can minimize the time spent for anomaly detection ensuring an early detection of Esca disease in grapevine plants that helps in preventing it to spread in the vineyards and in minimizing the financial loss to th

SUBMITTER: Alessandrini M 

PROVIDER: S-EPMC7881216 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature