Title
Design and implementation of a CNN architecture to classify images of banana leaves with diseases
Date Issued
22 March 2021
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
Piura is an agricultural region, and therefore, crop production is one of the primary sources of income. Competition in this sector has been growing in recent years, and Piura cannot be left behind. Due to factors such as diseases, pest attacks, and sudden changes in climatic conditions, the level of crop production decreases. Automatic recognition of plant diseases is essential to automatically detect disease symptoms as soon as they appear in the growing stage. This paper provides a proposed methodology for the analysis and detection of banana leaf diseases using digital image processing techniques. The results obtained show that the proposed system can successfully detect and classify two major banana leaf diseases: Black Sigatoka (BBS) and Bacterial Wilt (BBW).
Language
English
OCDE Knowledge area
Otras ingenierías y tecnologías
Ciencias de las plantas, Botánica
Protección y nutrición de las plantas
Agricultura
Subjects
Scopus EID
2-s2.0-85114205829
ISBN
9781665401272
Resource of which it is part
2021 IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021
ISBN of the container
978-166540127-2
Conference
IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021
Sponsor(s)
Partially supported by DFG grant SA 933/5-1, and the ‘Concept for the Future’ of Karlsruhe Institute of Technology within the framework of the German Excellence Initiative.
Sources of information:
Directorio de Producción Científica
Scopus