Disease Identification for Tea Leaves Using Explainable Artificial Intelligence

Title

Disease Identification for Tea Leaves Using Explainable Artificial Intelligence

Creator

Choudhury, Rishab; Poonia, Ramesh Chandra; Mehndiratta, Vandana

Description

Infection can consequently reduce both quality and yield, and causes major threats to tea production round the world. It is therefore sometime difficult to achieve fast, reliable, and precise identification of disease in tea plants and hence the need to embrace new methods of disease identification. To enable realisation of accurately understandable models for classification of the diseases in tea leaves, Explainable Artificial Intelligence (XAI) approaches are applied in this work. In order to train and test machine learning models, we collected a set of repos of high-resolution images of tea leaves affected by various diseases along with meta information. CNN models were trained with the help of our approach and adopting XAI tools as tools for explanation of predictions. From this study, the field of agricultural AI is benefitted from the illustration of how XAI might enhance disease management strategies in tea agriculture. The results demonstrate an accuracy of 87.85%, with precision, recall and F1-scores ranging between 0.78 and 0.95 across different classes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Smart Innovation, Systems and Technologies;Volume;121 SIST;pp.199-209

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Agricultural AI; Conventional methods; Explainable artificial intelligence (XAI); Tea industry

Coverage

Choudhury R., CHRIST University Bangalore, Bengaluru, India; Poonia R.C., CHRIST University Bangalore, Bengaluru, India; Mehndiratta V., CHRIST University Bangalore, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 21903018; ISBN: 978-981966253-1;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-6254-8_15

https://www.scopus.com/pages/publications/105018920420?origin=resultslist