Wheat Grain Identification Using Explainable Artificial Intelligence

Title

Wheat Grain Identification Using Explainable Artificial Intelligence

Creator

Yadav, Ajay; Poonia, Ramesh Chandra; Mehndiratta, Vandana

Description

This research delves into the innovative application of Explainable Artificial Intelligence (XAI) within wheat grain identification. Employing sophisticated machine learning models augmented by XAI techniques, the study aims to enhance the transparency and comprehensibility of decision-making processes associated with classifying wheat grains. Key objectives include refining model accuracy, imparting insights into critical identification-influencing characteristics, and developing an intuitive user interface tailored for end users, particularly farmers. Through a methodical analysis, the research underscores the significance of XAI in detecting flaws and fine-tuning the model, ultimately bolstering its reliability. The findings of this investigation carry implications for advancing agricultural practices, fostering stakeholder trust, and adapting to the ever-evolving dynamics of the environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1122;pp.281-288

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial intelligence; CNN; LIME; SHAP; XAI

Coverage

Yadav A., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Poonia R.C., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Mehndiratta V., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981977425-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-7426-5_21

https://www.scopus.com/pages/publications/85218216277?origin=resultslist