Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision

Title

Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision

Creator

Madgi M.; Danti A.

Description

This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd.

Source

Communications in Computer and Information Science, Vol-1037, pp. 529-537.

Date

2019-01-01

Publisher

Springer Verlag

Subject

Classifier; Color features; Texture features; Vegetable disease

Coverage

Madgi M., K. L. E. Institute of Technology, Hubballi, 580030, India; Danti A., Christ (Deemed to be University), Bengaluru, 560074, India

Rights

Restricted Access

Relation

ISSN: 18650929; ISBN: 978-981139186-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-13-9187-3_47

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070213879&doi=10.1007%2f978-981-13-9187-3_47&partnerID=40&md5=4c49a9444db620b141b87208955a7355