Pseudo Color Region Features for Plant Disease Detection

Title

Pseudo Color Region Features for Plant Disease Detection

Creator

Jos J.; Venkatesh K.A.

Description

This study reports a novel pseudo color region features for a computer vision system for the identification of diseases in Tomato Plants. The HSV based algorithm identifies eccentric and non- eccentric dots, spots, patches and region of different pseudo colors. Proposed method uses region properties and creates an enhanced and effective feature vector for plant disease detection. The features are more intuitive for humans to understand and help in tuning the underlying Artificial Intelligence Model better. The algorithm uses a scalable data structure to store regions counts using a hash function. It has wide application in the Computer Vision domain. 2020 IEEE.

Source

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Disease Detection; Feature Extraction; Pattern Classification; Pseudo Color Region Properties; Support Vector Machine; Tomato

Coverage

Jos J., Christ University, Department of Computer Science, Bengaluru, India; Venkatesh K.A., Myanmar Institute of Information Technology, Department of Mathematics and Computer Science, Mandalay, Myanmar

Rights

Restricted Access

Relation

ISBN: 978-172819744-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/INOCON50539.2020.9298337

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099534553&doi=10.1109%2fINOCON50539.2020.9298337&partnerID=40&md5=c4175719c33d05156001027081342eec