Detection and classification of plant diseases based on spatially kernelized FCM and support vector machine

Hui Li

doi:10.1117/12.3035628

16 August 2024 Detection and classification of plant diseases based on spatially kernelized FCM and support vector machine

Hui Li

Author Affiliations +

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 1323015 (2024) https://doi.org/10.1117/12.3035628
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

In nature, plants are an essential resource that are crucial to preserving the ecosystem's equilibrium. Crop safety is significantly impacted by plat diseases. This paper proposes an automated technique for plant disease diagnosis and classification using machine learning. The created processing plan is divided into four primary sections. First, we use spatially kernelized fuzzy C-means (SKFCM) to segment the images and find the pixels that are primarily green in color. After initially applying a masking process to green pixels using specific threshold values calculated with Otsu's method, a significant portion of these pixels are further masked. In a subsequent stage, all of the pixels with zero values for red, green, and blue as well as those on the edges of the infected cluster were eliminated. The final step in the categorization process is the application of support vector machines (SVM). The experimental findings show that the suggested method is a reliable one for identifying illnesses in plant leaves. The effectiveness of the created algorithm allows it to accurately identify and categorize the diseases under investigation with a precision of 83% to 94%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui Li "Detection and classification of plant diseases based on spatially kernelized FCM and support vector machine", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 1323015 (16 August 2024); https://doi.org/10.1117/12.3035628

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Diseases and disorders

Image segmentation

Color

Cooccurrence matrices

Education and training

Support vector machines

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years