Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images

Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive. In this paper, we propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kerne...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-01
Main Authors: Uno Fang, Li, Jianxin, Lu, Xuequan, Mumtaz, Ali, Gao, Longxiang, Xiang, Yong
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Clustering
Image classification
Kernels
Machine learning
Medical imaging
Plant diseases
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive. In this paper, we propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means. The main idea is to establish a cross iterative under-clustering algorithm based on Kernel K-means to produce the pseudo-labeled training set and a chaotic cluster to be further classified by a deep learning module. In order to verify the effectiveness of our proposed framework, we conduct extensive experiments on three different plant disease datatsets with five plants and 17 plant diseases. The experimental results show the high superiority of our method to do image-based plant disease classification over balanced and unbalanced datasets by comparing with five state-of-the-art existing works in terms of different metrics.
ISSN:2331-8422