  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.
