Clustering-Guided Multi-Layer Contrastive Representation Learning for Citrus Disease Classification

TL;DR

This paper introduces a novel clustering-guided multi-layer contrastive learning approach for citrus disease classification, effectively utilizing unannotated data and capturing hierarchical features to improve accuracy and robustness.

Contribution

It proposes a clustering-guided self-supervised contrastive learning method with multi-layer training, advancing citrus disease classification with less reliance on annotated data.

Findings

Achieves state-of-the-art accuracy on the CDD dataset

Outperforms existing methods by 4.5%-30.1% in accuracy

Shows robustness in class imbalance scenarios

Abstract

Citrus, as one of the most economically important fruit crops globally, suffers severe yield depressions due to various diseases. Accurate disease detection and classification serve as critical prerequisites for implementing targeted control measures. Recent advancements in artificial intelligence, particularly deep learning-based computer vision algorithms, have substantially decreased time and labor requirements while maintaining the accuracy of detection and classification. Nevertheless, these methods predominantly rely on massive, high-quality annotated training examples to attain promising performance. By introducing two key designs: contrasting with cluster centroids and a multi-layer contrastive training (MCT) paradigm, this paper proposes a novel clustering-guided self-supervised multi-layer contrastive representation learning (CMCRL) algorithm. The proposed method demonstrates several advantages over existing counterparts: (1) optimizing with massive unannotated samples; (2) effective adaptation to the symptom similarity across distinct citrus diseases; (3) hierarchical feature representation learning. The proposed method achieves state-of-the-art performance on the public citrus image set CDD, outperforming existing methods by 4.5\%-30.1\% accuracy. Remarkably, our method narrows the performance gap with fully supervised counterparts (all samples are labeled). Beyond classification accuracy, our method shows great performance on other evaluation metrics (F1 score, precision, and recall), highlighting the robustness against the class imbalance challenge.