A Diversity-optimized Deep Ensemble Approach for Accurate Plant Leaf Disease Detection

TL;DR

This paper introduces a novel diversity metric called SQ that improves the selection of neural network ensembles, leading to more accurate plant leaf disease detection from images.

Contribution

The paper proposes the SQ diversity metric, addressing limitations of existing metrics, and demonstrates its effectiveness in enhancing ensemble accuracy for plant disease detection.

Findings

SQ metric outperforms existing diversity metrics in ensemble selection

Ensembles using SQ achieve higher detection accuracy on plant leaf images

The approach improves reliability and efficiency of image-based plant disease diagnosis

Abstract

Plant diseases pose a significant threat to global agriculture, causing over $220 billion in annual economic losses and jeopardizing food security. The timely and accurate detection of these diseases from plant leaf images is critical to mitigating their adverse effects. Deep neural network Ensembles (Deep Ensembles) have emerged as a powerful approach to enhancing prediction accuracy by leveraging the strengths of diverse Deep Neural Networks (DNNs). However, selecting high-performing ensemble member models is challenging due to the inherent difficulty in measuring ensemble diversity. In this paper, we introduce the Synergistic Diversity (SQ) framework to enhance plant disease detection accuracy. First, we conduct a comprehensive analysis of the limitations of existing ensemble diversity metrics (denoted as Q metrics), which often fail to identify optimal ensemble teams. Second, we present the SQ metric, a novel measure that captures the synergy between ensemble members and consistently aligns with ensemble accuracy. Third, we validate our SQ approach through extensive experiments on a plant leaf image dataset, which demonstrates that our SQ metric substantially improves ensemble selection and enhances detection accuracy. Our findings pave the way for a more reliable and efficient image-based plant disease detection.