用新方法选对模型组合，让植物病害识别更准更快
A Diversity-optimized Deep Ensemble Approach for Accurate Plant Leaf Disease Detection
- 设计新指标衡量模型间互补性，更好选出高效组合
- 在真实数据集上使病害识别准确率显著提升
- 适合农业图像分析、模型集成研究者参考
植物病害每年造成超过2200亿美元经济损失，威胁全球粮食安全。及时准确地从叶片图像中检测病害至关重要。深度神经网络集成（Deep Ensembles）通过融合多个深度神经网络的优势来提高预测精度，但如何选择性能优且多样化的成员模型仍具挑战，因现有多样性度量（记为Q指标）常无法找到最优组合。本文提出协同多样性（Synergistic Diversity, SQ）框架：首先系统分析现有度量的局限性；其次提出新型SQ指标，能有效捕捉成员间的协同效应，与集成准确率高度一致；最后在植物叶片图像数据集上通过大量实验验证，SQ方法显著提升了集成选择效果，增强了检测精度。研究成果为更可靠高效的基于图像的植物病害检测提供了新路径。
原文摘要 · Abstract (English)
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.
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