用注意力机制和数据增强提升洋葱病虫害多分类准确率
Handling imbalance and few-sample size in ML based Onion disease classification
- 在预训练CNN中加入注意力模块，缓解数据不平衡问题
- 在真实田间图像上达到96.90%准确率和0.96 F1分数
- 适合需要精准识别具体病虫害的智慧农业场景
精准识别病虫害对精准农业至关重要，可实现高效诊断、靶向干预并防止扩散。然而，现有方法多聚焦于二分类，限制了实际应用，尤其在需精确识别具体病害类型时。本文提出一种基于深度学习的洋葱作物病虫害多分类鲁棒模型。通过在预训练卷积神经网络（CNN）中集成注意力模块，并采用全面的数据增强策略以缓解类别不平衡。该模型在真实田间图像数据集上取得96.90%的整体准确率和0.96的F1分数，优于使用相同数据集的其他方法。
原文摘要 · Abstract (English)
Accurate classification of pests and diseases plays a vital role in precision agriculture, enabling efficient identification, targeted interventions, and preventing their further spread. However, current methods primarily focus on binary classification, which limits their practical applications, especially in scenarios where accurately identifying the specific type of disease or pest is essential. We propose a robust deep learning based model for multi-class classification of onion crop diseases and pests. We enhance a pre-trained Convolutional Neural Network (CNN) model by integrating attention based modules and employing comprehensive data augmentation pipeline to mitigate class imbalance. We propose a model which gives 96.90% overall accuracy and 0.96 F1 score on real-world field image dataset. This model gives better results than other approaches using the same datasets.
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