扩充植物病害数据集，提升苹果叶病识别准确率
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks
- 在PlantVillage基础上新增6类苹果叶病图像，提升数据多样性
- 模型在扩展数据集上达97.87% F1分数，验证迁移学习有效性
- 新数据集开源，适合农业视觉研究与模型泛化能力评估
随着全球粮食安全形势演变，精准可靠的作物病害诊断需求日益迫切。为应对这一挑战，本文在广泛使用的PlantVillage数据集基础上，新增了6类苹果叶病图像，显著提升了数据集的多样性与复杂性。在原始和扩展数据集上的实验表明，现有模型对新增类别表现不佳，凸显出开发更鲁棒、泛化能力更强的计算机视觉模型的必要性。模型在原始数据集上取得99.63%的F1分数，在扩展数据集上达到97.87%。本研究构建了一个更具挑战性且多样化的基准数据集，为在不同成像条件下实现准确可靠的苹果叶病识别提供了支持。扩展数据集已公开于https://www.kaggle.com/datasets/akinyemijoseph/apple-leaf-disease-dataset-6-classes-v2，供后续研究使用。
原文摘要 · Abstract (English)
As the global food security landscape continues to evolve, the need for accurate and reliable crop disease diagnosis has never been more pressing. To address global food security concerns, we extend the widely used PlantVillage dataset with additional apple leaf disease classes, enhancing diversity and complexity. Experimental evaluations on both original and extended datasets reveal that existing models struggle with the new additions, highlighting the need for more robust and generalizable computer vision models. Test F1 scores of 99.63% and 97.87% were obtained on the original and extended datasets, respectively. Our study provides a more challenging and diverse benchmark, paving the way for the development of accurate and reliable models for identifying apple leaf diseases under varying imaging conditions. The expanded dataset is available at https://www.kaggle.com/datasets/akinyemijoseph/apple-leaf-disease-dataset-6-classes-v2 enabling future research to build upon our findings.
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