对比多种模型在孟加拉水稻病叶识别中的表现，发现ResNet50最优。
Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures
- 用Transfer Learning提升小数据下的泛化能力
- ResNet50在孟加拉稻叶病害数据集上准确率最高
- 首次系统比较了CNN、Transformer与传统机器学习方法
在孟加拉等农业国家，早期识别和分类植物病害对保障粮食产量与质量至关重要。本文针对孟加拉水稻叶片病害检测问题，使用Dhan-Shomadhan数据集，对比了多种计算机视觉方法：包括CNN（如ResNet50）、Vision Transformer（ViT）及传统机器学习方法（如支持向量机SVM）。通过迁移学习策略，在有限训练数据下提升模型泛化性能。实验表明，ResNet50在各类模型中表现最佳，成为该任务的最优选择。
原文摘要 · Abstract (English)
In nations such as Bangladesh, agriculture plays a vital role in providing livelihoods for a significant portion of the population. Identifying and classifying plant diseases early is critical to prevent their spread and minimize their impact on crop yield and quality. Various computer vision techniques can be used for such detection and classification. While CNNs have been dominant on such image classification tasks, vision transformers has become equally good in recent time also. In this paper we study the various computer vision techniques for Bangladeshi rice leaf disease detection. We use the Dhan-Shomadhan -- a Bangladeshi rice leaf disease dataset, to experiment with various CNN and ViT models. We also compared the performance of such deep neural network architecture with traditional machine learning architecture like Support Vector Machine(SVM). We leveraged transfer learning for better generalization with lower amount of training data. Among the models tested, ResNet50 exhibited the best performance over other CNN and transformer-based models making it the optimal choice for this task.
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