Department of Electronic Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, 54896, South Korea.
Sci Rep. 2024 Aug 2;14(1):17900. doi: 10.1038/s41598-024-66958-2.
Plant diseases pose significant threats to agriculture, impacting both food safety and public health. Traditional plant disease detection systems are typically limited to recognizing disease categories included in the training dataset, rendering them ineffective against new disease types. Although out-of-distribution (OOD) detection methods have been proposed to address this issue, the impact of fine-tuning paradigms on these methods has been overlooked. This paper focuses on studying the impact of fine-tuning paradigms on the performance of detecting unknown plant diseases. Currently, fine-tuning on visual tasks is mainly divided into visual-based models and visual-language-based models. We first discuss the limitations of large-scale visual language models in this task: textual prompts are difficult to design. To avoid the side effects of textual prompts, we futher explore the effectiveness of purely visual pre-trained models for OOD detection in plant disease tasks. Specifically, we employed five publicly accessible datasets to establish benchmarks for open-set recognition, OOD detection, and few-shot learning in plant disease recognition. Additionally, we comprehensively compared various OOD detection methods, fine-tuning paradigms, and factors affecting OOD detection performance, such as sample quantity. The results show that visual prompt tuning outperforms fully fine-tuning and linear probe tuning in out-of-distribution detection performance, especially in the few-shot scenarios. Notably, the max-logit-based on visual prompt tuning achieves an AUROC score of 94.8 in the 8-shot setting, which is nearly comparable to the method of fully fine-tuning on the full dataset (95.2 ), which implies that an appropriate fine-tuning paradigm can directly improve OOD detection performance. Finally, we visualized the prediction distributions of different OOD detection methods and discussed the selection of thresholds. Overall, this work lays the foundation for unknown plant disease recognition, providing strong support for the security and reliability of plant disease recognition systems. We will release our code at https://github.com/JiuqingDong/PDOOD to further advance this field.
植物病害对农业构成重大威胁，影响食品安全和公共健康。传统的植物病害检测系统通常仅限于识别训练数据集中包含的病害类别，因此对于新型病害类型无效。尽管已经提出了异常检测方法来解决这个问题，但微调范式对这些方法的影响尚未得到关注。本文专注于研究微调范式对检测未知植物病害性能的影响。目前，视觉任务的微调主要分为基于视觉的模型和基于视觉语言的模型。我们首先讨论了大规模视觉语言模型在这项任务中的局限性：文本提示难以设计。为了避免文本提示的副作用，我们进一步探索了纯粹基于视觉的预训练模型在植物病害任务中的异常检测的有效性。具体来说，我们使用了五个公开可用的数据集，为开放集识别、异常检测和植物病害识别中的少样本学习建立了基准。此外，我们全面比较了各种异常检测方法、微调范式以及影响异常检测性能的因素，例如样本数量。结果表明，在异常检测性能方面，视觉提示微调优于完全微调和线性探针微调，尤其是在少样本情况下。值得注意的是，基于最大对数的视觉提示微调在 8 -shot 设置下的 AUROC 评分为 94.8，几乎可与在全数据集上完全微调的方法（95.2）相媲美，这意味着适当的微调范式可以直接提高异常检测性能。最后，我们可视化了不同异常检测方法的预测分布，并讨论了阈值的选择。总的来说，这项工作为未知植物病害识别奠定了基础，为植物病害识别系统的安全性和可靠性提供了有力支持。我们将在 https://github.com/JiuqingDong/PDOOD 上发布我们的代码，以进一步推动该领域的发展。
