A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection.

Abstract

Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement restricts the model's adaptability when encountering samples from unseen disease categories. Additionally, there is a challenge of knowledge degradation for these static learning settings, as the acquisition of new knowledge tends to overwrite the old when learning new categories. To overcome these limitations, this study introduces a novel paradigm for plant disease detection called open-world setting. Our approach can infer disease categories that have never been seen during the model training phase and gradually learn these unseen diseases through dynamic knowledge updates in the next training phase. Specifically, we utilize a well-trained unknown-aware region proposal network to generate pseudo-labels for unknown diseases during training and employ a class-agnostic classifier to enhance the recall rate for unknown diseases. Besides, we employ a sample replay strategy to maintain recognition ability for previously learned classes. Extensive experimental evaluation and ablation studies investigate the efficacy of our method in detecting old and unknown classes. Remarkably, our method demonstrates robust generalization ability even in cross-species disease detection experiments. Overall, this open-world and dynamically updated detection method shows promising potential to become the future paradigm for plant disease detection. We discuss open issues including classification and localization, and propose promising approaches to address them. We encourage further research in the community to tackle the crucial challenges in open-world plant disease detection. The code will be released at https://github.com/JiuqingDong/OWPDD.

Plant phenotyping relevance

植物の病徴・病害状態を画像から検出する手法の開発と評価が中心であり、未知病害への検出・学習、アブレーションおよび交差種評価を行っているため、植物フェノタイピング手法として採用する。

Code and data availability

The paper's authors state their open-world plant disease detection code will be released at a public GitHub repository (JiuqingDong/OWPDD). Detectron2 is a generic third-party library, not a paper-specific asset. No separate phenotype dataset deposit is stated beyond the cited prior tomato/paprika datasets.