Detection of unknown strawberry diseases based on OpenMatch and two-head network for continual learning - Jiang, Kan; You, Jie; Dorj, Ulzii-Orshikh; Kim, Hyongsuk; Lee, Joonwhoan (Frontiers in Plant Science, 2022)
For continual learning in the process of plant disease recognition, first need to distinguish unknown diseases or conditions from known diseases. This paper deals with two different but related deep learning techniques for detection of unknown of plant diseases; Open Set Recognition (OSR) and Out-of-Distribution (OoD) detection. Despite the significant progress in OSR, it is still premature to apply it to fine-grained recognition tasks without outlier exposure that a certain part of OoD data (also called known unknowns) are prepared for training. On the other hand, OoD detection need intentionally prepared outlier data during training. This paper analyzes two-head network using OoD detection and semi-supervised OpenMatch using OSR technology, which explicitly and implicitly assume outlier exposure, respectively. For the experiment, we built an image dataset of eight strawberry diseases. In general, a two-head network and OpenMatch cannot be compared due to different training settings. In our experiment, we changed their training procedures to make them similar for comparison, and show that modified training procedures resulted in reasonable performance, including more than 90% accuracy for strawberry disease classification as well as detection of unknown diseases. Accurate detection of unknown diseases is an important prerequisite for continued learning.