  Recent advancements in deep learning have brought significant improvements to
plant disease recognition. However, achieving satisfactory performance often
requires high-quality training datasets, which are challenging and expensive to
collect. Consequently, the practical application of current deep learning-based
methods in real-world scenarios is hindered by the scarcity of high-quality
datasets. In this paper, we argue that embracing poor datasets is viable and
aim to explicitly define the challenges associated with using these datasets.
To delve into this topic, we analyze the characteristics of high-quality
datasets, namely large-scale images and desired annotation, and contrast them
with the \emph{limited} and \emph{imperfect} nature of poor datasets.
Challenges arise when the training datasets deviate from these characteristics.
To provide a comprehensive understanding, we propose a novel and informative
taxonomy that categorizes these challenges. Furthermore, we offer a brief
overview of existing studies and approaches that address these challenges. We
believe that our paper sheds light on the importance of embracing poor
datasets, enhances the understanding of the associated challenges, and
contributes to the ambitious objective of deploying deep learning in real-world
applications. To facilitate the progress, we finally describe several
outstanding questions and point out potential future directions. Although our
primary focus is on plant disease recognition, we emphasize that the principles
of embracing and analyzing poor datasets are applicable to a wider range of
domains, including agriculture.
