Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.

ABSTRACT: 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 aims 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 limited and imperfect nature of poor datasets. Challenges arise when the train

SUBMITTER: Xu M 

PROVIDER: S-EPMC10557492 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature