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

Frontiers in Plant Science

Plant Science

Jucheng Yang¹  Sook Yoon²  Yao Meng³  Alvaro Fuentes³  Hyongsuk Kim³  Dong Sun Park³  Mingle Xu³  Taehyun Kim⁴ 

[1]College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China

[2]Department of Computer Engineering, Mokpo National University, Muan, Republic of Korea

[3]Department of Electronic Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea

[4]National Institute of Agricultural Sciences, Wanju, Republic of Korea

关键词: plant disease recognition;    AI in Agriculture;    deep learning in agriculture;    smart agriculture;    precision agriculture;   

DOI  :  10.3389/fpls.2023.1225409

 received in 2023-05-19, accepted in 2023-08-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

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 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 point out 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. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.

【 授权许可】

Unknown   
Copyright © 2023 Xu, Kim, Yang, Fuentes, Meng, Yoon, Kim and Park