Earticle

초록

영어

The recognition of crop diseases and pests based on images is one of the required techniques to identify damage due to diseases and pests and to take efficient management and prior actions. With the development of deep learning technology, the image recognition of diseases and pests using deep learning exhibited excellent performance and plays an important role in managing and controlling diseases and pests of crops. However, research on image recognition of diseases and pests in crops is facing difficulties due to the lack of large-scale datasets about diseases and pests. To solve this problem, this study proposes a system to manage and annotate the images of diseases and pests that can efficiently manage collected disease and pest images to generate high-quality disease and pest datasets. The proposed disease and pest image management and annotation system can collect disease and pest images uploaded from various sources and create statistics of them. It can also provide image inspection and user-friendly annotation functions.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. DISEASE AND PEST IMAGE DATASET MANAGEMENT SYSTEM
A. Data Management and Analysis Module
B. Image Inspection Module
C. Image Annotation Module
IV. CONCLUSION AND FUTURE WORK
REFERENCES

키워드

저자

• Dong Jin [ Department of Computer and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University Seoul, Korea ]
• Ri Zheng [ Department of Computer and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University Seoul, Korea ]
• HeLin Yin [ Department of Computer and Engineering Sejong University Seoul, Korea ]
• Hun Cho [ Dayludens Seoul, Korea ]
• Yeong Hyeon Gu [ Department of Computer and Engineering Sejong University Seoul, Korea ] Corresponding Author
• Seong Joon Yoo [ Department of Computer and Engineering Sejong University Seoul, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004