Towards Improved Performance on Plant Disease Recognition with Symptoms Specific Annotation

• 발행기관: 한국스마트미디어학회
• 저자명: Jiuqing Dong Alvaro Fuentes 윤숙(Sook Yoon) 김태현(Taehyun Kim) 박동선(Dong Sun Park)
• 간행물 정보: 『스마트미디어저널』Vol11, No.4, 38~45쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2022.05.31

영문 초록

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.

목차

Ⅰ. Introduction

Ⅱ. Related Work

Ⅲ. Material and Methods

Ⅳ. Experimental Results

Ⅴ. Conclusion

키워드

plant disease data quality annotation technique data centric