Unsupervised Transfer Learning for Plant Anomaly Recognition

• 영문명: Unsupervised Transfer Learning for Plant Anomaly Recognition
• 발행기관: 한국스마트미디어학회
• 저자명: Mingle Xu 윤숙(Sook Yoon) 이재수(Jaesu Lee) 박동선(Dong Sun Park)
• 간행물 정보: 『스마트미디어저널』Vol11, No.4, 30~37쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2022.05.31

영문 초록

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.

목차

Ⅰ. Introduction

Ⅱ. Material

Ⅲ. Method

Ⅳ. Experimental results

Ⅴ. Conclusion

키워드

Plant Disease Recognition Unsupervised Transfer Learning Plant Village Dataset Vision Transformer