Improving the Recognition of Known and Unknown Plant Disease Classes Using Deep Learning

• 영문명: Improving the Recognition of Known and Unknown Plant Disease Classes Using Deep Learning
• 발행기관: 한국스마트미디어학회
• 저자명: Yao Meng Jaehwan Lee Alvaro Fuentes Mun Haeng Lee Taehyun Kim Sook Yoon Dong Sun Park
• 간행물 정보: 『스마트미디어저널』Vol13, No.8, 16~25쪽, 전체 10쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2024.08.30

국문 초록

Recently, there has been a growing emphasis on identifying both known and unknown diseases in plant disease recognition. In this task, a model trained only on images of known classes is required to classify an input image into either one of the known classes or into an unknown class. Consequently, the capability to recognize unknown diseases is critical for model deployment. To enhance this capability, we are considering three factors. Firstly, we propose a new logits-based scoring function for unknown scores. Secondly, initial experiments indicate that a compact feature space is crucial for the effectiveness of logits-based methods, leading us to employ the AM-Softmax loss instead of Cross-entropy loss during training. Thirdly, drawing inspiration from the efficacy of transfer learning, we utilize a large plant-relevant dataset, PlantCLEF2022, for pre-training a model. The experimental results suggest that our method outperforms current algorithms. Specifically, our method achieved a performance of 97.90 CSA, 91.77 AUROC, and 90.63 OSCR with the ResNet50 model and a performance of 98.28 CSA, 92.05 AUROC, and 91.12 OSCR with the ConvNext base model. We believe that our study will contribute to the community.

목차

Ⅰ. INTRODUCTION

Ⅱ. MATERIAL AND METHODS

Ⅲ. EXPERIMENTS and VISUALIZATION

Ⅳ. CONCLUSION

REFERENCES

키워드

deep learning plant disease recognition transfer learning unknown disease recognition