Earticle

초록

영어

As there is no cure for fire blight, which mainly affects pears and apples, effective and rapid detection is very important. Existing fire blight diagnostic studies usually used biotechnology, such as immunodiagnostic kits. With the development of deep learning-based image recognition technology, an image-based fire blight diagnosis method has been proposed. For the diagnosis of diseases that have similar symptoms, including fire blight, this study developed a disease recognition model using the deep convolutional neural network (CNN). Fine-tuning was performed on VGG16, VGG19, ResNet50, DenseNet121, Inception-ResNet v2, NASNet and EfficientNet models, which were pre-trained through ImageNet dataset. The experiment used 14,304 images of six diseases collected from pear and apple as the dataset. As a result of the experiment, all seven fine-tuned models achieved an accuracy of more than 90%, among which the ResNet50 model achieved the highest accuracy at 98.83%. It is anticipated that the proposed model can be valuably used at actual farmhouses to diagnose and prevent fire blight through appropriate services in the future.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. MATERIALS AND METHODS
A. Dataset
B. CNN Model and Fine-tuning
IV. EXPERIMENTS
A. Evaluation Metrics
B. Experiment Result
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Dong Jin [ Department of Compyter Science and Enginerring Sejong University ]
• Helin Yin [ Department of Compyter Science and Enginerring Sejong University ]
• Yeong Hyeon Gu [ Department of Compyter Science and Enginerring Sejong University ]
• Seong Joon Yoo [ Department of Compyter Science and Enginerring Sejong University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004