Earticle

초록

영어

Crop diseases and pests are one of the agricultural disasters that adversely affect the yield and quality of crops. To prevent and control them, many researchers have been working on deep learning-based disease and pest recognition. Most of these studies use classification techniques to output one class with the highest probability from a predefined list of pests. However, the accuracy of classification models is not perfect, and they can produce enough incorrect results to require additional aids. In this study, we proposed a novel disease and pest diagnosis pipeline that combines object detection with a similarity-based retrieval model. In our proposed pipeline, we first detect the damaged region in the image and then classify the class to which it belongs. In the similarity-based retrieval model, the detected region image can be used to further show the user the most similar damage symptom images to help them make a final decision. The pipeline proposed in this study was first applied to three diseases: fire blight, scab, and black necrotic leaf spot.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. PROPOSED PIPELINE
A. Dataset
B. Proposed plant disease diagnostic pipeline
IV. EXPERIMENTAL RESULTS
A. Performance of disease detection models
B. Performance of similarity-based image retrieval models
C. Expected scenarios
V. CONCLUSIONS
ACKNOWLEDGMENT
REFERENCES

저자

• Helin Yin [ Department of Artificial Intelligence Sejong University ]
• Dong Jin [ Department of Computer Science and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University ]
• Ri Zheng [ Department of Computer Science and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University ]
• Ji-Min Lee [ Department of Computer Science and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University ]
• Yeong Hyeon Gu [ Department of Artificial Intelligence Sejong University ] Corresponding author

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