Earticle

초록

영어

Planthopper is a major problem pest of agricultural crops and rice, feeding mainly on the leaves and stems of crops and causing devastating damage to farmers in South Korea. Planthoppers reduce nutrients in the body of crops and cause crop diseases by destroying tissue or transmitting viruses, so accurate detection and diagnosis is essential to minimize the damage. With the development of artificial intelligence in recent years, deep learning has been widely used to diagnose the pests. Most of the pest diagnosis research and programs use approaches based on object detection and image classification. However, traditional planthopper detection models may misidentify other pests as planthoppers, which can reduce user confidence in the diagnostic model. To address this misrecognition problem, this study investigates a deep learning-based planthopper Image detection and discrimination model for detecting and classifying the planthopper images. The proposed model combines the Faster RCNN object detection model and the Resnet50 classification model to automatically detect planthoppers among other pests in aerial entomology net images. The performance measurements showed that the benchmark model using the Faster RCNN algorithm achieved a high Recall of 91.23%, but a relatively low Precision of 24.34%. On the other hand, the model proposed in this study has a high recall of 96.22% and a high precision of 96.73%, proving that it can detect planthoppers well.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. DATASET AND PRE-PROCESSING
IV. DEEP LEARNING-BASED PLANTHOPPER IMAGE DETECTION AND DISCRIMINATION MODEL
A. Planthopper Detection Module
B. Planthopper Image Discrimination Module
C. Performance Metrics
V. RESULTS
VI. CONCLUSIONS
ACKNOWLEDGMENT
REFERENCES

저자

• Ri Zheng [ Department of Computer and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University ]
• JiMin Lee [ Department of Convergence Engineering for Artificial Intelligence Sejong University ]
• Dong Jin [ Department of Computer and Engineering Department of Convergence Engineering for Intelligent Drone Sejong University ]
• HeLin Yin [ Department of Convergence Engineering for Artificial Intelligence Sejong University ]
• Yeong Hyeon Gu [ Department of Convergence Engineering for Artificial Intelligence Sejong University ] Corresponding Author

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