Implementation of Detection and Classification System for Sudden Pest Using Object Detection Algorithm 


Vol. 48,  No. 6, pp. 704-711, Jun.  2023
10.7840/kics.2023.48.6.704


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  Abstract

To reduce the damage caused by the recent surge in Sudden pest due to climate change, we have built a detection and classification system of Sudden pest using raspberry Pi. Through this, even non-experts who are difficult to determine the type of pest can recognize the occurrence of Sudden pest and respond quickly and appropriately to Sudden pests appearing in farmers. In this paper, we trained YOLOv5s and YOLOv5x on a dataset consisting of four types of Sudden pest: Ricania sublimata, fall armyworm, Citrus flatid planthopper, and Spotted lanternfly to perform detection of Sudden pests. Through the mean Average Precision and Precesion-Recall curve of the two models, it was confirmed that YOLOv5x was superior to YOLOv5s in terms of precision, and prediction results based on the final model were derived.

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[IEEE Style]

Y. Jeong, S. Kim, D. Kim, "Implementation of Detection and Classification System for Sudden Pest Using Object Detection Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 704-711, 2023. DOI: 10.7840/kics.2023.48.6.704.

[ACM Style]

Yuna Jeong, Se-Ha Kim, and Dong-Hoi Kim. 2023. Implementation of Detection and Classification System for Sudden Pest Using Object Detection Algorithm. The Journal of Korean Institute of Communications and Information Sciences, 48, 6, (2023), 704-711. DOI: 10.7840/kics.2023.48.6.704.

[KICS Style]

Yuna Jeong, Se-Ha Kim, Dong-Hoi Kim, "Implementation of Detection and Classification System for Sudden Pest Using Object Detection Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 704-711, 6. 2023. (https://doi.org/10.7840/kics.2023.48.6.704)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880