Version 1
: Received: 20 March 2024 / Approved: 20 March 2024 / Online: 20 March 2024 (12:31:55 CET)
How to cite:
Ding, J.; Jeon, W.; Rhee, S.; Zou, C. DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8. Preprints2024, 2024031227. https://doi.org/10.20944/preprints202403.1227.v1
Ding, J.; Jeon, W.; Rhee, S.; Zou, C. DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8. Preprints 2024, 2024031227. https://doi.org/10.20944/preprints202403.1227.v1
Ding, J.; Jeon, W.; Rhee, S.; Zou, C. DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8. Preprints2024, 2024031227. https://doi.org/10.20944/preprints202403.1227.v1
APA Style
Ding, J., Jeon, W., Rhee, S., & Zou, C. (2024). DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8. Preprints. https://doi.org/10.20944/preprints202403.1227.v1
Chicago/Turabian Style
Ding, J., Sang-Yong Rhee and Chang-Man Zou. 2024 "DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8" Preprints. https://doi.org/10.20944/preprints202403.1227.v1
Abstract
In light of the prevalent pest and disease issues faced by greenhouse cucumbers, a staple vegetable during winter, this study introduces a detection method based on the enhanced YOLOv8s model. This method aims to provide technical support for detecting and classifying pests and diseases in cucumber agricultural production. The model integrates the 'MultiCat' module for multiscale feature fusion and employs the 'C2fe' and 'ADC2f'modules to strengthen spatial and channel attention. The 'Block2d' function also facilitates the choice between average pooling and attention-based spatial pooling. Channel fusion is achieved through additive and multiplicative operations, allowing the model to delve deeper into feature learning. Experimental results confirm that our approach outperforms the original YOLOv8s model in pest detection, particularly excelling in the identification of small-scale and overlapping afflictions.
Keywords
component; yolov8s; MultiCat; C2fe; ADC2f; Pest detection
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
