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YOLOv8-G: An Improved YOLOv8 Model for Major Disease Detection in Dragon Fruit Stems.
- Source :
- Sensors (14248220); Aug2024, Vol. 24 Issue 15, p5034, 22p
- Publication Year :
- 2024
-
Abstract
- Dragon fruit stem disease significantly affects both the quality and yield of dragon fruit. Therefore, there is an urgent need for an efficient, high-precision intelligent detection method to address the challenge of disease detection. To address the limitations of traditional methods, including slow detection and weak micro-integration capability, this paper proposes an improved YOLOv8-G algorithm. The algorithm reduces computational redundancy by introducing the C2f-Faster module. The loss function was modified to the structured intersection over union (SIoU), and the coordinate attention (CA) and content-aware reorganization feature extraction (CARAFE) modules were incorporated. These enhancements increased the model's stability and improved its accuracy in recognizing small targets. Experimental results showed that the YOLOv8-G algorithm achieved a mean average precision (mAP) of 83.1% and mAP50:95 of 48.3%, representing improvements of 3.3% and 2.3%, respectively, compared to the original model. The model size and floating point operations per second (FLOPS) were reduced to 4.9 MB and 6.9 G, respectively, indicating reductions of 20% and 14.8%. The improved model achieves higher accuracy in disease detection while maintaining a lighter weight, serving as a valuable reference for researchers in the field of dragon fruit stem disease detection. [ABSTRACT FROM AUTHOR]
- Subjects :
- PITAHAYAS
FEATURE extraction
FRUIT yield
RESEARCH personnel
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 24
- Issue :
- 15
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 178950097
- Full Text :
- https://doi.org/10.3390/s24155034