Earticle

초록

영어

Traditional methods for plant phenotypic analysis, including attributes such as color, plant health status, and size, rely on expert manual analysis and judgment. Measurements are manually taken using tools, and all data is recorded by hand, which is highly inefficient. However, the development of artificial intelligence and deep learning has provided efficient solutions for plant phenotypic analysis. This study established a framework based on high-resolution images and utilized the SEGMENT ANYTHING MODEL (SAM) and Explainable Contrastive Language-Image Pretraining (ECLIP) for plant phenotypic analysis. Through the segmentation results of this model, the length and width of radishes, cucumbers, and pumpkins were measured. Since this method performed experiments without requiring model training or manual annotation of data, the framework demonstrated strong efficiency in the segmentation tasks prior to phenotypic analysis, significantly reducing manual labor costs. Moreover, the experimental results indicated that the mean absolute error (MAE) was below 0.05 for most test samples.

목차

Abstract
I. INTRODUCTION
II. DATASET
III. METHODOLOGY
A. Preprocessing
B. Point Prompt Generation
C. SAM
D. Phenotypic Trait Measurement
IV. EXPERIMENTAL RESULTS
A. Segmentation Performance Analysis
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Taicheng Jin [ Department of Computer Science and Engineering, Sejong University Seoul 05006, Republic of Korea ]
• Wenqi Zhang [ Department of Computer Science and Engineering, Sejong University Seoul 05006, Republic of Korea ]
• Nur Alam [ Department of Computer Science and Engineering, Sejong University ]
• L. Minh Dang [ Department of Computer Science and Engineering, Sejong University ]
• Hyeonjoon Moon [ Department of Computer Science and Engineering, Sejong University ] Corresponding Author

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