Earticle

초록

영어

This study addresses the issue of apple detection and segmentation, which plays a crucial role in agricultural automation systems, by employing multi-object detection using machine learning. A Support Vector Machine (SVM) model was used to accurately distinguish apples from leaves, with apple pixels classified as red and leaf pixels as green. The performance of the SVM model was evaluated using various metrics. Key evaluation metrics included IoU (Intersection over Union), Precision, Recall, and mAP (mean Average Precision). The results showed an IoU of 0.48, a Precision of 0.51, a Recall of 0.90, and an mAP of 0.48. Consequently, the SVM model exhibited a high recall rate, successfully detecting most apples, but also had a high false-positive rate due to its low precision. In the future, the need for models that can simultaneously handle real-time processing and accurate boundary recognition is emerging, which could address a critical issue in agricultural automation systems.

목차

Abstract
1. INTRODUCTION
2. RESEARCH METHODOLOGY
2.1 SVM Model
3. SVM MODEL DESIGN
4. IMPLEMENTATION
4.1. Development Environment
4.2 Dataset
4.3 Evaluation Metrics
4.4 Results
5. CONCLUSION
REFERENCES

키워드

저자

• Kyu-Ha Kim [ Department of Computer Engineering, Honam University, Korea ]
• Sang-Hyun Lee [ Associate Prof., Department of Computer Engineering, Honam University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700