Earticle

초록

영어

This study explores advanced machine learning techniques for improving crop yield prediction in smart farming, utilizing multi-temporal spectral data from drone-based multispectral imagery. Conducted in garlic orchards in Andong, Gyeongbuk Province, South Korea, the research examines the effectiveness of various vegetation indices and cutting-edge models, including LSTM, CNN, Random Forest, and XGBoost. By integrating these models with the Analytic Hierarchy Process (AHP), the study systematically evaluates the factors that influence prediction accuracy. The integrated approach significantly outperforms single models, offering a more comprehensive and adaptable framework for yield prediction. This research contributes to precision agriculture by providing a robust, AI-driven methodology that enhances the sustainability and efficiency of farming practices.

목차

Abstract
1. Introduction
2. Related Studies
3. Data and Methodology
3.1 Data Collection and Preprocessing
3.2 Multispectral Image Acquisition and Processing
3.3 Model Implementation
4. Experimental Design
5. Results and Analysis
5.1 Enhancing Model Interpretability with Explainable AI Techniques
6. Conclusion and Future Research
References

키워드

저자

• Sungwook Yoon [ Division of SW Human Resources Development, Andong University, Instructor, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380