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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.18 No.3
- 발행연도
- 2024.9
- 수록면
- 152 - 168 (17page)
- DOI
- 10.5626/JCSE.2024.18.3.152
이용수
초록· 키워드
오류제보하기
Weeds need to be removed from the immediate areas surrounding crops as they compete for soil nutrients. Farmers currently clear weeds manually, which is both tiring and imprecise. Therefore, researchers have developed artificial intelligence (AI) using deep learning or non-handcrafted methods to facilitate precise detection. However, these methods have yet to achieve real-time inference speeds. Consequently, this study adopts a handcrafted approach that employs visual leaf features for classification via ensemble learning. The objective is to employ feature selection and data normalization to create an accurate and efficient machine-learning model. The experimental findings obtained in this work demonstrate that Information Gain effectively reduces features by 50%, from 22 to 11, while maintaining accuracy. Chebyshev normalization emerges as the most suitable normalization technique, as it significantly enhances classification accuracy in ensemble learning. The accuracy obtained when using histogram gradient boosting is found to be 0.92 with an inference time of 5.955 ms per image. These outcomes illustrate that handcrafted features achieve higher accuracy than non-handcrafted methods, ultimately improving efficiency and enabling real-time implementation.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. LITERATURE REVIEW
Ⅲ. RESEARCH METHODOLOGY
Ⅳ. RESULTS AND DISCUSSION
Ⅴ. CONCLUSION
REFERENCES
참고문헌 (47)
참고문헌 신청
A. Monteiro and S. Santos, “Sustainable approach to weed management: the role of precision weed management,” Agronomy, vol. 12, no. 1, article no. 118, 2022. https://doi.org/10.3390/agronomy12010118
W. H. Su, “Crop plant signaling for real-time plant identification in smart farm: a systematic review and new concept in artificial intelligence for automated weed control,” Artificial Intelligence in Agriculture, vol. 4, pp. 262-271, 2020. https://doi.org/10.1016/j.aiia.2020.11.001
A. Subeesh and C. R. Mehta, “Automation and digitization of agriculture using artificial intelligence and Internet of Things,” Artificial Intelligence in Agriculture, vol. 5, pp. 278-291, 2021. https://doi.org/10.1016/j.aiia.2021.11.004
A. Nasiri, M. Omid, A. Taheri-Garavand, and A. Jafari, “Deep learning-based precision agriculture through weed recognition in sugar beet fields,” Sustainable Computing: Informatics and Systems, vol. 35, article no. 100759, 2022. https://doi.org/10.1016/j.suscom.2022.100759
F. Garibaldi-Marquez, G. Flores, D. A. Mercado-Ravell, A. Ramirez-Pedraza, and L. M. Valentin-Coronado, “Weed classification from natural corn field-multi-plant images based on shallow and deep learning,” Sensors, vol. 22, no. 8, article no. 3021, 2022. https://doi.org/10.3390/s22083021
W. H. Su, “Crop plant signaling for real-time plant identification in smart farm: a systematic review and new concept in artificial intelligence for automated weed control,” Artificial Intelligence in Agriculture, vol. 4, pp. 262-271, 2020. https://doi.org/10.1016/j.aiia.2020.11.001
A. Subeesh and C. R. Mehta, “Automation and digitization of agriculture using artificial intelligence and Internet of Things,” Artificial Intelligence in Agriculture, vol. 5, pp. 278-291, 2021. https://doi.org/10.1016/j.aiia.2021.11.004
A. Nasiri, M. Omid, A. Taheri-Garavand, and A. Jafari, “Deep learning-based precision agriculture through weed recognition in sugar beet fields,” Sustainable Computing: Informatics and Systems, vol. 35, article no. 100759, 2022. https://doi.org/10.1016/j.suscom.2022.100759
F. Garibaldi-Marquez, G. Flores, D. A. Mercado-Ravell, A. Ramirez-Pedraza, and L. M. Valentin-Coronado, “Weed classification from natural corn field-multi-plant images based on shallow and deep learning,” Sensors, vol. 22, no. 8, article no. 3021, 2022. https://doi.org/10.3390/s22083021
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