이미지 기반의 식물 인식 기술 동향|RISS 상세보기

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アァカサザタダナハバパマヤャラワヮンイィキギシジチヂニヒビピミリウゥクグスズツヅッヌフブプムユュルエェケゲセゼテデヘベペメレオォコゴソゾトドノホボポモヨョロヲ ―

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中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.

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이미지 기반의 식물 인식 기술 동향 = Trends of Plant Image Processing Technology

한글로보기

https://www.riss.kr/link?id=A105549679

저자

윤여찬 ; 상종희 ; 박수명 ; Yoon, Y.C. ; Sang, J.H. ; Park, S.M.
발행기관
한국전자통신연구원
학술지명
전자통신동향분석(Electronics and telecommunications trends)
권호사항

Vol.33 No.4 [2018]
발행연도
2018
작성언어
Korean
자료형태
학술저널
수록면

54-60(7쪽)
DOI식별코드
https://doi.org/10.22648/etri.2018.j.330406
제공처
ScienceON
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다국어 초록 (Multilingual Abstract)

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

번역하기

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so...

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.