Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network
2022 · Sensors
저자
Taewon Moon, Dongpil Kim, Sungmin Kwon, 안태인 (Tae In Ahn), Jung Eek Son
연구주제
NTIS 과학기술표준분류
에너지/자원만에너지·환경·원자력 확장
가장 가까운 분류(농림수산식품 > 원예특용작물과학 > 원예특용작물 시설/재배)가 에너지·환경·원자력 확장 범위 밖이다.
AI 연구 키워드·방법
crop fresh weight monitoring leaf area estimation 파프리카(단고추)
키워드 (OpenAlex)
non-destructive monitoring leaf area estimation sweet pepper Capsicum annuum volumetric water content top-view images convolutional neural networks greenhouse crop monitoring
초록
Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers (Capsicum annuum var. annuum); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neur…
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
| 연도 | 과제 | 부처 | 사업 | 수행기관 | NTIS 분류(1순위) |
|---|---|---|---|---|---|
| 2022 | 농림생물자원 창의인재양성사업단 | 교육부 | 4단계두뇌한국21사업(R&D) | 서울대학 | – |
인접 그래프
노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동