지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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The objective of this research was to develop a model to measure the external quality traits using Multi-Spectral Image Sensor (MSIS) in lettuce. In addition, this study was conducted to examine the effect of different intensities of LED mixed light on the growth of lettuce in a closed-type plant factory. The prediction models of lettuce growth data (shoot fresh weight, shoot dry weight, and leaf area) were developed using the correlation between growth data and the number of pixels on an acquired image. Also, SPAD value prediction models were developed using the correlation between the reflectance of a lettuce leaf in an acquired multi-spectral image and the measured SPAD value of the lettuce leaf. The characteristics of lettuce growth according to the light intensity in a closed-type plant factory was conducted under light intensities of 100, 150, and 200 μmol · m<SUP>-2</SUP> · s<SUP>-1</SUP>. At 200 μmol · m<SUP>-2</SUP> · s<SUP>-1</SUP> light intensity, the shoot fresh weight increased about 27.37-73.93% when compared to 100 and 150 μmol · m<SUP>-2</SUP> · s<SUP>-1</SUP> on 20 days after sowing. The shoot dry weight showed a similar tendency. Lettuce leaf area increased by 6.89-52.52% on 20 days after sowing when compared to plants grown under 100 and 150 μmol · m<SUP>-2</SUP> · s<SUP>-1</SUP>. At a light intensity of 200 μmol · m<SUP>-2</SUP> · s<SUP>-1</SUP>, the SPAD value increased about 3.56-23.76% when compared to plants grown under 100 and 150 μmol · m<SUP>-2</SUP> · s<SUP>-1</SUP>. Using prediction models based on the number of pixels on a lettuce leaf image, lettuce growth data (shoot fresh weight, shoot dry weight, and leaf area) can be predicted with high precision. In addition, the prediction models for SPAD value were developed using the vegetation indices NDVI (Normalized Difference Vegetation Index), GNDVI (Green NDVI), NIR/Red ratio, NIR/Green ratio, and Red/Green ratio. The SPAD values were predicted with relatively high precision (R² = 0.834) using the prediction model based on NIR/Red.
목차
Abstract
Introduction
Materials and Methods
Results
Discussion
Literature Cited
참고문헌 (108)
참고문헌 신청
Ahm CK, Cho BK, Mo CY, Kim MS (2012a) Study on development of non-desturctive measurement technique for viability of lettuce seed (Lactuca sativa L.) using hyperspectral reflectance imaging. J Korean Soc Nondestruc Test 32:512-525
Ahm CK, Cho BK, Mo CY, Kim MS (2012a) Study on development of non-desturctive measurement technique for viability of lettuce seed (Lactuca sativa L.) using hyperspectral reflectance imaging. J Korean Soc Nondestruc Test 32:512-525
Ahm CK, Baek IS, Mo CY, Kang SW, Kim MS, Cho BK (2012b) Development of non-destructive quality measurement technique for cabbage seed (Brassica campestris L.) using hyperspectral reflectance imaging. Food Eng Prog 16:257-262
Ahm CK, Baek IS, Mo CY, Kang SW, Kim MS, Cho BK (2012b) Development of non-destructive quality measurement technique for cabbage seed (Brassica campestris L.) using hyperspectral reflectance imaging. Food Eng Prog 16:257-262
J. W. Choi, “Quest for Smart Farm Tech in Agriculture and Aquaculture Industries,” Institute of Control, Robotics and Systems Magazine (in Korean), vol. 22, no. 3, pp. 17-20, Oct. 2016.
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