Hyunseung Hwang

Growth and quality of sprouts of six vegetables cultivated under different light intensity and quality

Vegetable sprouts (alfalfa, broccoli, clover, kohlrabi, radish, and red radish) were cultivated under three monochromatic light regimes (red, green, and blue) with five different light intensities (0, 12.5, 25, 50, and 100 µmol·m−2·s−1) to investigate the effects of light intensity and quality on the growth and total phenolic content of the vegetable sprouts. The light quality and intensity had a direct effect on hypocotyl elongation in vegetable sprouts. Increasing blue light intensity enhanced suppression of hypocotyl elongation in all of the vegetable sprouts. Red light also reduced hypocotyl length in alfalfa and clover sprouts comparing those grown in darkness. The fresh weight of broccoli and radish sprouts markedly increased when red light intensity was 100 µmol·m−2·s−1; however, light use efficiency (LUE) decreased with increasing light intensity. Total phenolic content was reduced by increasing red light intensity in alfalfa and red radish sprouts.

Effect of LED mixed light conditions on the glucosinolate pathway in brassica rapa

In the agricultural industries, LEDs are used as supplementary, as well as main lighting sources in closed cultivation systems. In cultivation using artificial light sources, various light qualities have been tried to supplement fluorescent lamps to promote plant growth and metabolism. Microarray analysis of Brassica rapa seedlings under blue and fluorescent mixed with blue light conditions identified changes in three genes of the glucosinolate pathway. This attracted attention as functional materials highly expressed 3.6-4.6 fold under latter condition. We selected four more genes of the glucosinolate pathway from the Brassica database and tested their expression changes under fluorescent light mixed with red, green, and blue, respectively. Some genes increased expression under red and blue mixed conditions. The Bra026058, Bra015379, and Bra021429; the orthologous genes of CYP79F1, ST5a, and FMOGS-OX1 in Arabidopsis, are highly expressed in Brassica rapa under fluorescent mixed with blue light conditions. Further, Bra029355, Bra034180, Bra024634, and Bra022448; the orthologous genes of MAM1, AOP3, UGT74B1, and BCAT4 in Arabidopsis, are highly expressed in Brassica rapa under fluorescent mixed with red light conditions. The various light conditions had unique effects on the varieties of Brassica, resulting in differences in glucosinolate synthesis. However, in some varieties, glucosinolate synthesis increased under mixed blue light conditions. These results will help to construct artificial light facilities, which increase functional crops production.

Correlations between the Growth Period and Fresh Weight of Seed Sprouts and Pixel Counts of Leaf Area

【Purpose: This study was carried out to predict the growth period and fresh weight of sprouts grown in a cultivator designed to grow sprouts under optimal conditions. Methods: The temperature, light intensity, and amount of irrigation were controlled, and images of seed sprouts were acquired to predict the days of growth and weight from pixel counts of leaf area. Broccoli, clover, and radish sprouts were selected, and each sprout was cultivated in a 90-mm-diameter Petri dish under the same cultivating conditions. An image of each sprout was taken every 24 hours from the 4th day, and the whole cultivating period was 6 days, including 3 days in the dark. Images were processed by histogram inspection, binary images, image erosion, image dilation, and the overlay image process. The RGB range and ratio of leaves were adjusted to calculate the pixel counts for leaf area. Results: The correlation coefficients between the pixel count of leaf area and the growth period of sprouts were 0.91, 0.98, and 0.97 for broccoli, clover, and radish, respectively. Further, the correlation coefficients between the pixel count of leaf area and fresh weight were 0.90 for broccoli, 0.87 for clover, and 0.95 for radish. Conclusions: On the basis of these results, we suggest that the simple image acquisition system and processing algorithm can feasibly estimate the growth period and fresh weight of seed sprouts.】