• Korean Journal of Environment and Ecology
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• v.28 no.6
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• pp.634-641
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• 2014

In order to elucidate the ecological and phenological responses of bean under global warming situation, we investigated leafing and flowering phenomena and growth traits of Baktae and Seomoktae in control plot (ambient $CO_2$ concentration and temperature) and warmed plot (elevated $CO_2$ concentration and increased temperature than control) in green house. Average $CO_2$ concentration and temperature were set 540 ppm in treatment and higher $2.2^{\circ}C$ in treatment than control. The appearance of leaf unfolding and the initiation of blooming and fruit maturing in Seomoktae were late in treatment than control. In case of Baktae, the number of total pods and seeds per plant in treatment was fewer than that in control. For Seomoktae, the number of total pods per plant and weight of total seeds per plant were decreased in treatment than in control. Thus, the number of pods and seeds of Baktae declined and the production of Seomoktae was decreased under global warming condition. This result indicates that site selection and crop yield for cultivating of the bean may be changed into new area due to global warming condition.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.4
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• pp.373-381
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• 2017

The main objective of this study was to increase the germination percentage of kenaf seeds with less number of times under non-saline and saline conditions. Therefore, the first goal was to assess the response of kenaf seeds to NaCl. The second goal was to evaluate the effects of $KNO_3$ on kenaf seed germination. The germination percentage exhibited a decreasing tendency in germination rate. Plant dry weight was approximately 0.2 g in all treatments at 5 days after germination. As time passed, the electrical conductivity (EC) value of hydro-priming (HP) consistently increased by 8.7 mS/cm at 24 hours of immersion. However, seeds primed with $KNO_3$ showed no difference in EC values even as times passed. Regarding the priming effect, priming in 100 mM $KNO_3$ concentration for 12 hours increased germination up to 85% in $H_20$ solution and in 0 mM $KNO_3$ concentration upto 73.8% under 0.3% NaCl solution, compared to that of Control. Germination synchronization, shoot length, and leaf unfolding of primed seeds were greater than those of the Control. In addition, main root and hair roots appeared more rapidly in the treated seeds and were more abundant compared to that of the Control. The T50 (times to reach 50% of the final germination percentage) of the Control in both $H_20$ and 0.3% NaCl solutions was 18 and 22 hours, respectively. However, when treated $KNO_3$ priming (0 to 100 mM) in $H_20$ and 0.3% NaCl solution, 9 hours was sufficient to reach T50. Primed (hydro-priming and $KNO_3$) seeds had a lower MDG (mean days untill germination; 0.6-0.62) compared to that of the Control (1.13-1.31) in $H_20$ and 0.3% NaCl solutions. Regarding dry weight of plants after priming, an increasing tendency after the priming treatment in the H20 solution was observed. Furthermore, no significant difference in plant dry weight under 0.3% NaCl stress was observed between the Control and primed seeds. Taken together, the results suggest that 50-100 mM $KNO_3$ priming for 24 hours optimize seed germination rate in less number of times of exposure with great vigor. Therefore, it is recommended for kenaf seed invigoration before planting.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.