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- 주제어 : Plant weight prediction (검색결과 2 건)
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설성관,백영택,조영열 (사) 한국생물환경조절학회 2023 생물환경조절학회지 Vol.32 No.1
Growth modeling in plant factories can not only control stable production and yield, but also control environmental conditions by considering the relationship between environmental factors and plant growth rate. In this study, using the expolinear function, we modeled perilla [Perilla frutescens (L.) Britt.] cultivated in a plant factory. Perilla growth was investigated 12 times until flower bud differentiation occurred after planting under light intensity, photoperiod, and the ratio of mixed light conditions of 130 μmol·m-2·s-1, 12/12 h, red:green:blue (7:1:2), respectively. Additionally, modeling was performed to predict dry and fresh weights using the expolinear function. Fresh and dry weights were strongly positively correlated (r = 0.996). Except for dry weight, fresh weight showed a high positive correlation with leaf area, followed by plant height, number of leaves, number of nodes, leaf length, and leaf width. When the number of days after transplanting, leaf area, and plant height were used as independent variables for growth prediction, leaf area was found to be an appropriate independent variable for growth prediction. However, additional destructive or non-destructive methods for predicting growth should be considered. In this study, we created a growth model formula to predict perilla growth in plant factories.
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Development of a Low-Cost Plant Growth Chamber for Improved Phenotyping Research
이택성,Jung Dae-Hyun,김준용,Lee Ju Young,Park Jai-Eok,김형석,정제형,박수현 한국농업기계학회 2023 바이오시스템공학 Vol.48 No.3
Purpose This study aimed to develop a research-use plant growth chamber from which plant researchers can easily acquire data and in which plants can be effectively grown by improving the sealing and control performance of the plant grower. Methods The low-cost plant growth chamber presented in this paper enables control of internal temperature, LED lighting, and aeroponics and features easy remote operation using open-source technology. Furthermore, it can apply a prediction model for the area and fresh weight using canopy images. We also demonstrated that the small enclosed plant grower could provide the opportunity to improve the efficiency and reproducibility of plant growth for various research aims. Results When used in plant-growth tests, the manufactured prototype achieved over 600 μmol m−2 s−1 photosynthetic photon flux densities (PPFD) and internal temperatures satisfying day/night temperature of 21/17 °C, demonstrating its applicability to grow various plants. Moreover, we confirmed that the sensor and image data storage, download functionality, and image analysis operated normally. Conclusion This prototype is advantageous for researchers who have difficulty investing in commercial phenotype equipment and facilitates access to phenotype data processing through a programming approach using canopy images. In the future, as hardware manufacturing costs continue to drop and performance improves, the proposed prototype provides the potential for plant science researchers to utilize creative application programs, systematic experimental design, and shared datasets and code.
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