研究目的
To improve plant production in plant factories through the detection and utilization of circadian rhythms.
研究成果
Circadian rhythms are crucial for improving plant production in plant factories. Technologies for detecting and utilizing these rhythms, such as seedling diagnosis and growth prediction systems, can enhance productivity and quality. Further research is needed to expand these technologies to more plant species and cultivation conditions.
研究不足
The study focuses on specific plants (Lactuca sativa L. and tomato) and may not be directly applicable to all plant species. The high-throughput systems require significant initial setup and investment.
1:Experimental Design and Method Selection:
The study employs technologies based on circadian rhythms for seedling diagnosis, growth prediction, and transcriptome analysis.
2:Sample Selection and Data Sources:
Uses Lactuca sativa L. seedlings and tomato leaves for analysis.
3:List of Experimental Equipment and Materials:
Includes CMOS camera, blue LED panels, RFID system, and digital input/output unit for seedling diagnosis system.
4:Experimental Procedures and Operational Workflow:
Describes the process of measuring CF, analyzing gene expression, and controlling circadian rhythms through environmental stimuli.
5:Data Analysis Methods:
Utilizes machine learning for growth prediction and the molecular timetable method for analyzing circadian rhythms.
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