Plant phenomics have become essential component of modern plant science. Such complex data sets are critical for understanding the mechanisms governing energy intake and storage in plants. Large-scale phenotyping techniques have been developed to conduct high-throughput phenotyping on plants. However, a major issue facing these efforts is the determination of the quality of phenotypic data. Automated methods are needed to identify and characterize alteractions caused by system errors, all of which are difficult to remove in the data collection step. Another issue is we are limited by the tools to analyze fully the phenomics data, esp. the dynamic relationships between environments and phenotypes.The overarching goal of this thesis is to explore dynamic phenotype-environmental datavia data mining/machine learning methods. Raw data measured from biological devices is pre-processed to numerical data, then cleaned by Dynamic Filter to ensure high data quality for further analysis. The cleaned data is further explored and applied with inter-functional analysis in order to find patterns that comply with both machine learning methodologies and biological constraints.In this thesis we developed two tools to make exploration of phenotyping data available:(1) For data quality control, we developed a coarse-to-rened model called Dynamic Filterto identify abnormalities in plant photosynthesis phenotype data. (2) For inter-functionalphenomics data analysis, we present a new algorithm called PhenoCurve for inter-functional phenomics data analysis.
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