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Chlorophyll fluorescence imaging is noninvasive technique often used in plant physiology, molecular biology and precision farming. Captured sequences of images record the dynamic of chlorophyll fluorescence emission which contain the information about spatial and time changes of photosynthetic activity of plant. The goal of this Ph.D. thesis is to contribute to the development of chlorophyll fluorescence imaging by application of advanced statistical techniques. Methods of statistical pattern recognition allow to identify images in the captured sequence that are reach for information about observed biotic stress and to find small subsets of fluorescence images suitable for following analysis. I utilized only methods for identification of small sets of images providing high performance with realistic time consumptions.