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This paper proposes a novel framework for fluorescence plant videoprocessing. The plant research community is interested in the leaf-levelphotosynthetic analysis within a plant. A prerequisite for such analysis is tosegment all leaves, estimate their structures, and track them over time. Weidentify this as a joint multi-leaf segmentation, alignment, and trackingproblem. First, leaf segmentation and alignment are applied on the last frameof a plant video to find a number of well-aligned leaf candidates. Second, leaftracking is applied on the remaining frames with leaf candidate transformationfrom the previous frame. We form two optimization problems with shared terms intheir objective functions for leaf alignment and tracking respectively. Aquantitative evaluation framework is formulated to evaluate the performance ofour algorithm with four metrics. Two models are learned to predict thealignment accuracy and detect tracking failure respectively in order to provideguidance for subsequent plant biology analysis. The limitation of our algorithmis also studied. Experimental results show the effectiveness, efficiency, androbustness of the proposed method.

Joint Multi-Leaf Segmentation, Alignment and Tracking from Fluorescence Plant Videos