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In this paper, we investigate the problem of counting rosette leaves from anRGB image, an important task in plant phenotyping. We propose a data-drivenapproach for this task generalized over different plant species and imagingsetups. To accomplish this task, we use state-of-the-art deep learningarchitectures: a deconvolutional network for initial segmentation and aconvolutional network for leaf counting. Evaluation is performed on the leafcounting challenge dataset at CVPPP-2017. Despite the small number of trainingsamples in this dataset, as compared to typical deep learning image sets, weobtain satisfactory performance on segmenting leaves from the background as awhole and counting the number of leaves using simple data augmentationstrategies. Comparative analysis is provided against methods evaluated on theprevious competition datasets. Our framework achieves mean and standarddeviation of absolute count difference of 1.62 and 2.30 averaged over all fivetest datasets.

Leaf Counting with Deep Convolutional and Deconvolutional Networks