  The number of leaves a plant has is one of the key traits (phenotypes)
describing its development and growth. Here, we propose an automated, deep
learning based approach for counting leaves in model rosette plants. While
state-of-the-art results on leaf counting with deep learning methods have
recently been reported, they obtain the count as a result of leaf segmentation
and thus require per-leaf (instance) segmentation to train the models (a rather
strong annotation). Instead, our method treats leaf counting as a direct
regression problem and thus only requires as annotation the total leaf count
per plant. We argue that combining different datasets when training a deep
neural network is beneficial and improves the results of the proposed approach.
We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which
contains images of Arabidopsis and tobacco plants. Experimental results show
that the proposed method significantly outperforms the winner of the previous
CVPPP challenge, improving the results by a minimum of ~50% on each of the test
datasets, and can achieve this performance without knowing the experimental
origin of the data (i.e. in the wild setting of the challenge). We also compare
the counting accuracy of our model with that of per leaf segmentation
algorithms, achieving a 20% decrease in mean absolute difference in count
(|DiC|).
