Leveraging Multiple Datasets for Deep Leaf Counting

Andrei Dobrescu, Mario Valerio Giuffrida, Sotirios A. Tsaftaris; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2072-2079

The number of leaves of 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. Our method treats leaf counting as a direct regression problem and thus requires as only annotation the total leaf count per plant. We argue that combining different datasets when training the 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).