Leaf Counting with Deep Convolutional and Deconvolutional Networks

Abstract

In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets.

Plant phenotyping relevance

ロゼット葉の画像から葉数を推定する深層学習手法を提案し、セグメンテーションと葉数カウントをデータセットで評価しており、植物表現型取得手法が中心である。

Code and data availability

The paper's authors explicitly state their leaf counting/segmentation code is publicly available on GitHub, and the CVPPP2017 Leaf Counting Challenge dataset used for all experiments is publicly hosted at plant-phenotyping.org.