The use of plant models in deep learning: an application to leaf counting in rosette plants

Abstract

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

Plant phenotyping relevance

合成植物画像によるデータセット拡張と深層学習を用いたロゼット植物の葉数推定手法が中心であり、植物表現型取得・推定の技術開発に該当する。

Code and data availability

The paper publicly releases its generated/analysed leaf-counting datasets (including synthetic rosette images and real Ara2012/Ara2013-Canon subsets) via a figshare deposit with an explicit availability statement, and it uses the public IPPN PRL dataset as its real-plant phenotyping input. The L-system model code is in