A Segmentation-Guided Deep Learning Framework for Leaf Counting.

Abstract

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.

Plant phenotyping relevance

植物画像からのセグメンテーションと葉数推定を中核とする深層学習フェノタイピング手法の開発であり、植物形質の自動抽出性能も評価しているため。

Code and data availability

The paper's experiments use the public CVPPP 2017 Leaf Counting Challenge dataset (Arabidopsis and tobacco plant images with segmentation masks and leaf counts), which the authors explicitly link in the data availability statement. No author code or trained models are disclosed.