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arXiv Open Access 2020

Plant Stem Segmentation Using Fast Ground Truth Generation

Changye Yang Sriram Baireddy Yuhao Chen Enyu Cai Denise Caldwell +3 lainnya
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Abstrak

Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.

Topik & Kata Kunci

cs.CV

Penulis (8)

C

Changye Yang

S

Sriram Baireddy

Y

Yuhao Chen

E

Enyu Cai

D

Denise Caldwell

V

Valérian Méline

A

Anjali S. Iyer-Pascuzzi

E

Edward J. Delp

Format Sitasi

APA MLA BibTeX
Yang, C., Baireddy, S., Chen, Y., Cai, E., Caldwell, D., Méline, V. et al. (2020). Plant Stem Segmentation Using Fast Ground Truth Generation. https://arxiv.org/abs/2001.08854

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Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓