Super Resolution for Root Imaging

High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots i...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Ruiz-Munoz, Jose F, Nimmagadda, Jyothier K, Dowd, Tyler G, Baciak, James E, Zare, Alina
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.05.2020
Subjects
Artificial neural networks
Backscattering
High resolution
Image acquisition
Image quality
Image resolution
Machine learning
Neural networks
Performance evaluation
Plant roots
Signal to noise ratio
Training
Online AccessGet full text

Cover

More Information
Summary:High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is desired for overcoming resolution limitations of sensors, reducing storage space requirements, and boosting the performance of later analysis, such as automatic segmentation. We propose a SR framework for enhancing images of plant roots by using convolutional neural networks (CNNs). We compare three alternatives for training the SR model: i) training with non-plant-root images, ii) training with plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. We demonstrate on a collection of publicly available datasets that the SR models outperform the basic bicubic interpolation even when trained with non-root datasets. Also, our segmentation experiments show that high performance on this task can be achieved independently of the SNR. Therefore, we conclude that the quality of the image enhancement depends on the application.
ISSN:2331-8422