  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.
