Before revision:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training  : i)  non-plant-root images, ii)  plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network). We evaluate SR performance using signal to noise ratio (SNR) and intersection over union (IoU) metrics when segmenting the SR images. In our experiments, we observe that the studied SR models improve the quality of the low-resolution images (LR) of plant roots of an unseen dataset in terms of SNR. Likewise, we demonstrate that SR pre-processing boosts the performance of a machine learning system trained to separate plant roots from their background.
After revision:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training : i) non-plant-root images, ii) 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 pre-processing boosts the performance of a machine learning system trained to separate plant roots from their background.