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 , e. g., the venation network of leaves. However, the acquisition of high-resolution (HR) imagery of roots in situ remains a challenge. We apply super-resolution (SR)  convolutional neural networks (CNNs) to boost the resolution capability of a backscatter X-ray system designed to image buried roots. 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. In addition, we show examples of backscatter X-ray images upscaled by using the SR model. The current technology for non-intrusive root imaging acquires noisy and LR images. In this study, we show that this issue can be tackled by the incorporation of a deep-learning based SR model in the image formation process .
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 .
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, e. g., the venation network of leaves.
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roots in situ remains a challenge. We apply
plant roots is more challenging than above-ground data collection. Thus, an effective
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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
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to boost the resolution capability of a backscatter X-ray system designed to image buried roots. To overcome limited available backscatter X-ray data for training, we
. We
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the SR model
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training with
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training with
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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
. We demonstrate on a collection of publicly available datasets that the
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pre-processing boosts the performance of a machine learning system trained to separate plant roots from their background. In addition, we show examples of backscatter X-ray images upscaled by using the SR model. The current technology for non-intrusive root imaging acquires noisy and LR images. In this study, we show that this issue can be tackled by the incorporation of a deep-learning based SR model in the image formation process
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