High-Throughput in situ Root Image Segmentation Based on the Improved DeepLabv3+ Method

Frontiers in Plant Science
Chen ShenLimin Shao

Abstract

The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using in situ images of cotton roots obtained with a micro root window root system monitoring system. Segmentation performance of the proposed method utilizing WinRHIZO Tron MF analysis was assessed using these images. After 80 epochs of training, the final verification set F1-score, recall, and precision were 0.9773, 0.9847, and 0.9702, respectively. The Spearman...Continue Reading

References

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Citations

Sep 17, 2021·Frontiers in Plant Science·Zichen ZhangLiantao Liu

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Methods Mentioned

BETA
X-ray
nuclear magnetic resonance
feature extraction

Software Mentioned

DeepLabV3
ML
Minirhizotrons
AirSurf
TensorFlow
Roots
WinRHIZO Tron MF
Adobe Photoshop CC
Split
Rhizotrons

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