MyROOT 2.0: An automatic tool for high throughput and accurate primary root length measurement

González, Alejandro; Sevillano, Xavier; Betegón‐Putze, Isabel; Blasco‐Escámez, David; Ferrer, Marc; Caño‐Delgado, Ana I.

doi:10.1016/j.compag.2019.105125

Cited by 8 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Falk et al (2020) proposed a plant root segmentation method based on a computer vision imaging platform and ML and provided biologically relevant time series data on root growth and development for plant breeding applications. González et al (2020) developed MyROOT 2.0, which uses an automatic and efficient algorithm to detect the root regions of images; this improved the previous version MyROOT, which required manual intervention by the user to define the root area pattern (Betegón-Putze et al, 2019), and also improved the efficiency of batch root detection. Colmer et al (2020) proposed the SeedGerm system, which integrates automatic seed imaging and machine learning-based phenotype analysis, thus providing a wide range of applications for large-scale phenotype analysis and detection of plant seeds.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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 rank correlation between the manually obtained Rhizotrons manual segmentation root length and automated root length was 0.9667 (p < 10 −8), with r 2 = 0.9449. Based on the comparison of our segmentation results with those of traditional manual and U-net segmentation methods, this novel method can more accurately segment root systems in complex soil environments. Thus, using the improved DeepLabv3+ to segment root systems based on micro-root images is an effective method for accurately and quickly segmenting root systems in a homogeneous soil environment and has clear advantages over traditional manual segmentation.

show abstract