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Research Article | Open Access

Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots

Qiushi Yu1,†, Jingqi Wang1,†, Hui Tang1, Jiaxi Zhang1, Wenjie Zhang1, Liantao Liu2( ), Nan Wang1( )
College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000, Baoding, China
College of Agronomy, Hebei Agricultural University, 071000, Baoding, China

†These author contributed equally to this work.

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Abstract

The root is an important organ for crops to absorb water and nutrients. Complete and accurate acquisition of root phenotype information is important in root phenomics research. The in situ root research method can obtain root images without destroying the roots. In the image, some of the roots are vulnerable to soil shading, which severely fractures the root system and diminishes its structural integrity. The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored. Therefore, based on the in situ root image of cotton, this study proposes a root segmentation and reconstruction strategy, improves the UNet model, and achieves precise segmentation. It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two. The research results show that the improved UNet model has an accuracy of 99.2%, mIOU of 87.03%, and F1 of 92.63%. The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%. This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network. It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems, also realizes the restoration of the integrity of the in situ root image, and provides a new method for in situ root phenotype study.

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Plant Phenomics
Volume 5,
2023
Article number: 0066
DOI: 10.34133/plantphenomics.0066
Cite this article:
Yu Q, Wang J, Tang H, et al. Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots. Plant Phenomics, 2023, 5: 0066. https://doi.org/10.34133/plantphenomics.0066

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Received: 13 March 2023
Accepted: 14 June 2023
Published: 06 July 2023
© 2023 Qiushi Yu et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
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