Phenotypic Characteristics of the Mycelium of Using Image Recognition Technology

Overview

Journal Front Bioeng Biotechnol

Date 2024 Jul 2

PMID 38952667

Authors

Xingyi Wang

Ya Xu

Xuan Wei

Affiliations

Soon will be listed here.

Abstract

Phenotypic analysis has significant potential for aiding breeding efforts. However, there is a notable lack of studies utilizing phenotypic analysis in the field of edible fungi. is a lucrative edible fungus with significant market demand and substantial industrial output, and early-stage phenotypic analysis of is imperative during its breeding process. This study utilizes image recognition technology to investigate the phenotypic features of the mycelium of . We aim to establish the relations between these phenotypic characteristics and mycelial quality. Four groups of mycelia, namely, the non-degraded and degraded mycelium and the 5th and 14th subcultures, are used as image sources. Two categories of phenotypic metrics, outline and texture, are quantitatively calculated and analyzed. In the outline features of the mycelium, five indexes, namely, mycelial perimeter, radius, area, growth rate, and change speed, are proposed to demonstrate mycelial growth. In the texture features of the mycelium, five indexes, namely, mycelial coverage, roundness, groove depth, density, and density change, are studied to analyze the phenotypic characteristics of the mycelium. Moreover, we also compared the cellulase and laccase activities of the mycelium and found that cellulase level was consistent with the phenotypic indices of the mycelium, which further verified the accuracy of digital image processing technology in analyzing the phenotypic characteristics of the mycelium. The results indicate that there are significant differences in these 10 phenotypic characteristic indices ( ), elucidating a close relationship between phenotypic characteristics and mycelial quality. This conclusion facilitates rapid and accurate strain selection in the early breeding stage of .

References

Cardini A, Pellegrino E, Del Dottore E, Gamper H, Mazzolai B, Ercoli L . HyLength: a semi-automated digital image analysis tool for measuring the length of roots and fungal hyphae of dense mycelia. Mycorrhiza. 2020; 30(2-3):229-242. DOI: 10.1007/s00572-020-00956-w. View

Wei X, Liu S, Xie C, Fang W, Deng C, Wen Z . Nondestructive detection of strain degradation based on micro-hyperspectral imaging and machine learning. Front Plant Sci. 2023; 14:1260625. PMC: 10731295. DOI: 10.3389/fpls.2023.1260625. View

Li B, Chen L, Sun W, Wu D, Wang M, Yu Y . Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton. Plant Biotechnol J. 2020; 18(12):2533-2544. PMC: 7680548. DOI: 10.1111/pbi.13431. View

Yoo S, Lee H, Markkandan K, Moon S, Ahn Y, Ji S . Comparative transcriptome analysis identified candidate genes involved in mycelium browning in Lentinula edodes. BMC Genomics. 2019; 20(1):121. PMC: 6368761. DOI: 10.1186/s12864-019-5509-4. View

Zhai Z, Jin Z, Zhang R . Information integration of force sensing and machine vision for in-shell shrivelled walnut detection based on the golden-section search optimal discrimination threshold. J Sci Food Agric. 2019; 99(8):3941-3949. DOI: 10.1002/jsfa.9618. View

Basak A, Mirzaei M, Strzalka K, Yamada K . Texture feature extraction from microscope images enables a robust estimation of ER body phenotype in Arabidopsis. Plant Methods. 2021; 17(1):109. PMC: 8549183. DOI: 10.1186/s13007-021-00810-w. View

Danner C, Mach R, Mach-Aigner A . The phenomenon of strain degeneration in biotechnologically relevant fungi. Appl Microbiol Biotechnol. 2023; 107(15):4745-4758. PMC: 10345034. DOI: 10.1007/s00253-023-12615-z. View

Bae E, Bai N, Aroonnual A, Bhunia A, Hirleman E . Label-free identification of bacterial microcolonies via elastic scattering. Biotechnol Bioeng. 2011; 108(3):637-44. DOI: 10.1002/bit.22980. View

Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S . Crop Phenomics: Current Status and Perspectives. Front Plant Sci. 2019; 10:714. PMC: 6557228. DOI: 10.3389/fpls.2019.00714. View

10.

Li H, Feng H, Guo C, Yang S, Huang W, Xiong X . High-throughput phenotyping accelerates the dissection of the dynamic genetic architecture of plant growth and yield improvement in rapeseed. Plant Biotechnol J. 2020; 18(11):2345-2353. PMC: 7589443. DOI: 10.1111/pbi.13396. View

11.

Hardy N, Moreaud M, Guillaume D, Augier F, Nienow A, Beal C . Advanced digital image analysis method dedicated to the characterization of the morphology of filamentous fungus. J Microsc. 2017; 266(2):126-140. DOI: 10.1111/jmi.12523. View

12.

Lehmann A, Zheng W, Soutschek K, Roy J, Yurkov A, Rillig M . Tradeoffs in hyphal traits determine mycelium architecture in saprobic fungi. Sci Rep. 2019; 9(1):14152. PMC: 6775140. DOI: 10.1038/s41598-019-50565-7. View

13.

Furbank R, Tester M . Phenomics--technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011; 16(12):635-44. DOI: 10.1016/j.tplants.2011.09.005. View

14.

Yang C, Ma X, Guan H, Fan B . Rapid detection method of Pleurotus eryngii mycelium based on near infrared spectral characteristics. Spectrochim Acta A Mol Biomol Spectrosc. 2022; 271:120919. DOI: 10.1016/j.saa.2022.120919. View

15.

James J, Santhanam J, Zakaria L, Mamat Rusli N, Abu Bakar M, Suetrong S . Morphology, Phenotype, and Molecular Identification of Clinical and Environmental Species Complex Isolates from Malaysia. J Fungi (Basel). 2022; 8(8). PMC: 9409803. DOI: 10.3390/jof8080845. View

16.

Alkhudaydi T, Reynolds D, Griffiths S, Zhou J, De La Iglesia B . An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat. Plant Phenomics. 2020; 2019:7368761. PMC: 7706304. DOI: 10.34133/2019/7368761. View

17.

Guo Z, Yang W, Chang Y, Ma X, Tu H, Xiong F . Genome-Wide Association Studies of Image Traits Reveal Genetic Architecture of Drought Resistance in Rice. Mol Plant. 2018; 11(6):789-805. DOI: 10.1016/j.molp.2018.03.018. View

18.

Le C, Mendes R, Kruijt M, Raaijmakers J . Genetic and Phenotypic Diversity of Sclerotium rolfsii in Groundnut Fields in Central Vietnam. Plant Dis. 2019; 96(3):389-397. DOI: 10.1094/PDIS-06-11-0468. View

19.

Shakoor N, Lee S, Mockler T . High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr Opin Plant Biol. 2017; 38:184-192. DOI: 10.1016/j.pbi.2017.05.006. View

20.

Yang N, Qian Y, El-Mesery H, Zhang R, Wang A, Tang J . Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method. J Sci Food Agric. 2019; 99(14):6589-6600. DOI: 10.1002/jsfa.9943. View