22 Not applicable 336 337 Consent for publication 338 Not applicable 339 340 Availability of data and material 341 The data-sets generated and analysed during the current study are available in the Figshare 342 repository: 343 Binary segmentation non myc DOI https://doi.org/10.6084/m9.figshare.14679642 344 Thresholding and machine learning segmentation - mycorrhized roots DOI 345 https://doi.org/10.6084/m9.figshare.14679729 346 Thresholding and machine learning segmentation – non mycorrhized roots DOI 347 https://doi.org/10.6084/m9.figshare.14679684 348 349 Competing interests 350 The authors declare that they have no competi
Open resource ↗Figshare · 10.6084/m9.figshare.14679642 · pdf-raw-page:23 lines:1-85Unverified paper record
Quantifying Root Colonization in Arbuscular Mycorrhizas by Image Segmentation and Machine Learning
2 Jun 2021 · 10.21203/rs.3.rs-538682/v1
Abstract
Motivation Arbuscular mycorrhizas are the most widespread plant symbioses and involve the majority of crop plants. The beneficial interaction between plant roots and a group of soil fungi (Glomeromycotina) grants the green host a preferential access to soil mineral nutrients and water, supporting plant health, biomass production and resistance to both abiotic and biotic stresses. The nutritional exchanges at the core of this symbiosis take place inside the living root cells, which are diffusely colonized by specialized fungal structures called arbuscules. For this reason, the vast majority of studies investigating arbuscular mycorrhizas and their applications in agriculture require a precise quantification of the intensity of root colonization. To this aim, several manual methods have been used for decades to estimate the extension of intraradical fungal structures, mostly based on optical microscopy observations and individual assessment of fungal abundance in the root tissues. Results: Here we propose a novel semi-automated approach to quantify AM colonization based on digital image analysis and compare two methods based on image thresholding and machine learning. Our results indicate in machine learning a very promising tool for accelerating, simplifying and standardizing this critical type of analysis, with a direct potential interest for applicative and basic research. Contact ivan.sciascia@unito.it; andrea.genre@unito.it
Plant phenotyping relevance
植物根の菌根コロニー形成という植物状態を、画像解析・画像しきい値処理・機械学習で定量する手法の提案と比較検証が中心である。
abstractHere we propose a novel semi-automated approach to quantify AM colonization based on digital image analysis and compare two methods based on image thresholding and machine learning.
abstractOur results indicate in machine learning a very promising tool for accelerating, simplifying and standardizing this critical type of analysis
Code and data availability
The authors deposited the microscopy image datasets used for binary, thresholding, and machine-learning segmentation of mycorrhizal Medicago truncatula roots in three public Figshare repositories, explicitly listed under 'Availability of data and material'. These are paper-specific phenotype image datasets directly支撑本.
Not applicable 339 340 Availability of data and material 341 The data-sets generated and analysed during the current study are available in the Figshare 342 repository: 343 Binary segmentation non myc DOI https://doi.org/10.6084/m9.figshare.14679642 344 Thresholding and machine learning segmentation - mycorrhized roots DOI 345 https://doi.org/10.6084/m9.figshare.14679729 346 Thresholding and machine learning segmentation – non mycorrhized roots DOI 347 https://doi.org/10.6084/m9.figshare.14679684 348 349 Competing interests 350 The authors declare that they have no competing interests. 351 352 Funding 353 Ministero dell’Istruzione, dell’Università e della Ricerca: PhD fellowship to AC Unive
Open resource ↗Figshare · 10.6084/m9.figshare.14679729 · pdf-raw-page:23 lines:1-85available in the Figshare 342 repository: 343 Binary segmentation non myc DOI https://doi.org/10.6084/m9.figshare.14679642 344 Thresholding and machine learning segmentation - mycorrhized roots DOI 345 https://doi.org/10.6084/m9.figshare.14679729 346 Thresholding and machine learning segmentation – non mycorrhized roots DOI 347 https://doi.org/10.6084/m9.figshare.14679684 348 349 Competing interests 350 The authors declare that they have no competing interests. 351 352 Funding 353 Ministero dell’Istruzione, dell’Università e della Ricerca: PhD fellowship to AC Università degli 354 Studi di Torino 355 356 Authors' contributions 357 IS designed the image analysis approach, performed image an
Open resource ↗Figshare · 10.6084/m9.figshare.14679684 · pdf-raw-page:23 lines:1-85This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.