Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images

Sell, Marili; Smith, Abraham George; Burdun, Iuliia; Rohula‐Okunev, Gristin; Kupper, Priit; Ostonen, Ivika

doi:10.1007/s11104-022-05565-4

Cited by 7 publications

(7 citation statements)

References 58 publications

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“…Our proposed root angle measurement method offers speed, accuracy (depending on root visibility) and is freely available as open-source software with a user-accessible GUI. The segmentation component of our pipeline was implemented using RootPainter [34], which previous studies have demonstrated to be a flexible, accessible and efficient way to train segmentation models for a variety of root datasets [33, 3, 11, 12, 2, 21], including for images obtained from rhizobox experiments [1, 4].…”

Section: Discussionmentioning

confidence: 99%

Automated Seminal Root Angle Measurement with Corrective Annotation

Smith,

Malinowska,

Janss

et al. 2024

Preprint

Self Cite

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Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify SNPs significantly associated with angle and length, shedding light on the genetic basis of root architecture.

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Section: Discussionmentioning

confidence: 99%

Automated Seminal Root Angle Measurement with Corrective Annotation

Smith,

Malinowska,

Janss

et al. 2024

Preprint

Self Cite

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“…[ 43 ] note that on the basis of their results, the DL models could be trained to high accuracy with several datasets of varying target objects, backgrounds, and image quality in as few as 2-h annotation time and that annotation, training, and data processing of numerous datasets can be achieved within a single day when using RootPainter. To test this finding, RootPainter was used by another team interested in measuring continuous root growth and investigated humidity and soil nitrogen effects on Norway spruce ( Picea abies L.) sapling roots using 2,288 laboratory-derived images acquired from smartphone (Samsung S6 Edge and Samsung Galaxy S8+) cameras (RGB) [ 100 ]. When the model was used, a small reduction in F 1 score occurred (0.88) when comparing model outputs to manual annotation of fine roots.…”

Section: Root Image Analysis Using Ai and MLmentioning

confidence: 99%

“…Sell et al. [ 100 ] found that increased humidity reduced fine root growth and diminished sequential developmental peaks.…”

Section: Root Image Analysis Using Ai and MLmentioning

confidence: 99%

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Weihs,

Heuschele,

Tang

et al. 2024

Plant Phenomics

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Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the “Second Green Revolution”. To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

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“…Reliance on non-trivial image pre-processing reduces both the accessibility of machine learning algorithms and the transferability of models, limiting processing pipelines to applications on specific datasets. It is, therefore, important that user-friendly machine learning tools, such as RootPainter, do not depend on user-controlled image pre-processing to produce successful models [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

New interactive machine learning tool for marine image analysis

Clark,

Smith,

McKay Fletcher

et al. 2024

R. Soc. Open Sci.

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Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter , to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua , was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter , with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

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Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images

Cited by 7 publications

References 58 publications

Automated Seminal Root Angle Measurement with Corrective Annotation

Automated Seminal Root Angle Measurement with Corrective Annotation

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

New interactive machine learning tool for marine image analysis

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