Plant Stem Segmentation Using Fast Ground Truth Generation

Yang, Clifford; Baireddy, Sriram; Chen, Yuhao; Cai, Enyu; Caldwell, Denise; Méline, Valérian; Iyer‐Pascuzzi, Anjali S.; Delp, Edward J.

doi:10.1109/ssiai49293.2020.9094605

Cited by 4 publications

(11 citation statements)

References 12 publications

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“…Stem Segmentation: For stem segmentation, we use a deep neural network-based solution similar to what was used by Yang et al [32]. A set of stem masks are manually labeled and used to train the stem segmentation networks.…”

Section: Initial Processingmentioning

confidence: 99%

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Image-Based Plant Wilting Estimation

Yang¹,

Baireddy²,

Cai³

et al. 2021

Preprint

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Many plants become limp or droop through heat, loss of water, or disease. This is also known as wilting. In this paper we examine plant wilting caused by bacterial infection. In particular we want to design a metric for wilting based on images acquired of the plant. A quantifiable wilting metric will be useful in studying bacterial wilt and identifying resistance genes. Since there is no standard way to estimate wilting, it is common to use ad hoc visual scores. This is very subjective and requires expert knowledge of the plants and the disease mechanism. Our solution consists of using various wilting metrics acquired from RGB images of the plants. We also designed several experiments to demonstrate that our metrics are effective at estimating wilting in plants.

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Section: Initial Processingmentioning

confidence: 99%

“…For the plants where Mask R-CNN fails to detect stems, we use the U-Net to detect the stem. Mask R-CNN produces better quality [32] masks compared to U-Net but it sometimes fails to detect the stem and outputs nothing. Since U-Net always produces a mask, it is used as a complementary stem segmentation network.…”

Section: Initial Processingmentioning

confidence: 99%

Image-Based Plant Wilting Estimation

Yang¹,

Baireddy²,

Cai³

et al. 2021

Preprint

Self Cite

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“…From the plant images, ten traits were acquired for QTL analysis. A detailed description of the acquisition process can be found in 47,48 . All traits were extracted based on plant color and shape.…”

Section: Phenotyping Pipeline and Image Analysismentioning

confidence: 99%

“…To train the neural networks, stem segmentation ground truth data were generated using Adobe Photoshop and LabelMe (a Python-based annotation tool) to mark the location of the image pixels belonging to the stem of the plant. Further details about this procedure are described in 47,48 .…”

Section: Phenotyping Pipeline and Image Analysismentioning

confidence: 99%

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Image-based assessment of plant disease progression identifies new genetic loci for resistance

Meline.

Caldwell

et al. 2021

Preprint

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A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best means of disease control is plant resistance, but the identification of genes that promote resistance has been limited by the subjective quantification of disease, which is typically scored by the human eye. We hypothesized that image-based quantification of disease phenotypes would enable the identification of new disease resistance loci. We tested this using the interaction between tomato and Ralstonia solanacearum , a soilborne pathogen that causes bacterial wilt disease. We acquired over 40,000 time-series images of disease progression in a tomato recombinant inbred line population, and developed an image analysis pipeline providing a suite of ten traits to quantify wilt disease based on plant shape and size. Quantitative trait loci (QTL) analyses using image-based phenotyping identified QTL that were both unique and shared compared with those identified by human assessment of wilting. When shared loci were identified, image-based phenotyping could detect some QTL several days earlier than human assessment. Thus, expanding the phenotypic space of disease with image-based phenotyping allowed both earlier detection and identified new genetic components of resistance.

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Image‐based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato

et al. 2023

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SUMMARY A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image‐based, non‐destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time‐series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image‐based phenotyping for single and multi‐traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image‐based, non‐destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.

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Plant Stem Segmentation Using Fast Ground Truth Generation

Cited by 4 publications

References 12 publications

Image-Based Plant Wilting Estimation

Image-Based Plant Wilting Estimation

Image-based assessment of plant disease progression identifies new genetic loci for resistance

Image‐based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato

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