High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Jangra, Sumit; Chaudhary, Vrantika; Yadav, Renu; Yadav, N. R.

doi:10.1007/s43657-020-00007-6

Cited by 51 publications

(36 citation statements)

References 189 publications

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“…Regarding the utilization of RWSI 1 , RWSI 2 , or I G as stress indices, it should be noted that E or stomatal conductance may fluctuate with radiation and other non‐stress factors. A combination of thermal imaging with other techniques, such as fluorescence or hyperspectral imaging, can give more comprehensive insights into the impact of stress on plant function and growth (Gerhards et al, 2019; Jangra et al, 2021). The artificial reference surfaces used were substantial and easy to obtain and utilize.…”

Section: Discussionmentioning

confidence: 99%

“…Arabidopsis thaliana (L.) is a model plant (Meyerowitz, 2001) highly employed in high-throughput phenotyping platforms (Jangra et al, 2021) to advance our understanding of genotype-to-phenotype transitions. Thermal imaging has been employed and various stress indices tested against different physiological parameters across a range of conditions and plant species in the form of proximal or remote sensing (reviewed in Pineda et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, more measurements within a day (e.g., Agam et al, 2013) insights into the impact of stress on plant function and growth (Gerhards et al, 2019;Jangra et al, 2021). The artificial reference surfaces used were substantial and easy to obtain and utilize.…”

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confidence: 99%

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Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration

Savvides

Velez‐Ramirez

Fotopoulos

2022

Physiologia Plantarum

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Water stress may greatly limit plant functionality and growth. Stomatal closure and consequently reduced transpiration are considered as early and sensitive plant responses to drought and salinity stress. An important consequence of stomatal closure under water stress is the rise of leaf temperature (Tleaf), yet Tleaf is not only fluctuating with stomatal closure. It is regulated by several plant parameters and environmental factors. Thermal imaging and different stress indices, incorporating actual leaf/crop temperature and reference temperatures, were developed in previous studies toward normalizing for effects unassociated to water stress on Tleaf, aiming at a more efficient water stress assessment. The concept of stress indices has not been extensively studied on the model plant Arabidopsis thaliana. Therefore, the aim of this study was to examine the different indices employed in previous studies in assessing rosette transpiration rate (E) in Arabidopsis plants grown under two different light environments and subjected to salinity. After salinity imposition, E was gravimetrically quantified, and thermal imaging was employed to quantify rosette (Trosette) and artificial reference temperature (Twet, Tdry). Trosette and several water stress indices were tested for their relation to E. Among the microclimatic growth conditions tested, RWSI1 ([Trosette − Twet]/[Tdry − Twet]) and RWSI2 ([Tdry − Trosette]/[Tdry − Twet]) were well linearly‐related to E, irrespective of the light environment, while the sole use of either Twet or Tdry in different combinations with Trosette returned less accurate results. This study provides evidence that selected combinations of Trosette, Tdry, and Twet can be utilized to assess E under water stress irrespective of the light environment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration

Savvides

Velez‐Ramirez

Fotopoulos

2022

Physiologia Plantarum

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“…With the rise of computer vision, high-throughput phenotypic technology has been used more in crop disease and pest identification (Jangra et al, 2021). The traditional method was usually applied to extract the color and texture, shape features of disease spots in leaves, and then distinguish the disease spots from normal leaves by adjusting the threshold (Tao et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

Shi

et al. 2022

Front. Plant Sci.

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Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.

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“…The interactions between cancer cells, stromal cells, and recruited immune cells promote the invasion and metastasis of a variety of cancers, as well as cell proliferation, anti-apoptosis signals, and evasion of immune surveillance. This significantly impacts the treatment and prognosis of cancer patients (18,19). The TME is mainly composed of resident stromal cells and recruited immune cells (20), which affect tumor blood vessel growth and tumor proliferation, respectively.…”

Section: Introductionmentioning

confidence: 99%

Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment

et al. 2021

Front. Oncol.

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The tumor microenvironment (TME) contributes to the initiation and progression of many neoplasms. However, the impact of low-grade glioma (LGG) purity on carcinogenesis remains to be elucidated. We selected 509 LGG patients with available genomic and clinical information from the TCGA database. The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. Hub genes involved in LGG purity were then identified and functionally annotated using bioinformatics analyses. Prognostic implications were validated in 100 patients from an Asian real-world cohort. Elevated tumor purity burden, immune scores, and stromal scores were significantly associated with poor outcomes and increased grade in LGG patients from the TCGA cohort. In addition, CD3E was selected with the most significant prognostic value (Hazard Ratio=1.552, P<0.001). Differentially expressed genes screened according to CD3E expression were mainly involved in stromal related activities. Additionally, significantly increased CD3E expression was found in 100 LGG samples from the validation cohort compared with adjacent normal brain tissues. High CD3E expression could serve as an independent prognostic indicator for survival of LGG patients and promotes malignant cellular biological behaviors of LGG. In conclusion, tumor purity has a considerable impact on the clinical, genomic, and biological status of LGG. CD3E, the gene for novel membrane immune biomarker deeply affecting tumor purity, may help to evaluate the prognosis and develop individual immunotherapy strategies for LGG patients. Evaluating the ratio of differential tumor purity and CD3E expression levels may provide novel insights into the complex structure of the LGG microenvironment and targeted drug development.

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High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Cited by 51 publications

References 189 publications

Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration

Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment

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