Disease Identification: A Review of Vibrational Spectroscopy Applications

Treguier, Sylvain

doi:10.1016/bs.coac.2018.03.005

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…Implementation of sensor technology for phenotyping of PPI allows for measurement of a wide range of signs and symptoms (Table 2 ). Plant metabolites may be detected with spectroscopic methods, and internal plant and pathogen structures can be detected with tomographic methods [ 15 , 18 ]. Fluorescence imaging, possibly with tagged pathogens, and chlorophyll fluorescence imaging are other promising approaches for measuring pathogen growth and photosynthetic parameters.…”

Section: Discussionmentioning

confidence: 99%

Sensor-based phenotyping of above-ground plant-pathogen interactions

et al. 2022

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Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.

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Section: Discussionmentioning

confidence: 99%

Sensor-based phenotyping of above-ground plant-pathogen interactions

et al. 2022

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“…In general, reflectance spectroscopy is used for the sensing of a wide range of stressful conditions. Some of the more recent literature on this topic concerns the assessment of the nitrogen status in crops (Stellacci et al, 2016 ), the macro- and micro-nutrient deficiencies (Galieni et al, 2015 ; Visioli et al, 2016 ; Rustioni, 2017 ; Rustioni et al, 2018 ), the impact of air and soil pollution (Cotrozzi et al, 2018 ; Gholizadeh and Kopacková, 2019 ), the plant disease detection (Ortiz et al, 2019 ; Zhang et al, 2019a ), the salinity effects on crop growth and yield (El-Hendawy S. et al, 2019 ; El-Hendawy et al, 2019a ; Boshkovski et al, 2020 ), the drought-induced changes in plants (Stagnari et al, 2014 ; Maimaitiyiming et al, 2017 ; Sylvain and Cecile, 2018 ; El-Hendawy et al, 2019b ), the specific secondary metabolites accumulation in plant tissue (Couture et al, 2016 ) as well as the plant phenotyping (Garriga et al, 2017 ; Ge et al, 2019 ).…”

Section: Remote Sensing Qualitative Methods For Stress Assessmentmentioning

confidence: 99%

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Galieni¹,

D’Ascenzo

Stagnari

et al. 2021

Front. Plant Sci.

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Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

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“…The problems of poor generalization ability and the difficult convergence of neural networks were solved by SVM. In recent years, good progress has been made in studies on disease diagnosis by using NIRS combined with SVM (Sylvain and Cecile, 2018;Afara et al, 2020). In the SVM method, different kernel functions can generate different SVM algorithms.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

Guo²,

et al. 2020

Front. Chem.

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Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.

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Disease Identification: A Review of Vibrational Spectroscopy Applications

Cited by 6 publications

References 56 publications

Sensor-based phenotyping of above-ground plant-pathogen interactions

Sensor-based phenotyping of above-ground plant-pathogen interactions

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

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