Determination of Main Spectral and Luminescent Characteristics of Winter Wheat Seeds Infected with Pathogenic Microflora

In connection with the constant growth of demand for high-quality food products, there is a need to develop effective methods for storing agricultural products, and the registration and predicting infection in the early stages. The studying of the physical properties of infected plants and seeds has fundamental importance for determining crop losses, conducting a survey of diseases, and assessing the effectiveness of their control (assessment of the resistance of crops and varieties, the effect of fungicides, etc.). Presently, photoluminescent methods for diagnosing seeds in the ultraviolet and visible ranges have not been studied. For research, seeds of winter wheat were selected, and were infected with one of the most common and dangerous diseases for plants—fusarium. The research of luminescence was carried out based on a hardware–software complex consisting of a multifunctional spectrofluorometer “Fluorat-02-Panorama”, a computer with software “Panorama Pro” installed, and an external camera for the samples under study. Spectra were obtained with a diagnostic range of winter wheat seeds of 220–400 nm. Based on the results obtained for winter wheat seeds, it is possible to further develop a method for determining the degree of fusarium infection.

[1]  S. Vovk,et al.  Sum Decomposition Method for Gaussian Functions Comprising an Experimental Photoluminescence Spectrum , 2021, Journal of Applied Spectroscopy.

[2]  K. Gaikwad,et al.  Quantitative detection of pathogen load of Fusarium oxysporum f.sp. ciceris infected wilt resistant and susceptible genotypes of chickpea using intergenic spacer region-based marker , 2021 .

[3]  E. Sokolova,et al.  Photoluminescent Control Ripeness of the Seeds of Plants , 2021, E3S Web of Conferences.

[4]  R. Sarimov,et al.  The Use of Fluorescence Spectra for the Detection of Scab and Rot in Fruit and Vegetable Crops , 2021, Frontiers in Physics.

[5]  M. G. Kulikova,et al.  Control of bulk products’ humidity and grinding size by the photoluminescent method photoluminescent quality control , 2020 .

[6]  M. Bilal,et al.  Laser-induced fluorescence spectroscopy for early disease detection in grapefruit plants , 2020, Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology.

[7]  Emerson M. Del Ponte,et al.  From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy , 2020 .

[8]  M. Reynolds,et al.  Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations , 2019, Frontiers of Agricultural Science and Engineering.

[9]  Kristian Kersting,et al.  Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range , 2019, Remote. Sens..

[10]  Jayme Garcia Arnal Barbedo,et al.  Detection of sprout damage in wheat kernels using NIR hyperspectral imaging , 2018, Biosystems Engineering.

[11]  Anne-Katrin Mahlein,et al.  Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art. , 2018, Annual review of phytopathology.

[12]  A. Ibrahim.  Monitoring Some Quality Attributes of Different Wheat Varieties by Infrared Technology , 2018 .

[13]  Pei-Shih Liang,et al.  Nondestructive detection of zebra chip disease in potatoes using near-infrared spectroscopy , 2018 .

[14]  Anne-Katrin Mahlein,et al.  Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective , 2018 .

[15]  Rebecca L. Whetton,et al.  Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study , 2018 .

[16]  Seyed Ahmad Mireei,et al.  Early detection of freezing damage in sweet lemons using Vis/SWNIR spectroscopy , 2017 .

[17]  Moon S. Kim,et al.  Detection of cucumber green mottle mosaic virus-infected watermelon seeds using a near-infrared (NIR) hyperspectral imaging system: Application to seeds of the “Sambok Honey” cultivar , 2016 .

[18]  Jayme Garcia Arnal Barbedo,et al.  A review on the main challenges in automatic plant disease identification based on visible range images , 2016 .

[19]  Christian Bauckhage,et al.  Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images , 2015, PloS one.

[20]  Z. Xin,et al.  Development of a real-time fluorescence loop-mediated isothermal amplification assay for rapid and quantitative detection of Fusarium mangiferae associated with mango malformation , 2014 .

[21]  M. Gasparoto,et al.  Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy. , 2008, Applied optics.

[22]  Renfu Lu,et al.  Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality , 2007 .

[23]  A. Dobson,et al.  PCR-based detection and quantification of mycotoxigenic fungi , 2002 .