Photoluminescent Device for Monitoring Fusarium Infection in Seeds

Plant diseases reduce crop yields and can significantly undermine the sustainability of the agricultural sector. Early detection is crucial for effective disease control and management. An analysis of optical methods and devices for diagnosing plant infestations was carried out. (Research purpose) To develop a device for optical photoluminescence diagnostics of Fusarium infestation in cereal seeds. (Materials and methods) Fusarium-infected seeds of Irishka 172 winter wheat and Moskovsky 86 barley were studied. (Results and discussion) A universal device for measuring wheat and barley infestation must be equipped with three radiation sources, operating at wavelengths of 362, 424, and 485 nanometers. The VLMU3510-365-130 LED is most suitable for exciting luminescence at 362 nanometers, the CREELED424 LED is optimal for 424 nanometers, and the XPEBBL-L1 LED is ideal for 485 nanometers. The VEMD5510 photodiode was chosen to detect seed luminescence in the ranges of 390-550 and 510-670 nanometers, while the BPW21R photodiode was selected for the range of 450-600 nanometers. Additionally, a microcontroller, operational amplifier, display, keyboard and other components were also selected. A block diagram was developed that includes incorporating light-optical and electronic units, along with a power supply. During laboratory tests of the LUM VIM-1 device prototype, photosignal responses were observed at 362, 424 and 485 nanometers for wheat and barley seeds with varying infestation levels. The method for determining Fusarium infection includes sample preparation, excitation and detection of photoluminescence, amplification of the photoluminescence signal ratio, and calculation of infection levels using calibration equations. (Conclusions) Based on the energy efficiency criterion, radiation sources and receivers were selected for the device used in the express monitoring of Fusarium infection levels in wheat and barley seeds. During laboratory tests, previously obtained dependencies of seed photoluminescence fluxes on infection levels were confirmed, and the calibration characteristics of the developed device were refined.

1. Izmailov A.Yu., Lobachevsky Ya.P., Khoroshenkov V.K. et al. Optimization of technological process control in crop production Agricultural Machinery and Technologies. 2018. Vol. 12. N3. 4-11 (In Russian). DOI: 10.22314/2073-7599-2018-12-3-4-11.

2. Alt V.V., Isakova S.P. Crop production planning using digital technologies. Agricultural Machinery and Technologies. 2022. Vol. 16. N3. 12-19 (In Russian). DOI: 10.22314/2073-7599-2022-16-3-12-19.

3. Lobachevskiy Ya.P, Dorokhov A.S. Digital technologies and robotic devices in the agriculture. Agricultural Machinery and Technologies. 2021. N15(4). 6-10 (In Russian). DOI: 10.22314/2073-7599-2021-15-4-6-10.

4. Alemu K. Detection of diseases, identification and diversity of viruses: A review. Journal of Biology, Agriculture and Healthcare. 2015. N5(1) 132-141 (In English).

5. Mohd Ali M., Bachik N.A., Muhadi N.A. et al. Non-destructive techniques of detecting plant diseases: A review. Physiological andMolecular Plant Pathology. 2019. 108. 101426 (In English). DOI: 10.1016/j.pmpp.2019.101426.

6. Mahlein A.-K., Alisaac E., Al Masri A. et al. Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring Fusarium head blight of wheat on spikelet scale. Sensors. 2019. N19(10). 2281 (In English). DOI: 10.3390/s19102281.

7. Makmuang S., Nootchanat S., Ekgasit S., Wongravee K. Non-destructive method for discrimination of weedy rice using near infrared spectroscopy and modified Self-Organizing Maps (SOMs). Computers and Electronics in Agriculture. 2021. N191. 106522 (In English). DOI: 10.1016/j.compag.2021.106522.

8. Tsakanikas P, Fengou L.-C., Manthou E. et al. A unified spectra analysis workflow for the assessment of microbial contamination of ready-to-eat green salads: Comparative study and application of non-invasive sensors. Computers and Electronics in Agriculture. 2018. N155. 212-219 (In English). DOI: 10.1016/j.compag.2018.10.025.

9. Johannes A., Picon A., Alvarez-Gila A. et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture. 2017. 138. 200-209 (In English). DOI: 10.1016/j.compag.2017.04.013.

10. Zhang D.-Y., Chen G., Yin X. et al. Integrating spectral and image data to detect Fusarium head blight of wheat. Computers and Electronics in Agriculture. 2020. N175. 105588 (In English). DOI: 10.1016/j.compag.2020.105588.

11. Heim R.H.J., Wright I.J., Chang H.C. et al. Detecting myrtle rust (Austropucciniapsidii) on lemon myrtle trees using spectral signatures and machine learning. Plant Pathology. 2018. N67(5). 1114-1121 (In English). DOI: 10.1111/ppa.12830.

12. 12 Bauriegel E., Herppich W.B. Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on wheat. Agriculture. 2014. N4(1). 32-57 (In English). DOI: 10.3390/AGRICULTURE4010032.

13. Tischler Y.K., Thiessen E., Hartung E. Early optical detection of infection with brown rust in winter wheat by chlorophyll fluorescence excitation spectra. Computers and Electronics in Agriculture. 2018. N146. 77-85 (In English). DOI: 10.3389/fpls.2019.01239.

14. Berzaghi P, Cherney J.H., Casler M.D. Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples. Computers and Electronics in Agriculture. 2021. N182. 106013 (In English). DOI: 10.1016/j.compag.2021.106013.

15. Zhang L., Wang L., Wang J. et al. Leaf Scanner: a portable and low-cost multispectral corn leaf scanning device for precise phenotyping. Computers and Electronics in Agriculture. 2019. N167. 105069 (In English). DOI: 10.1016/j.compag.2019.105069.

16. Song D., Qiao L., Gao D. et al. Development of crop chlorophyll detector based on a type of interference filter optical sensor. Computers and Electronics in Agriculture. 2021. N187. 106260 (In English). DOI: 10.1016/j.compag.2021.106260.

17. Zhou L., Zhang C., Taha M.F. et al. Determination of leaf water content with a portable NIRS system based on deep learning and information fusion analysis. Transactions of the ASABE. 2021. N64(1). 127-135 (In English). DOI: 10.13031/trans.13989.

18. Lebedev D.V., Rozhkov E.A. Sorting by color of wheat seeds infected with fusarium and smut in a multicriteria photoelectronic separator. Electrical technology and equipment in the Agro-Industrial Complex. 2019. N 4(37). 25-29 (In Russian).

19. Bashilov A.M., Efremenkov I.Y., Belyakov M.V. et al. Determination of main spectral and luminescent characteristics of winter wheat seeds infected with pathogenic microflora. Photonics. 2021. N8. 494 (In English). DOI: 10.3390/photonics8110494.

20. Moskovskiy M.N., Belyakov M.V., Dorokhov A.S. et al. Design of device for optical luminescent diagnostic of the seeds infected by Fusarium. Agriculture. 2023. N13(3). 619 (In English). DOI: 10.3390/agriculture13030619.