Development of woody plants phenotyping sys tem with the help of machine vision and spectral analysis algorithms

  • Antonina Yu. Shashko Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Uladzislau Yu. Bandarenka Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Alexander A. Mikhalchenko Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Tatsiana G. Kaliaha Institute of Biophysics and Cell Engineering, National Academy of Sciences of Belarus, 27 Akademičnaja Street, Minsk 220072, Belarus
  • Olga Yu. Safonova Institute of Biophysics and Cell Engineering, National Academy of Sciences of Belarus, 27 Akademičnaja Street, Minsk 220072, Belarus
  • Darya A. Przhevalskaya Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Maryia A. Charnysh Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Dmitrii V. Kolbanov Teaching and Research Centre «Schemislitsa», Belarusian State University, 15-А Žukoŭskaha Street, Ščomyslica 223049, Minsk region, Belarus
  • Vladimir N. Zhabinskii Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 5 Academician V. F. Kupreviča Street, Minsk 220141, Belarus
  • Vladimir A. Khripach Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 5 Academician V. F. Kupreviča Street, Minsk 220141, Belarus
  • Ihor I. Smolich Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Anatoliy I. Sokolik Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Alexander N. Valvachev Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Sergei V. Ablameyko Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Victor V. Krasnoproshin Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus
  • Galina N. Smolikova Saint Petersburg State University, 7/9 Universitetskaya Embankment, Saint Petersburg 199034, Russia
  • Vadim V. Demidchik Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

Abstract

Phenotyping is a modern technology that allows registering and analyzing data about living systems phenotypes in automatic mode. In recent years a high-efficiency higher plants phenotyping with the aim of deep study of the physiological and biochemical bases of their functioning, as well as selection of new high-yielding and stress-resistant varieties has acquired significant development. It is based on adaptation and introduction of modern information approaches such as algorithms of «computer vision» and allows receiving detailed information about plants phenomes at various organization levels. One of the unreached research sections in this field is tree plant cuttings phenotyping, which represents a great practical interest. In this work the system of phenotyping of green stem cuttings of woody plants Thuja occidentalis L. (Smaragd), Juniperus scopulorum Sarg. (Blue Arrow), Picea Abies L., H. Karst. was developed using machine vision algorithms and spectral analysis. A modular phenomics complex was created. It consist of phenomics box, plant cultivation system, lighting and watering systems, as well as system of registration and processing of RGB-images, including software. This complex has been tested in the laboratory and in field conditions. The initial testing of the phenomics complex was carried out. It showed that the data of changes in the pixel distribution of RGB-images by wavelengths can be the basis for estimating the size and physiological state of stem cuttings of woody plants. It was shown that the shift of the spectral curve of the reflected light into the long-wave area under the influence of water deficit allows to non-invasive and statistically reliably register the death of some cells. At the same time, statistically reliable distinctions have not been revealed at testing of influence of phytohormones (auxins and brassinosteroids) on rooting of stem cuttings of coniferous species. The obtained results have a fundamental practical significance and can be used in the research of physiological processes in higher plants, ornamental crop production and forestry.

Author Biographies

Antonina Yu. Shashko, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

master’s degree student at the department of plant cell biology and bioengineering, faculty of biology

Uladzislau Yu. Bandarenka, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

master’s degree student at the department of plant cell biology and bioengineering, faculty of biology

Alexander A. Mikhalchenko, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

probationer of junior researcher at the research laboratory of information technologies and computer graphics, department of management information systems, faculty of applied mathematics and computer science

Tatsiana G. Kaliaha, Institute of Biophysics and Cell Engineering, National Academy of Sciences of Belarus, 27 Akademičnaja Street, Minsk 220072, Belarus

junior researcher at the laboratory of plant cell biophysics and biochemistry

Olga Yu. Safonova, Institute of Biophysics and Cell Engineering, National Academy of Sciences of Belarus, 27 Akademičnaja Street, Minsk 220072, Belarus

junior researcher at the laboratory of plant cell biophysics and biochemistry

Darya A. Przhevalskaya, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

junior researcher at the research laboratory of plant physiology and biotechnology, department of plant cell biology and bioengineering, faculty of biology

Maryia A. Charnysh, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

postgraduate student at the department of plant cell biology and bioengineering, faculty of biology

Dmitrii V. Kolbanov, Teaching and Research Centre «Schemislitsa», Belarusian State University, 15-А Žukoŭskaha Street, Ščomyslica 223049, Minsk region, Belarus

deputy director

Vladimir N. Zhabinskii, Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 5 Academician V. F. Kupreviča Street, Minsk 220141, Belarus

doctor of science (chemistry), docent; chief researcher at the laboratory of steroid chemistry

Vladimir A. Khripach, Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 5 Academician V. F. Kupreviča Street, Minsk 220141, Belarus

academician of the National Academy of Sciences of Belarus, doctor of science (chemistry), full professor; head of the laboratory of steroid chemistry

Ihor I. Smolich, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

PhD (biology), docent; associate professor at the department of plant cell biology and bioengineering, faculty of biology

Anatoliy I. Sokolik, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

PhD (biology), docent; associate professor at the department of plant cell biology and bioengineering, faculty of biology

Alexander N. Valvachev, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

PhD (engineering); associate professor at the department of information management systems, faculty of applied mathematics and computer science

Sergei V. Ablameyko, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

academician of the National Academy of Sciences of Belarus, doctor of science (engineering), full professor; professor at the department of web technologies and computer modeling, faculty of mechanics and mathematics

Victor V. Krasnoproshin, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

doctor of science (engineering), full professor; head of the department of information management systems, faculty of applied mathematics and computer science

Galina N. Smolikova, Saint Petersburg State University, 7/9 Universitetskaya Embankment, Saint Petersburg 199034, Russia

PhD (biology); associate professor at the department of plant physiology and biochemistry, faculty of biology

Vadim V. Demidchik, Belarusian State University, Niezaliežnasci Avenue, 4, 220030, Minsk, Belarus

doctor of science (biology), docent; head of the department of plant cell biology and bioengineering, faculty of biology

References

  1. Furbank RT, Tester M. Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science. 2011;16(12): 635– 644. DOI: 10.1016/j.tplants.2011.09.005.
  2. Coppens F, Wuyts N, Inze D, Dhondt S. Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Current Opinion in Systems Biology. 2017;4:58 – 63. DOI: 10.1016/j.coisb.2017.07.002.
  3. Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M. Plant phenomics, from sensors to knowledge. Current Biology. 2017; 27(15):770 –783. DOI: 10.1016/j.cub.2017.05.055.
  4. Ghanem ME, Marrou H, Sinclair TR. Physiological phenotyping of plants for crop improvement. Trends in Plant Science. 2015; 3:139 –144. DOI: 10.1016/j.tplants.2014.11.006.
  5. Awada L, Phillips PWB, Smyth SJ. The adoption of automated phenotyping by plant breeders. Euphytica. 2018;214. DOI: 10.1007/s10681-018-2226-z.
  6. Araus JL, Cairns JE. Field high-throughput phenotyping: The new crop breeding frontier. Trends in Plant Science. 2014;19(1): 52– 61. DOI: 10.1016/j.tplants.2013.09.008.
  7. Wallace A, Nichol C, Woodhouse I. Recovery of forest canopy parameters by inversion of multispectral LiDAR data. Remote Sensing. 2012;4(2):509–531. DOI: 10.3390/rs4020509.
  8. Gerlai R. Phenomics: fiction or the future? Trends Neurosciences. 2002;25(10):506 –509. DOI: 10.1016/S0166-2236(02)02250-6.
  9. Thorp KR, Gore MA, Andrade-Sanchez P, Carmo-Silva AE, Welch SM, White JW, et al. Proximal hyperspectral sensing and data analysis; approaches for field-based plant phenomics. Computers and Electronics in Agriculture. 2015;118:225–236. DOI: 10.1016/j.compag.2015.09.005.
  10. Hughes N, Askew K, Scotson CP, Williams K, Sauze C, Corke F, et al. Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography. Plant Methods. 2017;13:76. DOI: 10.1186/s13007-017-0229-8.
  11. Jones HG, Serraj R, Loveys BR, Xiong L, Wheaton A, Price AH. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Functional Plant Biology. 2009;36:978–989. DOI: 10.1071/ FP09123.
  12. Garbout A, Munkholm LJ, Hansen SB, Petersen BM, Munk OL, Pajor R. The use of PET/CT scanning technique for 3D visualization and quantification of real-time soil/plant interactions. Plant and Soil. 2012;352:113–127.
  13. Zhang J, Naik HS, Assefa T, Sarkar S, Reddy RVC, Singh A, et al. Computer vision and machine learning for robust phenotyping in genome-wide studies. Scientific Reports. 2017;7:440 – 448. DOI: 10.1038/srep44048.
  14. Ghosal S, Blystone D, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S. An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences. 2018;115(18):4613– 4618. DOI: 10.1073/pnas. 1716999115.
  15. Gutiérrez S, Fernandez-Novales J, Diago MP, Tardaguila J. On-the-go hyperspectral imaging under field conditions and machine learning for the classification of grapevine varieties. Frontiers in Plant Science. 2018;9:1102. DOI: 10.3389/fpls.2018.01102.
  16. Hunt ER, Hively WD, Fujikawa S, Linden D, Daughtry CST, McCarty G. Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing. 2010;2(1):290 –305. DOI: 10.3390/rs2010290.
  17. Pottmann H. Integral invariants for robust geometry processing. Computer Aided Geometric Design. 2009;26(1):37– 60. DOI: 10.1016/j.cagd.2008.01.002.
  18. Sello S, Moscatiello R, La Rocca N, Baldan B, Navazio L. A rapid and efficient method to obtain photosynthetic cell suspension cultures of Arabidopsis thaliana. Frontiers in Plant Science. 2017;8:1444. DOI: 10.3389/fpls.2017.01444.
  19. Straka L, Rittmann BE. Effect of culture density on biomass production and light utilization efficiency of Synechocystis sp. PCC 6803. Biotechnology and Bioengineering. 2018;115(2):507–511. DOI: 10.1002/bit.26479.
  20. Mishra KB, Mishra A, Novotná K, Rapantová B, Hodaňová P, Urban O, et al. Chlorophyll a fluorescence, under half of the adaptive growth-irradiance, for high-throughput sensing of leaf-water deficit in Arabidopsis thaliana accessions. Plant Methods. 2016; 12(46). DOI: 10.1186/s13007-016-0145-3.
  21. Gonzalez-Dugo V, Zarco-Tejada P, Nicolas E, Nortes PA, Alarcon JJ, Intrigliolo DS, et al. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture. 2013; 14(6):660 – 678. DOI: 10.1007/s11119-013-9322-9.
  22. Dungey HS, Dash JP, Pont D, Clinton PW, Watt MS, Telfer EJ. Phenotyping whole forests will help to track genetic performance. Trends in Plant Science. 2018;23(10):854 – 864. DOI: 10.1016/j.tplants.2018.08.005.
  23. Montagnoli A, Terzaghi M, Fulgaro N, Stoew B, Wipenmyr J, Ilver D, et al. Non-destructive phenotypic analysis of early stage tree seedling growth using an automated stereovision imaging method. Frontiers in Plant Science. 2016;7:1644 –1662. DOI: 10.3389/ fpls.2016.01644.
  24. Krabel D, Meyer M, Nyamjav B, Reiche B. Phenotyping trees for traits related to drought stress tolerance – importance and challenge. In: Agrosym 2017. VIII International Scientific Agriculture Symposium; 2017 October 5– 8; Jahorina, Bosnia and Herzegovina. Lukavica: University of East Sarajevo; 2017.
  25. Matyukhin DL, Manina OS, Koroleva NS. Vidy i formy khvoinykh, kul’tiviruemye v Rossii. Moscow: Tovarishchestvo nauchnykh izdanii KMK; 2009. p. 138 –139. Russian.
  26. Leontyak GP. Dendroproektirovanie (Arkhitektura zelenogo stroitel’stva). Tiraspol: Pridnestrovian State University named after Taras Shevchenko; 2006. p. 16 –17. Russian.
  27. Warner JL, Denny JC, Kreda DA, Alterovitz G. Seeing the forest through the trees: Uncovering phenomic complexity through interactive network visualization. Journal of the American Medical Informatics Association. 2015;22(2):324 –329. DOI: 10.1136/amiajnl-2014-002965.
  28. Larichev OI. Sistemy podderzhki prinyatiya reshenii. Sovremennoe sostoyanie i perspektivy ikh razvitiya. Itogi nauki i tekhniki. Seriya: Tekhnicheskaya kibernetika. 1987;21:131–164. Russian.
  29. Martin SL, George T. Applications of hyperspectral image analysis for precision agriculture. In: Conference on Micro- and Nanotechnology Sensors, Systems, and Applications X; 2018 April 15–19; Orlando, United States. Orlando: [publisher unknown]; 2018. DOI: 10.1117/12.2303921.
  30. Zhao H, Xu L, Shi S, Jiang H, Chen D. A high throughput integrated hyperspectral imaging and 3D measurement system. Sensors. 2018;18(4):1068. DOI: 10.3390/s18041068.
  31. Yao X, Si H, Cheng T, Jia M, Chen Q, Tian Y, et al. Hyperspectral estimation of canopy leaf biomass phenotype per ground area using a continuous wavelet analysis in wheat. Frontiers in Plant Science. 2018;9:1360. DOI: 10.3389/fpls.2018.01360.
  32. Fahlgren N, Feldman M, Gehan MA, Wilson MS, Shyu C, Bryant DW, et al. A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecular Plant. 2015;8(10):1520 –1535. DOI: 10.1016/j. molp.2015.06.005.
  33. Phenomics NL. Wageningen UR [Internet]. [Cited 2018 November 29]. Available from: https://www.wageningenur.nl/en/Research-Results/Projects-and-programmes/PhenomicsNL.htm.
Published
2019-03-13
Keywords: phenotyping, plant phenomics, computer vision, stem cuttings of woody plants, coniferous plants, ornamental nursery-gardening, phytohormones
Supporting Agencies The work was financed within the project No. 13 of subprogramme 1 «Innovative biotechnologies – 2020» of the state project «Science-intensive technologies and technics» Republic of Belarus, the project No. 63 of the Industrial scientific and technical programs of the Republic of Belarus «Introduction, greening, eco-safety», the basic financing State programs of scientific research «Nature use and ecology» and «Chemical technologies and materials, natural resources potential» (No. 20161634 and 20161274).
How to Cite
Shashko, A. Y., Bandarenka, U. Y., Mikhalchenko, A. A., Kaliaha, T. G., Safonova, O. Y., Przhevalskaya, D. A., Charnysh, M. A., Kolbanov, D. V., Zhabinskii, V. N., Khripach, V. A., Smolich, I. I., Sokolik, A. I., Valvachev, A. N., Ablameyko, S. V., Krasnoproshin, V. V., Smolikova, G. N., & Demidchik, V. V. (2019). Development of woody plants phenotyping sys tem with the help of machine vision and spectral analysis algorithms. Experimental Biology and Biotechnology, 1, 33-44. https://doi.org/10.33581/2521-1722-2019-1-33-44