Photosynthetica 2022, 60(1):79-87 | DOI: 10.32615/ps.2021.066

An open Internet of Things (IoT)-based framework for feedback controlof photosynthetic activities

S. YUAN1, H. TANG2, L.J. FU1, J.L. TAN3, G. GOVINDJEE4, Y. GUO1, 3
1 Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of IoT, Jiangnan University, 214122 Wuxi, China
2 Lushixin Sci. & Tec. (Wuxi) Co. Ltd., 214124 Wuxi, China
3 Department of Bioengineering, University of Missouri, Columbia, MO 65211, USA
4 Center of Biophysics & Quantitative Biology, Department of Biochemistry and Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Active control of photosynthetic activities is important in plant physiological study. Although models of plant photosynthesis have been built at different scales, they have not been fully examined for their application in plant growth control. However, we do not have an infrastructure to support such experiments since current plant growth chambers usually use fixed control protocols. In our current paper, an open IoT-based framework is proposed. This framework allows a plant scientist or agricultural engineer, through an application programming interface (API), in a desirable programming language, (1) to gather environmental data and plant physiological responses; (2) to program and execute control algorithms based on their models, and then (3) to implement real-time commands to control environmental factors. A plant growth chamber was developed to demonstrate the concept of the proposed open framework.

Additional key words: chlorophyll fluorescence; greenhouse; Internet of things; open framework; photosynthesis model; plant growth chamber.

Received: August 16, 2021; Revised: November 7, 2021; Accepted: December 7, 2021; Prepublished online: January 20, 2022; Published: March 18, 2022  Show citation

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YUAN, S., TANG, H., FU, L.J., TAN, J.L., GOVINDJEE, G., & GUO, Y. (2022). An open Internet of Things (IoT)-based framework for feedback controlof photosynthetic activities. Photosynthetica60(SPECIAL ISSUE 2022), 79-87. doi: 10.32615/ps.2021.066
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References

  1. Astill J., Dara R.A., Fraser E.D.G. et al.: Smart poultry management: Smart sensors, big data, and the internet of things. - Comput. Electron. Agr. 170: 105291, 2020. Go to original source...
  2. Baker N.R.: Chlorophyll fluorescence: a probe of photosynthesis in vivo. - Annu. Rev. Plant Biol. 59: 89-113, 2008. Go to original source...
  3. Balaji G.N., Nandhini V., Mithra S. et al.: Iot based smart crop monitoring in farm land. - Imp. J. Interdiscip. Res. 4: 88-92, 2018.
  4. Barber J., Andersson B.: Too much of a good thing: light can be bad for photosynthesis. - Trends Biochem. Sci. 17: 61-66, 1992. Go to original source...
  5. Bermudez I., Traverso S., Mellia M., Munafò M.: Exploring the cloud from passive measurements: The Amazon AWS case. - In: 2013 Proceedings IEEE INFOCOM. Pp. 230-234. IEEE, Turin 2013. Go to original source...
  6. Bulthuis D.A.: Effects of temperature on photosynthesis and growth of seagrasses. - Aquat. Bot. 27: 27-40, 1987. Go to original source...
  7. Campani G., Ribeiro M.P.A., Zangirolami T.C., Lima F.V.: A hierarchical state estimation and control framework for monitoring and dissolved oxygen regulation in bioprocesses. -Bioprocess Biosyst. Eng. 42: 1467-1481, 2019. Go to original source...
  8. Chang T.G., Zhu X.G.: Source-sink interaction: a century old concept under the light of modern molecular systems biology. - J. Exp. Bot. 68: 4417-4431, 2017. Go to original source...
  9. Christensen A.J., Srinivasan V., Hart J.C. et al.: Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security. - Nutr. Rev. 76: 332-347, 2018. Go to original source...
  10. Dietz K.-J., Schreiber U., Heber U.: The relationship between the redox state of QA and photosynthesis in leaves at various carbon-dioxide, oxygen and light regimes. - Planta 166: 219-226, 1985. Go to original source...
  11. Dinar A., Tieu A., Huynh H.: Water scarcity impacts on global food production. - Glob. Food Secur. 23: 212-226, 2019. Go to original source...
  12. Feng S., Fu L., Xia Q. et al.: Modelling and simulation of photosystem II chlorophyll fluorescence transition from dark-adapted state to light-adapted state. - IET Syst. Biol. 12: 289-293, 2018. Go to original source...
  13. Fernandez-Jaramillo A.A., Duarte-Galvan C., Contreras-Medina L.M. et al.: Instrumentation in developing chlorophyll fluorescence biosensing: A review. - Sensors-Basel 12: 11853-11869, 2012. Go to original source...
  14. Fu L., Govindjee G., Tan J., Guo Y.: Development of a minimized model structure and a feedback control framework for regulating photosynthetic activities. - Photosynth. Res. 146: 213-225, 2020. Go to original source...
  15. Genty B., Briantais J.-M., Baker N.R.: The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. - BBA-Gen. Subjects 990: 87-92, 1989. Go to original source...
  16. Govindjee G., Amesz J., Fork D.C.: Light Emission by Plants and Bacteria. Pp. 638. Academic Press, Orlando 1986.
  17. Guarini J.-M., Moritz C.: Modelling the dynamics of the electron transport rate measured by PAM fluorimetry during rapid light curve experiments. - Photosynthetica 47: 206-214, 2009. Go to original source...
  18. Guo Y., Tan J.: Modeling and simulation of the initial phases of chlorophyll fluorescence from Photosystem II. - BioSystems 103: 152-157, 2011. Go to original source...
  19. Hemming S., de Zwart F., Elings A. et al.: Remote control of greenhouse vegetable production with artificial intelligence -greenhouse climate, irrigation, and crop production. - Sensors-Basel 19: 1807, 2019. Go to original source...
  20. Iersel M.W., Mattos E., Weaver G. et al.: Using chlorophyll fluorescence to control lighting in controlled environment agriculture. - Acta Hortic. 1134: 427-434, 2016a. Go to original source...
  21. Iersel M.W., Weaver G., Martin M.T. et al.: A chlorophyll fluorescence-based biofeedback system to control photosynthetic lighting in controlled environment agriculture. - J. Am. Soc. Hortic. Sci. 141: 169-176, 2016b. Go to original source...
  22. Juhasova B., Juhas M., Halenar I.: TCP/IP protocol utilisation in process of dynamic control of robotic cell according industry 4.0 concept. - In: 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics. Pp. 000217-000222. SAMI, 2017.
  23. Kannan K., Wang Y., Lang M. et al.: Combining gene network, metabolic and leaf-level models show means to future-proof soybean photosynthesis under rising CO2. - in silico Plants 1: diz008, 2019. Go to original source...
  24. Kennedy D., Norman C.: What don't we know? - Science 309: 75, 2005. Go to original source...
  25. Kim J.Y.: Roadmap to high throughput phenotyping for plant breeding. - J. Biosyst. Eng. 45: 43-55, 2020. Go to original source...
  26. Kramer P.J.: Carbon dioxide concentration, photosynthesis, and dry matter production. - BioScience 31: 29-33, 1981. Go to original source...
  27. Light R.A.: Mosquitto: server and client implementation of the MQTT protocol. - J. Open Source Softw. 2: 265, 2017. Go to original source...
  28. Long S.P., Marshall-Colon A., Zhu X.G.: Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. - Cell 161: 56-66, 2015. Go to original source...
  29. Mahmoud R., Yousuf T., Aloul F., Zualkernan I.: Internet of things (IoT) security: Current status, challenges and prospective measures. - In: 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST). Pp. 336-341. IEEE, London 2015. Go to original source...
  30. Malik A.W., Rahman A.U., Qayyum T., Ravana S.D.: Leveraging fog computing for sustainable smart farming using distributed simulation. - IEEE Internet Things J. 7: 3300-3309, 2020. Go to original source...
  31. Marshall-Colon A., Long S.P., Allen D.K. et al.: Crops in silico: generating virtual crops using an integrative and multi-scale modeling platform. - Front. Plant Sci. 8: 786, 2017. Go to original source...
  32. Padhi B., Chauhan G., Kandoi D. et al.: A comparison of chlorophyll fluorescence transient measurements, using Handy PEA and FluorPen fluorometers. - Photosynthetica 59: 399-408, 2021. Go to original source...
  33. Pasha S.: ThingSpeak based sensing and monitoring system for IoT with Matlab analysis. - Int. J. New Technol. Res. 2: 19-23, 2016.
  34. Pommier C., Cornut G., Letellier T. et al.: Data standards for plant phenotyping: MIAPPE and its implementations. - In: 26. Plant and Animal Genome Conference (PAG XXVI), Jan 2018, San Diego, California, United States. Pp. 24 slides. San Diego 2018.
  35. Schansker G., Tóth S.Z., Holzwarth A.R., Garab G.: Chlorophyll a fluorescence: beyond the limits of the QA model. - Photosynth. Res. 120: 43-58, 2014. Go to original source...
  36. Shinkarev V.P., Govindjee G.: Insight into the relationship of chlorophyll a fluorescence yield to the concentration of its natural quenchers in oxygenic photosynthesis. - P. Natl. Acad. Sci. USA 90: 7466-7469, 1993. Go to original source...
  37. Sipka G., Magyar M., Mezzetti A. et al.: Light-adapted charge-separated state of photosystem II: structural and functional dynamics of the closed reaction center. - Plant Cell 33: 1286-1302, 2021. Go to original source...
  38. Stirbet A., Riznichenko G.Yu., Rubin A., Govindjee: Modeling chlorophyll a fluorescence transient: relation to photosynthesis. - Biochemistry-Moscow 79: 291-323, 2014. Go to original source...
  39. Verdouw C., Sundmaeker H., Tekinerdogan B. et al.: Architecture framework of IoT-based food and farm systems: A multiple case study. - Comput. Electron. Agr. 165: 104939, 2019. Go to original source...
  40. Walker B.J., Busch F.A., Driever S.M. et al.: Survey of tools for measuring in vivo photosynthesis. - In: Covshoff S. (ed.): Photosynthesis. Vol. 1770. Pp. 3-24. Humana Press, New York 2018. Go to original source...
  41. Yu J., Liberton M., Cliften P.F. et al.: Synechococcus elongatus UTEX 2973, a fast growing cyanobacterial chassis for biosynthesis using light and CO2. - Sci. Rep.-UK 5: 8132, 2015. Go to original source...
  42. Zhou W., Li L., Luo M., Chou W.: REST API design patterns for SDN northbound API. - 2014 28th International Conference on Advanced Information Networking and Applications Workshops. Pp. 358-365. IEEE, Victoria 2014. Go to original source...
  43. Zhu X.G., Govindjee G., Baker N.R. et al.: Chlorophyll a fluorescence induction kinetics in leaves predicted from a model describing each discrete step of excitation energy and electron transfer associated with photosystem II. - Planta 223: 114-133, 2005. Go to original source...