To date, Light Emitting Diodes-based (LED) illuminators are widely used for plants lighting in greenhouses in addition to natural light, as well as in plant factories without natural light. Optimization of artificial lighting parameters, such as the daily light integral and the ratios of different spectral components, can significantly reduce the cost of crop production in light culture including Space Greenhouses (SG) in Biological Life Support Systems (BLSS). However, the optimization of LED lighting systems is so far limited by the lack of information about the physiological effects caused by narrow-band radiation, as well as the complexity of the mathematical description of plant crops reactions to the changes of LED lighting parameters. In conditions of artificial illumination, crop producers usually strive to establish an optimal light regime that is constant throughout the whole growing season. However, there is experimental data on changes in the requirements for the illumination regime of crops with increasing age of plants. A promising approach to improving the parameters of crops LED lighting is the adaptive method of search engine optimization using biological feedback. The Adaptive Lighting System (ALS) is described on the basis of illuminator with red and white LEDs built at the Institute for Biomedical Problems (Moscow, Russia) for Chinese cabbage cultivation. The adaptive control procedure implements a continuous automatic search for current lighting parameters that provide optimal plant growth characteristics in real time. ALS includes a closed growth chamber with Light Assembly (LA) based on red and white LEDs, equipped with a Gas CO2 Analyzer (GA). The Photosynthetic Photon Flux Density (PPFD) from each type of LEDs can be controlled independently from each other according to the program in the MicroProcessor (MP). Periodically, infrared GA measures the decrease in CO2 concentration inside the growth chamber caused by Visible Photosynthesis (VF) of the crop. MP receives a signal from the GA output and calculates the photosynthesis rate of the crop, as well as the value of the lighting quality functional at the current time. Then the program compares the obtained values of the optimization criterion at the current moment and at the previous step and calculates the direction of the gradient according to picked algorithm and the new values of the LED supply currents, leading to a change in the value of the optimization criterion in the right direction. Further, the power supply unit realizes the currents of LED chains of each type and LA changes the plant lighting mode. As a criterion for the lighting quality in SG we used the minimum specific value of the Equivalent System Mass (ESM), which depends on the plants lighting regime. The cost coefficients of the unit of SG planting area equivalent mass and the unit of electric power consumed by SG significantly depend both on the spacecraft design and on the space expedition scenario. According to the literature, the equivalent system mass estimates depending on the light flux density and the crop light efficiency have been calculated in a spacecraft for the space expedition scenario at a long-term use lunar base with a crew of 4. To search for the current optimal lighting parameters during the plant growth, gradient and simplex algorithms were used. As optimization factors, the integral PPFD incident on the crop at the shoot tips level and the ratio of red and white light flux densities (factors X1 and X2, respectively) were used. Factor X1 was regulated in the range from 200 μmol/(m2·s) to 700 μmol/(m2·s), and factor X2 was from 0 to 1.5. The effectiveness of ALS was evaluated by comparing ESM when using ALS or the best constant LED lighting from comparison experiment. Adaptive optimization of Chinese cabbage crop lighting from the 14th to 24th day of vegetation according to the minimum ESM criterion (1) for the lunar base expedition led to a 14.9% saving in the SG equivalent mass. Similar systems with other optimization criterion can be use for terrestrial plant factories.
[1] ZABEL P, BAMSEY M, SCHUBERT D, et al. Review and analysis of plant growth chambers and greenhouse modules for space[C]//44th International Conference on Environmental Systems.Tucson, Arizona, United States: [s. l.], 2014.
[2] BERKOVICH YU A, SMOLYANINA S O, KRIVOBOK N M, et al. Vegetable production facility as a part of a closed life support system in a Russian space flight scenario[J]. Advances in Space Research,2009,44:70-176
[3] ROMANOV S, ZHELEZNYAKOV A G, TELEGIN A A, et al. Life support systems for crews on long-duration interplanetary missions[J]. Izvestiya RAN. Energetika (in Russian),2007,3:57-74
[4] BERKOVICH YU A, KRIVOBOK N M, SMOLYANINA S O, et al. Space greenhouses: present and future[M]. Moscow: Slovo (in Russian), 2005: 368.
[5] BERKOVICH YU A, KRIVOBOK N M, SINYAK Y., et al Developing a vitamin greenhouse for the life support system of the international space station and for future interplanetary missions[J]. Advances in Space Research,2004,34:1552-1557
[6] ZEIDLER C, VRAKKING V, BAMSEY M, et al. Greenhouse module for space system: a lunar greenhouse design[J]. Open Agriculture,2017,2:116-132
[7] JONES H W. Comparizon of bioregenerative and physical/chemical life support systems: ICES 2006-01-2082[R]. [S. l.]: SAE, 2006.
[8] LEVRI J A, VACCARY D A, DRYSDALE A E. Theory and application of the equivalent system mass metric: SAE Technical Paper 2000-01-2395[R]. [S. l.]: SAE, 2000.
[9] DRYSDALE A, EWERT M, HANFORD A. Equivalent system mass studies of missions and concepts: SAE Technical Paper 1999-01-2081[R]. [S. l.]: SAE, 1999.
[10] KANG M, WANG F Y. From parallel plants to smart plants: intellegent control and management for plant growth[J]. Journal of Automatica Sinica,2017,4(2):161-166
[11] AVERCHEVA O V, BERKOVICH YU A, KONOVALOVA I O, et al. Optimizing LED lighting for space plant growth unit: Joint effects of photon flux density, red to white ratios and intermittent light pulses[J]. Life Sciences in Space Research,2016,11:29-42
[12] MOKRONOSOV A T. The relationship of photosynthesis and growth functions[M]//Photosynthesis and the Production Process. Moscow: Nauka (in Russian), 1988: 109-121.
[13] RITCHIE J T, SINGH U, GODWIN D C, et al. Cereal growth, development and yield[M]//TSUJI G Y, HOOGENBOOM G, THORNTON P K, Eds. Understanding Options for Agricultural Production. Netherlands: Springer, 1998: 79-98.
[14] GIJZEN H, HEUVELINK E, CHALLA H, et al. Hortisim: a model for greenhouse crops and greenhouse climate[J]. Acta Horticulturae,1998,456:441-450
[15] MA Y, WEN M, GUO Y, et al. Parameter optimization and field validation of the functional-structural model GREENLAB for maize at different population densities[J]. Annals of Botany,2008,101(8):1185-1194
[16] YAN H P, KANG M Z, DE REFFYE P, et al. Dynamic architectural plant model simulating resource-dependent growth[J]. Annals of Botany,2004,93(5):591-602
[17] EVERS J B, VOS J, YIN X, et al. Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation[J]. Journal of Experimental Botany,2010,61(8):2203-2216
[18] MEDINA-RUIZ C A, MERCADO-LUNA I A, SOTO-ZARAZUA G M, et al. Mathematical modeling on tomato plants: a review[J]. African Journal of Agricultural Research,2011,6(33):6745-6749
[19] SPEETJENS S L, STIGTER J D, VAN STRATEN G. Towards an adaptive model for greenhouse control[J]. Computers and Electronics in Agriculture,2009,67(1-2):1-8
[20] FAN X R, KANG M Z, HEUVELINK E, et al. A knowledge-and-data-driven modeling approach for simulating plant growth: a case study on tomato growth[J]. Ecological Modelling,2015,312:363-373
[21] BERKOVICH Y, KONOVALOVA I O, SMOLYANINA S O, et al. LED crop illumination inside space greenhouses[J]. REACH - Reviews in Human Space Exploration,2017,6:11-24
[22] BERKOVICH Y, KONOVALOVA I O, EROKHIN A N, et al. LED lighting optimization as applied to a vitamin space plant growth facility[J]. Life Sciences in Space Research,2019,20:93-100
[23] AVERCHEVA O, BERKOVICH Y, SMOLYANINA S., et al Biochemical, photosynthetic and productive parameters of Chinese cabbage grown under blue-red LED assembly designed for space agriculture[J]. Advances in Space Research,2014,53:1574-1581
[24] KORBUT V L. Optimization of plant productivity in biotechnical systems.[M]//Problems of optimization in biotechnical systems using computer technology. Moscow, Russian: [s. n.], 1981: 5-32.
[25] GACHINSKY E, DROZDOV A, CHERKASHIN M. Adaptation in continuous systems of automatic search[M]. Moscow, Russian: [s. n], 1991.
[26] KARMANOV V G. Mathematical programming[M]. Moscow, Russian: [s. n], 1986.
[27] CHARLES-EDWARDS D A. The mathematics of photosynthesis and productivity[M]. New York: Academic Press, 1981.
[28] BERKOVICHYU A, OCHKOV O A, PEREVEDENTSEV O V, et al. Selection of algorithms for adaptive optimization of plant photosynthesis in space greenhouses[J]. Aviakosmicheskaya i Ekologicheskaya Meditsina (in Russian),2019,53(2):85-92
[29] DRYSDALE A, BUGBEE B. Optimizing plant habitat for space: a novel approach to plant growth on the Moon: SAE technical paper 2003-01-2360[R]. [S. l.]: SAE, 2003.
[30] DRYSDALE A, MAXWELL S, EWERT M, et al. System analysis of life support for long duration missions: SAE technical paper 2000-01-2394[R]. [S. l.]: SAE, 2000.
[31] THIMIJAN R W, HEINS R D. Photometric, radiometric and quantum light units of measure: a review of procedures for interconversion[J]. HortScience,1983,18(6):818-822
[32] BERKOVICH YU A, OCHKOV O A, PEREVEDENTSEV O V. Substantiation of the approach to adaptive optimization of light-emitting diode illumination of crops in vitamin greenhouses within the life support system for space crews[J]. Aviakosmicheskaya i Ekologicheskaya Meditsina (in Russian),2018,52(6):86-94
[33] DRYSDALE A, NAKAMURA T, YORIO N, et al. Use of sunlight in a bioregenerative life support system - Equivalent system mass calculations[J]. Advances in Space Research,2008,42:1929-1943