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.
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