Method for controlling the plant growth during a plant day

Abstract

1) Method for controlling the plant growth at an indoor cultivation area, wherein the method comprising the following steps: A Provision of data about the changes of at least three parameters, that change over the course of 24 hours day cycle at an outdoor cultivation of plants, wherein said parameters related to: i) a first parameter related to light ii) a second parameter related to nutrients iii) a third parameter related to the atmosphere and / or humidity B controlling of the conditions of the plant growth such that B1 a change of at least two of the parameters wherein the third param- eter remains constant and / or B2 a change of only the second parameter whereas the first parameter remains constant to less than 40% of the average value for a maximum of said first parameter over the sequence of a day, and wherein said change over sequence of a day in step B1 or step B2 is con- trolled with the data of step A.

Description

[0001] METHOD FOR CONTROLLING THE PLANT GROWTH DURING A PLANT DAY

[0002] The current invention relates to a method for controlling the plant growth of a plant during a plant day.

[0003] A plant has no specific time control. Thus, a plant day can be different to a normal day having 24h. The invention uses this approach for the development of the current invention.

[0004] With growing pollution of soil, water, and air, modem farms try to reduce the contamination of plants with polluted air, water, and soil by using clean water, artificial light, standardized soil alternatives, or even aqua- or aeroponic installations. These modern farms, whether they are called vertical farms or indoor farms, control parameters mostly as isolated parameters or bundled in a farming system.

[0005] US10765069B2 is for non-confined systems and more specifically, CN113342036A and CN109115268A use manned or unmanned aerial vehicles for data retrieval of plants’ growth status.

[0006] US2017035002A1 uses an apparatus and US2020110933A1 specific sensors for plant growth optimization, whereas the herein-described invention is an apparatus- and sensor-agnostic programmable system. Further, the herein-described invention can but does not necessarily need to rely on loT, as described in US10803312B2, and is for a single confined farm and not for multiple farms as described in JP2021093971A, allowing, however, to compare data from multiple farms.

[0007] CN106886187A describes a monitoring system. Monitoring systems, whatsoever, lack the possibility to actively control parameters by either following a recipe or by closed- loop intelligence rendering such systems weak when applying such systems to automated farms. Similarly, EP3996012A1 lacks the possibility to apply it to fully automated farming systems as instructions to workers for on-site manipulation are given for plant growth parameters.

[0008] US2013006401 describes a system that controls and optimizes plant growth according to a formula, however, for regulated industries, such as pharmaceutical, cosmetical, and supplements, recipes are more common and work with statistical tolerances and confidence intervals in regards to boundaries rather than calculations based on formulas.

[0009] WO2021251969A1 utilizes Al and / or machine-learning algorithms to optimize plant growth. However, machine learning, neuronal networks, and Al lack the ability to calculate statistical significance as specifically with Al, the output is calculated de-novo based on input data and / or results from previous outputs and thus, cannot be used for Quality by Design (QbD), tolerance analysis, and other statistical solid methods to prove by objective evidence the correctness and robustness of the results. In a similar way, US2022338421A1 makes use of machine learning algorithms to determine health predictors. Whatsoever, these predictors are not validated nor verified whereas biomarkers are specific and proven for a biochemical reaction and pathway leading to a plant-specific outcome.

[0010] None of these methods describe a controlled environment control system, as a programmable system, for controlling a plant day and utilizing the data with machine learning, Al, or neural networks for trends analysis and / or biomarker, digital biomarker determination only that are subject to separate statistical significance tests.

[0011] All of these methods can more or less control the parameters over time but lack internal checks, calibrations, growth protocol segmentation, and plant feedback as closed-loop or intelligent closed loops, such as with statistical sound methods and significance and / or trend analysis with machine learning, neuronal networks, or artificial intelligence for determining potential biomarkers or digital biomarkers. All of these systems, in addition, do only parametrize the growth protocol but are not sophisticated enough to mimic a day or a plant day alone or in response to the plant and the parametrized environmental factors.

[0012] It is thus the object of the invention to provide a method with an optimized plant day for more enhanced growing condition of a plant.

[0013] The object is achieved by a method with the subject-matter of claim 1 .

[0014] An inventive method for controlling the plant growth at an indoor cultivation area is characterized by the following steps:

[0015] A Provision of data about the changes of at least three parameters, that change over the course of 24 hours day cycle at an outdoor cultivation of plants, wherein said parameters related to: i) a first parameter related to light ii) a second parameter related to nutrients iii) a third parameter related to the atmosphere and / or humidity

[0016] All three parameters normally change over the course of a normal 24h-day-sequence during outdoor farming. While it might be logic that the light change from day to night, also the nutrients are changing. Some nutrients such as CO2 depend on photosynthesis. Other plants, such as tomatoes, accumulate nutrients during the day and mainly grow over night. Further to this with enhanced temperatures due to sunlight, the water and the soil may be warmer. Thus, the solubility of some nutrients in water is enhanced. The third parameter is the change of the atmosphere. The atmosphere comprises air, such as air pressure, the amount of CO2 produced over the day is higher at day than at night. Further the humidity of the air may change at day. Since there is a higher tendency of condensation at night, the humidity might be higher at day. The same applies for the humidity of soil.

[0017] B controlling of the conditions of the plant growth such that there is B1 a change of at least two of the parameters wherein the third parameter remains constant and / or

[0018] B2 a change of only the second parameter whereas the first parameter remains constant to less than 40% of the average value for a maximum of said first parameter over the sequence of a day,

[0019] The control of the plant growth based on the data provided in step A might be B1 a change of at least two parameters while the third parameter remains constant. It was found that only two parameters are necessary to simulate in indoor farming a normal day for a plant.

[0020] Alternatively, the growth of the plant can be controlled in indoor farming such that the change of only the second parameter whereas the first parameter remains constant to less than 40% of the average value for a maximum of said first parameter over the sequence of a day. Thus, the third parameter may remain constant as well.

[0021] Said changes of said one or more parameters over the sequence of a day in step B1 or step B2 is controlled with the data of step A. It was found that the plant does not need a full adaptation of the sequence of a day with all necessary parameters. At least one parameter can be held constant while the other parameter is changed just as it would be a normal day in nature. The day is an average day that can be based on average worldwide values. International database for rain, sunshine, wind and further data can be used to find data for an average day. Day with extreme weather conditions such as hail or drought are not used as data base.

[0022] The data for an average day sequence may be alternatively determined on different cultivation areas in different regions of the world, such as Europe, over the course of a year. The data may be determined for a simplified model where only some cultivation areas with high yield for the specific plant that should be grown are taken into account.

[0023] Further advantages are subject matter of the sub-claims.

[0024] It is of advantage when in step A said data about the changes of said at least three parameters is provided with at least four periods of time, which represent the change of the first, second and third parameter at night time, sunrise, day time and sunset which is the sequence of a day. This means that the data does not need to represent a day sequence in detail but the main periods of a day.

[0025] It is further of advantage if said night time, sunrise, day time and sunset correspond with the global average amount of time of each period over a time of 24h at an outdoor cultivation area, wherein the repetition of the sequence of said periods, representing a day, is at least one hour more or less than 24h. Thus, the plant day is not 24h but can be reduced or enhanced due to the specific needs of a plant. Reducing the time for a day, for example to 19h instead of 24h, a plant may be fully grown in far less time than 100 days for a normal growth.

[0026] Thus, it is of advantage if the repetition of the sequence of said period is less than 20 h.

[0027] It is of advantage for a simplified control mechanism, when during night time or day time the first parameter remains constant and when during sunrise or sunset said first parameter change.

[0028] It is energy saving if the first parameter remains at less than 5%, preferably less than 2%, of the average value for the maximum of said first value.

[0029] Said first parameter might preferably be the light intensity and / or the change of the spectrum of light.

[0030] Said second parameter may preferably be the concentration of nutrition in aqueous solution and / or nutrient intake of the plant that change over the sequence of a day.

[0031] Said third parameter may preferably be the average amount of wind, heat, CO2-Con- centration, O2-Concentration, air humidity and / or soil humidity over the sequence of a day. It is of advantage if the method allows the day / night cycle simulation being able to be extended by preparatory, harvest and / or post-harvest segments.

[0032] It is further of advantage if said data in step A are provided by a determination of the outdoor cultivation of plants at different outdoor growing areas at different location and / or at different seasons of the year.

[0033] Step B may be further controlled by measurement, preferably optical measurement, of the plant growth and data analysis by means of statistical and non-statistical procedures using data of said measurement.

[0034] The method can be implemented in a programmable system which is further described below. The subject-matter of the system may be also implemented in the inventive method. Said system and method are described with the help of Figures:

[0035] Fig 1 schematic drawing of a programmable system for the application of the inventive method.

[0036] Fig. 1 shows a programmable system (1 ) to control plant growth for single or multiple plants in one or more segments (2) which can be for example a startup phase, a growth phase, and a harvest phase. The more automated or aseptic the growth compartment is run, the more important a first segment (3) for different manual, semi-automated, or automated steps becomes.

[0037] As this initial segment can be overlapping with a potential second segment or the steps (4) within the segment, it can be carried out in series or in parallel, time-consuming steps can be smartly aligned to reduce the time during which no plant growth can be carried out. Supportive intelligence, whether it is software or algorithm, can align these tasks in a manner to reduce time and resources. Such resources of primary interest can be electrical energy, water, nutrients, reference materials, or even manual operations and manipulations on the installation and equipment. To further support this, data on tasks and resources can be collected by the system from the equipment, installation, or infrastructure by means of sensors, run time and / or sensor metadata.

[0038] During this initial segment, preventive maintenance or regular maintenance can be carried out. Even though maintenance or replacements could be made during a growth cycle, depending on the layout and architecture of the infrastructure, replacements, refills or replenishments should be preferably made during this segment of the process. The supportive intelligence can calculate, based on the information provided of the envisioned or planned growth of plant type and its process which parts of the equipment or infrastructure has to be maintained, refilled, and / or replenished. To further support this, the system can make use of sensor readings and literature data. Sensor readings can be of biological, physical, or chemical nature, and in most cases electrochemical, electromagnetic, physicochemical, chemical, physical, visual, and / or audio signals.

[0039] During this initial segment, checks on the integrity and correct functioning of the infrastructure can be carried out manually, semi-automatic, or automatically. Depending on the nature of the infrastructure, different checks might be carried out for safety reasons or plant contamination reasons whereas others might be carried out for quality assurance and comparability reasons. Checks related to safety can be leakage, filling levels, capacity levels, and / or clogging. Further, exchanges of filters or filter cascade systems, as found in clean rooms or aseptic rooms, can be made during this segment.

[0040] During this initial segment, stocks of nutrients, gases, liquids, and other consumables can be replenished and filled. Most probably, stocks of nutrients might be refilled or exchanged to match the upcoming, subsequent segment of plant growth and / or the plant type. This might be true for reference materials as well as needed for testing, reference, and / or comparison during the process and subsequent segments and / or steps.

[0041] For sensors and actuators, for which internal and / or external calibration, alignment, or functional checks are possible, these can be carried out during this segment. Here, sensors can be specific or summative analyte sensors, such as for a specific gas in the atmosphere, e.g. CO2, N2, O2, CO, O3, or other gases found in the natural atmosphere as well as gases coming specifically from the plant, such as terpenes and other volatiles.

[0042] Each phase can be segmented into one or multiple steps and uses one or multiple parameters and feedback from the plant, either directly or indirectly, to control the plant growth towards a specification or global plant growth goal.

[0043] The individual steps in such a protocol can be overlapping in time and can have one or more parameters that are controlled in the same or different ways, being defined herein as control type (5), such as step-wise, linear, sinus, cosinus, cotangent, tangent, hyperbolic, exponential, constant, pulsed, increasing or decreasing as controlled by slope and / or feedback, as in proportional, integral or derivate controls.

[0044] As potential second segment (6), which can be a sequence of steps or a single step only, is the plant growth segment. Most promising sequential steps, among others, depict a simulated day of a plant day (7). Here, a day not necessarily has to be 24 hours but can be shorter or longer. The plant day, which can differ from a regular earth day, can be programmed by the programmable system or provided as input by an operator. The simulated plant day makes use of different controllable parameters (8), such as atmospheric composition, atmospheric pressure, illumination, temperature, humidity, nutrition composition, nutrition temperature, pH, conductivity, osmolality, and / or alike.

[0045] The programmable segment or sequences of steps or single steps mimics a day with at least sunrise, day-time, sunset, and night-time, each most preferably being a programmable step. Each of these steps can be programmable by the controlled parameters, time, and control type (5). As an example, figure 1 shows a day with these 4 steps, the atmospheric temperature (i), the time (ii) and the control type (5) being linear.

[0046] To mimic the plant day, orientation on natural processes on earth could be followed meaning that the steps for sunrise, day-time, sunset, and night-time could be aligned for the controlled parameters such as a sinus control type of illumination increase including shifts in the electromagnetic spectrum due to the generally known atmospheric absorption of wavelengths of the light relevant for plant growth and / or photosynthetic activity. In addition, the atmospheric conditions change during sunrise with increasing temperature and subsequent increase or decrease of relative humidity as well as, but not necessarily occurring, upcoming wind due to thermal changes on the earth’s surface and in the atmosphere. From either naturally occurring or man-made pollution, the atmospheric gas composition changes with upcoming irradiation from the sun potentially, but not necessarily, by starting chemical reactions in the atmosphere, such as the known reaction of NO2 and O2 under UV-irradiation to NO and O3 or the formation of O3 from O2 during thunderstorms. Especially O2 is of importance as this is one of the transpiration waste-product of the plants from photosynthesis and / or during the resting period of the plant during the night-time. Further, photosynthesis is only active when light is irradiated onto the plant’s leaves and CO2 is present to be fixed into the plant. Thus, CO2 is important during sunrise, day-time, and sunset, as light exposure is given only during these steps. Nutrients and water are understood to be mainly taken up by the roots of the plant which follow a biological sequence as well and need to be present when the plant is in need of water and nutrients dissolved or transported with the water to or into the plant. The root compartment of the plant thus has different conditions from the part of the plant exposed to the atmosphere. Therefore, control of the water and nutrients is independently necessary from the atmospheric conditions. As all of these parameters change during sunrise to levels held within boundaries during the day-time, they might decrease or increase with a control type during sunset. Again, as with the sunrise, the parameters are controlled in the same manner to bring the parameters back to the state before sunrise for the upcoming night-time. Some parameters, such as the frequency of earth’s harmonic frequency, are parameters that are globally present during each step and can be controlled as well. The parameters could drift from day to day towards a global goal or, according to natural processes on earth, follow a band between average and extreme conditions for the months on earth during which the plant would naturally grow.

[0047] This approach, as versatile in the parameters and steps, can be used to simulate growth in greenhouses and other installations on other planets as well by adjusting the control type and other parameters according to the envisioned or simulated planet.

[0048] The programmable system (1 ) is further specified by parameters to control the atmospherically exposed part of the plant, the root compartment, and global parameters present for both the atmospherically exposed plant as well as the root compartment. As the programmable system is specific to the plants, the parameters are specific to plant growth and / or suspected to influence plant growth. The parameters for the atmospheric exposed plant part can be activated in the programmable system either as a single parameter or a multitude of it. Parameters, which can be controlled are atmospheric pressure to mimic altitude, and atmospheric composition to mimic changes in the gas due to atmospheric changes and / or chemical reactions whereas the gases N2, O2, Ar, CO2, Ne, He, H2, CH4, Kr, NO2, NO, O3, NH3, either alone or in combination. Other gaseous moieties can be detected in the atmosphere and added to the composition either by the plant or by a controlling system which are volatile organic carbons (VOC), such as isoprenoids, terpenoids, phenylpropanoids, benzenoids, fatty acid derivates, sulfur compounds (e.g. brassicales), furancoumarins and their derivatives (e.g. apiales, asterales, fabales, resales) and oxygenated VOC (e.g. methanol, acetone, acetaldehyde, methyl- ethyl-ketone, methyl-vinyl-ketone) to detect or mimic specific stages of the plant, such as growth or flowering stage. Partial gas pressure, e.g. CO2 during day-time or O2 during night-time can be controlled in the atmosphere. Wind speeds, wind direction, temperature, and humidity can be controlled in addition and together or separately from the light intensity, spectrum, brilliance, photon pressure, photon momentum, light polarization, and / or light distance from the plant tip, and / or light focus, which all can be controlled and / or present as single or multiple parameters. Electromagnetic data, such as visual data, but not bound to the visual range of the electromagnetic spectrum, can be controlled by the programmable system as well by means of visual representations of the plant over time and data thereof, e.g. color, shape, absorbance, and dimensions of plant parts.

[0049] Leave or other plant parts nutrients, can be controlled by composition with the ingredients as single chemicals or in a mixture, such as cations, e.g. calcium, potassium, sodium, phosphorous, magnesium, copper, manganese, zinc, cobalt, ammonia boron, molybdenum, sulfur, iron, anions, e.g. nitrate, nitrite, phosphate, phosphite, sulfate, sulfite, chlorate, chlorite, chloride, oxide, plant vitamins, e.g. thiamine, pyridoxine, and their HCI-derivatives, nicotinic acid, vitamin A, vitamin B2, vitamin B3, vitamin B5, vitamin B7, vitamin B9, vitamin C, vitamin D, vitamin E, vitamin K, cyanocobalamin, glycine, plant hormones, e.g. class of auxins with (indole-3-acetic acid, naphthalene acetic acid, 2,4- dichlorophexyacetic acid, 3-indoleacetic acid, indole-3-butyric acid, indole-3-acetalde- hyde, 4-chloroindole-3-acetic acid, phenylacetic acid, 2,4,5-trichlorophenoxyacetic acid), class of cytokinins (with 6-benzylaminopurine, kinetin, zeatin, isopentenyladenine, 6- benzyladenine, N6-(2-isopentenyl)-adenine, N6-(2-Hydroxybenzyl)-adenine, Dihydrozeatin, meta-Topolin), class of gibberellins (with GA1 , gibberellic acids(GA3), GA4, GA5, GA6, GA7, GA8, GA9), class of acids (with abscisic acid and derivatives thereof, salicylic acid, jasmonic acid and derivates thereof), class of brassinosteroids (with brassino- lide, castasterone, typhasterol, homobrassinolide, dolicholide, 24-epibrassinolide, 6-de- oxocastasterone, 28-homocastasterone, 28-norcastasterone, Teasterone, 28-norcho- lasterol, 6-deoxotyphasterol, 28-homosteasterone, cathasterone, 24-epicastasterone, 28-homobrassinolide, 28-norbrassinolide), class of gases (with ethylene, methane, nitrogen oxide, carbon monoxide, carbon dioxide, oxygen, ozone, hydrogen sulfide), carbon sources (with monosaccharides, such as, glucose, fructose, galactose, ribose, mannose, allose, altrose, gulose, idose, talose, with disaccharides, such as sucrose, lactose, maltose, with trisaccharide, such as maltotriose, panose, raffinose, 4’-galacto- syl-lactose (GOS), 1 -kestose (FOS), gentianose, with higher saccharides, such as starch, cellulose, amylose, amylopectin, glycogen) with Murashige and Skoog salts, Gamborg’s salts, Woody Plant Medium (WPM) salts, White’s medium salts, 2-(N-mor- pholino)-ethanesulfonic acid (MES), and / or Schenk and Hildebrandt medium salt.

[0050] The root compartment can be controlled separately or together with the atmospheric exposed plant part. For the root compartment, parameters such as humidity, temperature, and nutrients composition with the selection of at least 3 of the nutrients mentioned for the atmospheric exposed plant part, and for the nutrients, provided as a liquid, osmolality, ion strength, total organic carbon, pH, conductivity, dissolved CO2, O2 and / or O3 concentration, particle count and / or particle size distribution, volume flow, temperature, turbidity, density, color and / or absorbance. Specifically, for aeroponic systems, the pressure, particle size, focus of the aeroponic spray, angle of injection of the aeroponic spray, wind direction of the aeroponic mist, and / or wind speed of the aeroponic mist can be controlled and / or measured by the programmable system. Visual data can be controlled by the programmable system as well by means of visual representations of the aeroponic spray, roots over time and / or data thereof, e.g., color, shape, absorbance, and dimensions. Global parameters can be controlled and / or measured by the programmable system for the entire plant or the atmospheric exposed part and / or root part separately. These parameters are sound frequencies, either discrete or variable with a control type, and vibration frequencies, either discrete or variable with a control type.

[0051] The programmable system can derive logic either from literature or by statistical means, as applied in neuronal networks, Design of Experiments, historical data analysis, machine learning, or artificial intelligence. As certain measurements and / or controls are strictly bound to logic, e.g., photosynthesis is only possible with light and CO2, the programmable system can follow such basic rules by not compensating or adding CO2 to the atmosphere during night-time or not switching on the lights when CO2 depletion or below a limit is present. Such logic is based on biologies, physics, and chemistry and depicts hard rules for the programmable system and can be present or not. If present, such hard rules shall never be crossed by the programmable system.

[0052] The programmable system can derive, by means of statistics, conclusions about potential prospective outcomes of specific factors of the plant. For example, a higher photosynthetic fixation of CO2 together with sensor readings on CO2 partial atmospheric pressure and increased light luminescence and adjusted spectrum might lead to a faster growth of the plant which can be visualized by the visual system recording the plant growth vertically and horizontally and comparing the data for the same genus of plants to previous sensor readings. Such logic is programmable as tests into the programmable system and / or can be explored by comparing sensor readings and process controls over multiple runs by means of data visualization and / or statistical calculations, such as median, mean, quantiles, variance, standard deviation, average, minimum, maximum, or secondary analysis such as analysis of variance (ANOVA), f-test, T-test, outlier-tests, e.g. Grubb’s test, Tietjen-Moore Test, Generalized extreme studentized deviate (ESD), significance test, Mann-Whitney test, Wilcoxon-Mann-Whitney-Test, and others.

[0053] The programmable system can further be supportive and / or decisive in prospective experimentation towards plant growth protocol establishments and / or optimization towards a global goal of a given genus of plant. The programmable system can be used to learn, by means of statistics, from sensor readings, such as that the specific sensor for VOC detects a chemical in the atmosphere over the plant, visual data can be compared to estimate whether the VOC is a plant signal for initiating a specific stage in the growth of the plant, such as the flowering stage. These data can then be accessed by the programmable system for post-processing and comparing multiple growth cycles for these sensor readings. In a verification experiment, the programmable system can then, for example, add the VOC to the atmosphere to force the flowering stage of the plant and detect the successful flowering by other sensors, such as the visual system to verify the statistically higher incidence and logic behind the sensor readings. With one or more such sensor readings, so-called biomarkers or digital biomarkers can be found by means of statistics by the programmable system and recommended for further investigation and verification.

[0054] The programmable system can use the sensor reading or readings of multiple sensors to extrapolate these digital or non-digital biomarkers to another plant genus for intra- and / or trans-genus verification. For clarification, such sensor readings are digital sensor readings with time stamps for later time-corrected analysis and trending.

[0055] Plant feedback can be manifold and can come as single feedback or multiple feedback at once. Plant feedback can be, for example, the change of color in leaves due to changed nutrient feed to the root compartment of the plant. By applying statistical algorithms, e.g., Design of Experiment, multivariate data analysis, or time-dependent analysis, a single or multiple plant genus can be tested for different plant feedbacks and statistically analyzed to conclude on specific feedbacks.

[0056] By applying statistical methods, the programmable system can optimize a single or multiple plant genus towards specific and / or global goals. Such goals can be, for example, faster vertical growth, a faster biomass yield, bigger flowers, and / or higher concentration of secondary metabolites in the plant. The optimization, however, is limited by the phenotypical boundaries given by the genus. These boundaries can be explored by the programmable system as well when applying specific analytical methods, such as response-surface methods, and other optimization algorithms.

[0057] List of references:

[0058] 1 programmable system

[0059] 2 segment 3 preparatory or maintenance segment

[0060] 4 steps

[0061] 5 control type

[0062] 6 day / night cycle

[0063] 7 day 8 parameters i: temperature ii time

Claims

Claims1 ) Method for controlling the plant growth at an indoor cultivation area, wherein the method is characterized by the following steps:A Provision of data about the changes of at least three parameters, that change over the course of 24 hours day cycle at an outdoor cultivation of plants, wherein said parameters related to: i) a first parameter related to light ii) a second parameter related to nutrients iii) a third parameter related to the atmosphere and / or humidityB controlling of the conditions of the plant growth such that there isB1 a change of at least two of the parameters wherein the third parameter remains constant and / orB2 a change of only the second parameter whereas the first parameter remains constant to less than 40% of the average value for a maximum of said first parameter over the sequence of a day, and wherein said changes of said one or more parameters over sequence of a day in step B1 or step B2 are controlled on the basis of the data of step A.2) Method according to claim 1 , characterized in that in step A said data about the changes of said at least three parameters is provided with at least four periods of time, which represent the change of the first, second and third parameter at night time, sunrise, day time and sunset which is the sequence of a day.3) Method according to claim 2, characterized in that said night time, sunrise, day time and sunset correspond with the global average amount of time of each period over a time of 24h at an outdoor cultivation area, wherein the repetition of the sequence of said periods, representing a plant day, is at least one hour more or less than 24h.4) Method according to claim 1 or 2, characterized in that the repetition of the sequence of night time, sunrise, day time and sunset of the method is less than 20 h.5) Method according to claim 2, characterized in that during night time or day time the first parameter remains constant and that during sunrise or sunset said first parameter change.6) Method according to claim 2, characterized in that the first parameter remains at less than 5%, preferably less than 2%, of the average value for the maximum of said first value.7) Method according to one of the preceding claims, characterized in that said first parameter is the light intensity and / or the change of the spectrum of light.8) Method according to one of the preceding claims, characterized in that said second parameter is the concentration of nutrition in aqueous solution and / or nutrient intake of the plant that change over the sequence of a day.9) Method according to one of the preceding claims, characterized in that said third parameter is the average amount of wind, Temperature change, CO2-Con- centration, O2-Concentration, air humidity and / or soil humidity over the sequence of a day.10)Method according to one of the preceding claims, characterized in that allowing the day / night cycle simulation being able to be extended by preparatory, harvest and / or post-harvest segments.11 )Method according to one of the preceding claims, characterized in that said data in step A are provided by a determination of the outdoor cultivation of plants at different outdoor growing areas at different location and / or at different seasons of the year.12)Method according to one of the preceding claims, characterized in that step B can be further controlled by measurement, preferably optical measurement, of the plant growth and data analysis by means of statistical and non-statistical procedures using data of said measurement.13)Method according to one of the preceding claims, characterized in that in step B the third parameter remain constant over the sequence of a day.14)Programmable system (1 ) for the performance of a method according to one of the preceding claims.

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