Self-learning non-integrated luminaire

Abstract

A non-integrated luminaire (100) is arranged in a horticultural environment (HE). The non-integrated luminaire includes a light source (102) and a controller (104). The light source provides illumination and affects environmental parameters of the horticultural environment based on one or more illumination attributes (106). The lighting properties may include spectrum, light intensity, spatial position, and / or light direction. The controller is communicatively coupled with the sensor (110). The sensor measures environmental parameters. The sensor includes at least one of an ambient temperature sensor (112) or a plant temperature sensor (116). The controller generates an optimized lighting recipe based on the measured environmental parameters and an optimization model. The optimization model calculates a weighted average of the differences based on the measured environmental parameters recorded during the modification of the one or more lighting attributes. The controller also adjusts one or more lighting attributes according to the optimized lighting recipe.

Description

Technical Field

[0001] The present disclosure generally relates to systems and methods for providing optimized lighting from one or more non-integrated illuminators based on ambient parameters measured around the illuminator. More specifically, the present disclosure relates to self-learning non-integrated illuminators and self-learning non-integrated illuminators that optimally illuminate a horticultural environment based on measured ambient parameters. Background Art

[0002] The legalization and decriminalization of cannabis across the United States is expected to significantly increase the market for home and small farm growers. These home and small farm growers include those who use indoor or enclosed growing areas such as small greenhouses or grow rooms. On large farms, lighting systems are typically integrated with other farm systems such as heating, ventilation, and air conditioning (HVAC) systems. Integrating the lighting system with the HVAC system enables these two systems to optimize their impact on plants growing in a horticultural environment. For example, in an integrated system, the lighting and HVAC systems can be coordinated such that the grow room temperature follows the day and night set points with minimal variance since the integrated system quickly adjusts for deviations measured in temperature due to the rich information provided by the sensors and controllers of the integrated subsystems.

[0003] However, home and small farm growers often lack the resources to integrate lighting systems with HVAC systems and typically rely on off-the-shelf non-integrated illuminators. Such non-integrated grow rooms are often sub-optimal. For example, the lighting arrangement may turn on non-integrated illuminators at full load without considering the subsequent impact on the cooling load (the power required to remove excess heat) of the HVAC system and plant stress, resulting in a rapid increase in lighting and temperature that negatively impacts the growing plants. It is estimated that 50% (in the case of light-emitting diode (LED) illuminators) to 62% (in the case of high-pressure sodium (HPS) illuminators) of the thermal energy is typically trapped in the grow room. This trapped heat ultimately becomes the cooling load of the HVAC system. Additionally, studies have shown that asynchronous fluctuations in the light and temperature regimes not only affect processes at the molecular level of plants but also result in changes in morphological traits at the whole-plant level. Studies have also shown that plant stress is observed when variable temperature is combined with fixed light conditions. These results suggest that plant production and experiments should avoid applying a constant level of light in an environment with temperature fluctuations (i.e., a greenhouse). Additionally, the unnecessary increase in heat also results in higher energy costs and a larger carbon footprint. Thus, there is a need in the art for improved systems and methods for non-integrated illuminators in home and small farm environments. Summary of the Invention

[0004] The present disclosure generally relates to self - learning, non - integrated illuminators, and more particularly, to self - learning, non - integrated illuminators configured to optimally illuminate a horticultural environment based on environmental parameters measured around the illuminator. The non - integrated illuminator includes one or more light sources, such as light - emitting diodes (LEDs) or high - pressure sodium (HPS) light sources. The light sources are configured to illuminate one or more plants (or plant canopies) within the horticultural environment. The light sources can be arranged in various configurations, such as in ceiling - mounted or grid - light configurations, but any suitable configuration can be envisioned. The horticultural environment can be a greenhouse, a growing room, a growth chamber, or other indoor and / or enclosed agricultural spaces.

[0005] The non - integrated illuminator envisioned herein also includes a controller. The controller is communicatively coupled to a sensor configured to measure environmental parameters within the horticultural environment. In some examples, one or more of the sensors can be embedded within the non - integrated illuminator. In some other examples, one or more of the sensors can be arranged outside the non - integrated illuminator, such as close to one or more of the plants within the horticultural environment. The external sensors can provide the measured environmental parameters to the controller via or using a wired or wireless connection. The sensors can include an ambient temperature sensor, a plant temperature sensor (such as a thermal camera), and / or a light sensor (such as a multispectral camera or a photosynthetically active radiation (PAR) sensor). The environmental parameters can be measured at a predetermined time interval or continuously otherwise.

[0006] The controller then feeds the measured environmental parameters into an optimization model to generate an optimized lighting recipe. The optimized lighting recipe sets one or more lighting attributes of the non - integrated illuminator. The lighting attributes can include spectrum, light intensity, spatial location, light direction, etc. These lighting attributes can be wavelength - dependent, such as varying the light intensity or light direction over a certain light wavelength range. The lighting attributes then prescribe the lighting provided by one or more light sources of the non - integrated illuminator. The optimization model can be a reinforcement machine - learning model, such as a multi - armed bandit (MAB) problem model. These reinforcement machine - learning models are configured to continuously determine the optimal lighting attributes to improve the measured environmental parameters. For example, the optimization model can determine an optimized light intensity level to reduce the ambient and plant temperatures to a safe level for the plants in the horticultural environment. In other examples, the optimization model can determine the optimized lighting recipe based on user feedback corresponding to the user's assessment of plant health and ambient temperature.

[0007] Unlike integrated luminaires, non-integrated luminaires are not communicatively coupled to other systems operating in the horticultural environment, such as heating, ventilation, and air conditioning (HVAC) systems. However, non-integrated luminaires can be communicatively coupled to additional non-integrated luminaires within the horticultural environment, such as via a wired or wireless connection. Non-integrated luminaires can exchange information with each other, such as lighting attributes, measured environmental parameters, or optimized lighting recipes, for further optimization.

[0008] Generally, in one aspect, a non-integrated luminaire is provided. The non-integrated luminaire is arranged in a horticultural environment. According to some examples, the non-integrated luminaire can be a ceiling light or a grid light.

[0009] The non-integrated luminaire includes one or more light sources. The one or more light sources are configured to provide illumination based on one or more lighting attributes and to affect one or more environmental parameters of the horticultural environment. According to one example, at least one of the one or more light sources is an LED or an HPS lamp. According to one example, the one or more lighting attributes include spectrum, light intensity, spatial location, and / or light direction. At least one of the one or more lighting attributes can be wavelength-dependent.

[0010] The non-integrated luminaire further includes a controller. The controller is communicatively coupled to one or more sensors. The one or more sensors are configured to measure one or more environmental parameters. The one or more sensors include at least one of an ambient temperature sensor or a plant temperature sensor. The ambient temperature sensor is configured to measure ambient temperature data. The plant temperature sensor is configured to measure plant temperature data. According to one example, the plant temperature sensor can be a thermal camera, a single-pixel thermopile sensor, or a multi-pixel thermopile array.

[0011] The controller is configured to generate an optimized lighting recipe. The optimized lighting recipe is based on the measured environmental parameters and an optimization model. The optimization model is configured to calculate a weighted average of differences. The weighted average of differences is based on the measured environmental parameters recorded during a modification of one or more lighting attributes. The optimization model can be a reinforcement learning model. According to one example, the optimized lighting recipe can be further generated based on user feedback received by the controller.

[0012] The controller is further configured to adjust one or more lighting attributes. The one or more lighting attributes are adjusted according to the optimized lighting recipe.

[0013] According to one example, the one or more sensors can include a light sensor. The light sensor can be configured to measure spectral data. The light sensor can be a multispectral camera or a photosynthetically active radiation (PAR) sensor.

[0014] According to one example, the controller can also be configured to receive a second optimized lighting recipe from a second non-integrated illuminator via a wired or wireless connection. Adjustment of one or more lighting attributes can also be based on the second optimized lighting recipe.

[0015] According to one example, the controller is also configured to transmit an optimized lighting recipe to a second non-integrated illuminator via a wired or wireless connection.

[0016] According to one example, one or more environmental parameters are provided to the controller according to a predetermined time interval.

[0017] Generally, in another aspect, a method of illuminating a horticultural environment is provided. The method includes providing a non-integrated illuminator. The non-integrated illuminator includes one or more light sources for providing illumination based on one or more lighting attributes and influencing one or more environmental parameters of the horticultural environment. The non-integrated illuminator also includes a controller communicatively coupled to one or more sensors configured to measure one or more environmental parameters. The one or more sensors include at least one of an environmental temperature sensor configured to measure environmental temperature data or a plant temperature sensor configured to measure plant temperature data.

[0018] The method further includes measuring one or more environmental parameters of the horticultural environment via the one or more sensors.

[0019] The method further includes generating, via the controller, an optimized lighting recipe based on the measured environmental parameters and an optimization model. The optimization model is configured to calculate a weighted average of differences based on the measured environmental parameters recorded during a modification of one or more lighting attributes.

[0020] The method further includes adjusting, via the controller, one or more lighting attributes according to the optimized lighting recipe.

[0021] In various implementations, a processor or controller may be associated with one or more storage media (commonly referred to herein as "memory", such as volatile and non-volatile computer memory, such as ROM, RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tapes, flash memories, OTP-ROMs, SSDs, HDDs, etc.). In some implementations, the storage media may be encoded with one or more programs that, when executed on one or more processors and / or controllers, perform at least some of the functions discussed herein. The various storage media may be fixed within the processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into the processor or controller to implement the various aspects discussed herein. The term "program" or "computer program" is used herein in a general sense to refer to any type of computer code (e.g., software or microcode) that can be used to program one or more processors or controllers.

[0022] It should be understood that all combinations of the above concepts and additional concepts discussed in more detail below (as long as such concepts are not mutually inconsistent) are considered to be part of the inventive subject matter disclosed herein. In particular, all combinations of the claimed subject matter appearing at the end of this disclosure are considered to be part of the inventive subject matter disclosed herein. It should also be understood that terms explicitly used herein may also appear in any disclosure incorporated by reference, and should be given the meaning most consistent with the particular concepts disclosed herein.

[0023] Reference will be made to the (multiple) embodiments described below, and these and other aspects of the various embodiments will become apparent and be elucidated. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In the drawings, like reference numerals generally refer to the same parts throughout the different views. Additionally, the drawings are not necessarily to scale; instead, emphasis is generally placed on illustrating the principles of the various embodiments.

[0025] Figure 1 is an illustration of two non-integrated illuminators arranged in a horticultural environment according to aspects of the present disclosure.

[0026] Figure 2A is a diagram showing the temperature in a horticultural environment utilizing an integrated illuminator according to aspects of the present disclosure.

[0027] Figure 2B is a diagram showing the temperature in a horticultural environment utilizing a non-integrated illuminator according to aspects of the present disclosure.

[0028] Figure 3 is an isometric view of a ceiling light according to aspects of the present disclosure.

[0029] Figure 4 is a bottom view of a grille lamp according to aspects of the present disclosure.

[0030] Figure 5 is a block diagram of a non-integrated illuminator and associated equipment according to aspects of the present disclosure.

[0031] Figure 6 is a schematic diagram of a controller of a non-integrated illuminator according to aspects of the present disclosure.

[0032] Figure 7 is a flowchart of a method of illuminating a horticultural environment according to aspects of the present disclosure. Detailed Description

[0033] The present disclosure generally relates to self-learning, non-integrated illuminators and, more particularly, to self-learning, non-integrated illuminators configured to optimally illuminate a horticultural environment based on environmental parameters measured around the illuminator. The non-integrated illuminator includes one or more light sources to illuminate one or more plants (or plant canopies) within the horticultural environment. The non-integrated illuminator also includes a controller. The controller is communicatively coupled to a sensor configured to measure environmental parameters within the horticultural environment and under the influence of the light sources of the non-integrated illuminator. The controller then feeds the measured environmental parameters into an optimization model to generate an optimized lighting recipe. The optimization model is configured to calculate a weighted average of differences based on the measured environmental parameters recorded during modification of one or more lighting attributes. The optimized lighting recipe sets one or more lighting attributes of the non-integrated illuminator. The lighting attributes then define the lighting provided by the one or more light sources of the non-integrated illuminator. The optimization model can be a reinforcement machine learning model. The non-integrated illuminator can also be communicatively coupled to additional non-integrated illuminators within the horticultural environment to exchange information for further optimization.

[0034] Referring now to the drawings, Figure 1 is an illustration of two non-integrated illuminators 100, 200 disposed in a horticultural environment HE. The horticultural environment HE can be any enclosed area for growing plants P1 - P3, such as a greenhouse, a growing room, a growth chamber, or other indoor and / or enclosed agricultural space. The plants P1 - P3 can be any type of plant capable of growing in an indoor or enclosed environment. The upper edge of the group of plants P1 - P3 can be referred to as the plant canopy PC. These indoor and / or enclosed horticultural environments HE rely on the light generated by the light sources 102, 202 of the non-integrated illuminators 100, 200 to promote plant growth via photosynthesis. In some examples, these light sources 102, 202 can include one or more light-emitting diodes (LEDs) or high-pressure sodium (HPS) lamps. Thus, the non-integrated illuminators 100, 200 can be colloquially referred to as "grow lights".

[0035] The light generated by the non-integrated illuminators 100, 200 also generates a significant amount of unwanted heat within the horticultural environment HE. This heat must be offset by various internal systems of the horticultural environment HE, such as heating, ventilation, and air conditioning (HVAC) systems. Some estimates suggest that 50% (in the case of LEDs) to 62% (in the case of HPS lamps) of the heat generated by the grow lights may be trapped within the horticultural environment HE. This excess heat must be removed by the HVAC system to prevent harmful heat-related stress effects on the plants and can be considered a "cooling load" on the HVAC system. Additionally, asynchronous changes in the light-temperature regime within the horticultural environment HE can affect plant processes (such as metabolism) at the molecular level, while also affecting morphological traits (such as the root-to-shoot ratio). Furthermore, studies have shown that heat-related plant stress is most pronounced when variable temperature is combined with fixed light conditions. Therefore, growers should avoid applying a constant level of light in an environment with temperature fluctuations. Additionally, the energy required to remove excess heat from the horticultural environment HE results in higher energy costs and a larger carbon footprint.

[0036] Large agricultural operations typically utilize integrated systems where the HVAC system communicates with the grow lights (such as via a central controller) in order to reduce overall heat gain and sudden heat spikes while still providing sufficient light for plant growth. However, for home and small-scale growing operations, such integrated systems are often too expensive. In the case of small-scale agricultural operations, such as Figure 1 the horticultural environment HE, the non-integrated illuminators 100, 200 provide light to the plants P1 - P3 independently of the HVAC system (or other systems of the horticultural environment HE). Due to the legalization and / or decriminalization of cannabis across the United States, the greenhouse market is expected to reach 413 hectares by 2025, while the market for home and small growers is expected to reach 477 hectares in the same time frame. The non-integrated illuminators 100, 200 within these home and small-scale growing operations are often arranged to turn on the illuminators at full load without considering the effects on the HVAC cooling load and plant heat stress. The results of the integrated system compared to the non-integrated system are shown in Figure 2A and 2B are shown. Figure 2A The programmed temperature in the integrated growth system is compared with the measured temperature. As can be seen, the measured temperature closely follows the day and night set points of the programmed temperature with minimal variance. This can be achieved by coordinating aspects of the HVAC system and / or other systems with the expected increase in temperature due to the activation of the grow lights. Additionally, due to receiving rich and detailed information from various aspects of the system (such as individual subsystems or sensors), the integrated system can quickly adjust for deviations in temperature (and in some cases humidity). In contrast, Figure 2BThe figure shows the measured temperature in a non-integrated growth system. As can be clearly seen from Figure 2A and 2B , when the growth lamp is turned on or off, the temperature within the non-integrated growth system suffers rapid changes in temperature.

[0037] Therefore, Figure 1 the non-integrated illuminators 100, 200 provide a cheap (compared to a fully integrated system), plug-and-play, flexible solution to counteract Figure 2B the rapid temperature changes shown in Figure 1 . Although for illustrative purposes, Figure 3 the horticultural environment HE of Figure 4 includes two non-integrated illuminators 100, 200, any practical number of non-integrated illuminators 100, 200 can be used. The non-integrated illuminators 100, 200 can be any practical type of illuminator, such as Figure 1 the ceiling lights depicted in

[0038] The first non-integrated illuminator 100 includes a plurality of light sources 102 (such as LEDs) and one or more sensors 110. In Figure 1 the example, the sensors 110 embedded within the first non-integrated illuminator 100 are shown as an ambient temperature sensor 112 and a plant temperature sensor 116. The example first non-integrated illuminator 100 also includes fourteen light sources 102a-n. Any practical number, size, shape, or type of light source 102 can be used. The ambient temperature sensor 112 is configured to measure the ambient air temperature of the horticultural environment HE. Similarly, the plant temperature sensor 116 is configured to measure the temperature at the plants P1-P3. In some examples, the plant temperature sensor 116 can be a thermal camera, a single-pixel thermopile (SPT) sensor, or a multi-pixel thermopile (MPT) array.

[0039] While the example ambient temperature sensor 112 and plant temperature sensor 116 are depicted as being embedded within the first non-integrated illuminator 100, in other examples, sensors 112, 116 may be located external to the non-integrated illuminator 100. For example, the ambient temperature sensor 112 may be suspended from the ceiling of the horticultural environment HE, while the plant temperature sensor 116 may be arranged proximate to the plant P1. In these examples, the ambient temperature sensor 112 and / or the plant temperature sensor 116 may convey the measured data to the first non-integrated illuminator 100 via wired (such as power line) and / or wireless (such as Zigbee, Bluetooth, Wi-Fi, etc.) connections.

[0040] Figure 1 A light sensor 132 arranged proximate to the first plant P1 and the second plant P2 is also shown. The light sensor 132 is configured to measure the amount of light received by the plants P1, P2. In some examples, the light sensor 132 may be a multispectral camera configured for multiple bands, including the visible spectrum (including the red, green, blue (RGB) spectrum) and the near-infrared (NIR) spectrum. In other examples, the light sensor 132 may be a photosynthetically active radiation (PAR) sensor. The light sensor 132 is also configured to convey data to the first non-integrated illuminator 100 via wired (such as power line) and / or wireless (such as Zigbee, Bluetooth, Wi-Fi, etc.) connections. In further examples, particularly in the case of a multispectral camera, the light sensor 132 may be embedded within the first non-integrated illuminator 100. Since PAR sensors typically must be arranged near the area to be measured, PAR sensors will typically not be embedded within the first non-integrated illuminator 100. In further examples, the sensors may also include a humidity sensor. The humidity sensor may be configured to generate data related to vapor pressure deficit.

[0041] Similar to the first non-integrated illuminator 100, the second non-integrated illuminator 200 also includes a plurality of light sources 200 (such as LEDs) and one or more sensors 210. The second non-integrated illuminator 200 includes an ambient temperature sensor 212 and a light sensor 232, such as a multispectral camera. The second non-integrated illuminator 200 also receives data from an external plant temperature sensor 216 via wired (such as power line) and / or wireless (such as Zigbee, Bluetooth, Wi-Fi, etc.) connections.

[0042] Although the first and second non-integrated illuminators 100, 200 are not connected to various systems (such as HVAC systems) of the horticultural environment HE, they can be communicatively coupled to each other via wired (such as power line) and / or wireless (such as Zigbee, Bluetooth, Wi-Fi, etc.) connections. In this way, the first and second non-integrated illuminators 100, 200 can share information, such as data measured by various sensors 100, 200 and optimized lighting settings.

[0043] Figure 5 is a block diagram of the non-integrated illuminator 100 and related devices. As shown in this non-limiting example, among other components, the non-integrated illuminator 100 includes light sources 102a-c (such as individual LEDs or arrays or groups of LEDs), a controller 104, an embedded ambient temperature sensor 112, and a transceiver 195. The controller 104 includes a lighting recipe generator 142, a lighting attribute regulator 144, and a light source driver 146. The non-integrated illuminator 100 is also configured to receive information from a plant temperature sensor 116, a light sensor 132, a second non-integrated illuminator 200, and / or a user interface 300. As Figure 1 shown in the example of, the non-integrated illuminator 100 can be arranged within the horticultural environment HE to provide lighting to one or more plants P1-P3 and / or a plant canopy PC.

[0044] In Figure 5 the example of, the plant temperature sensor 116 and the light sensor 132 are wirelessly connected to the non-integrated illuminator 100 via the transceiver 195. Accordingly, the transceiver 195 receives plant temperature data 118 (measured and transmitted by the plant temperature sensor 116) and spectral data 134 (measured and transmitted by the light sensor 132). The transceiver 195 then conveys the plant temperature data 118 and the spectral data 134 to the lighting recipe generator 142 of the controller 104. The lighting recipe generator 142 also receives ambient temperature data 114 measured by the ambient temperature sensor 112 embedded within the non-integrated illuminator 100.

[0045] The lighting recipe generator 142 is configured to generate an optimized lighting recipe 120 for the non-integrated illuminator 100. The optimized lighting recipe 120 indicates one or more optimized lighting attributes to stimulate plant growth. In many cases, the optimized lighting recipe 120 balances the lighting output required to promote photosynthesis while limiting heat increase within the horticultural environment HE. The optimized lighting recipe 120 can include settings for an optimized spectrum, an optimized light intensity, an optimized spatial location, and / or an optimized light direction. These individual attributes will be explained in further detail below.

[0046] By taking one or more measured environmental parameters 106 of the horticultural environment HE (seeFigure 6 ) is fed into the optimization model 122 to generate an optimized lighting recipe 120. The environmental parameters 106 are measured by various sensors 110 within the horticultural environment HE and can include sensors 110 embedded within the non-integrated illuminator 100 as well as sensors 110 arranged external to the non-integrated illuminator 100. In Figure 5 an example, one or more of the environmental parameters 106 include environmental temperature data 114, plant temperature data 118, and spectral data 134. The environmental parameters 106 can be continuously measured by the sensors 110 and provided to the controller 104 according to a predetermined time interval 136. For example, the continuously measured environmental parameters 106 can be provided to the controller 104 every 30 seconds or any other suitable time interval. In some examples, and as will be further explained in detail below, the optimization model 122 can be a reinforcement learning model configured to determine a weighted average 140 of differences based on the measured environmental parameters 106.

[0047] The lighting recipe generator 142 provides the optimized lighting recipe 120 to the lighting property regulator 144. The lighting property regulator 144 is configured to regulate one or more lighting properties 108 of the non-integrated illuminator 100. In one example, the lighting properties can include one or more of the spectrum 124, light intensity 126, spatial location 128, and light direction 130. The spectrum 124 defines the wavelengths of light provided by the non-integrated illuminator 100. In some examples, the spectrum 124 can be set to delay or limit infrared wavelengths generated by the light source 102 during a defined lighting period, such as when the light source 102 is initially powered on to reduce stress on the plant canopy PC (see Figure 1 ) above. The light intensity 126 defines the brightness of the light provided by the non-integrated illuminator 100. In some examples, the light intensity 126 can vary across wavelengths in order to reduce heat buildup in the horticultural environment HE and stress observed on the plants P1 - P3. The spatial location 128 defines the intended destination location of the light generated by the non-integrated illuminator 100 (such as a specific volume of the plant canopy PC). The spatial location 128 can be controlled by activating or deactivating individual light sources 102 of the non-integrated illuminator 100. For example, if the non-integrated illuminator 100 is implemented as Figure 4 a grid lamp, the spatial location 128 can be controlled by turning individual light sources 102 on or off. The light direction 130 defines the direction of the light generated by each light source 102 of the non-integrated illuminator 100. For example, as Figure 3As shown in the example of, each light source 102 of the ceiling-mounted non-integrated illuminator 100 includes a plurality of LEDs. Each of the plurality of LEDs can be individually controlled (activated or deactivated) to generate a desired light direction 130. In some examples, the light source 102 can be a pixelated LED array, where each pixel of the LED array is individually controllable.

[0048] The adjusted lighting attribute 108 is provided to the light source driver 146. The light source driver 146 is a circuit configured to generate one or more drive signals 148 to power the light source 102 such that the light generated by the non-integrated illuminator 100 corresponds to the optimized lighting recipe 120. In Figure 5 the example of, the light source driver 146 generates three drive signals 148a-c, each corresponding to a respective light source 102a-c. In further examples, additional drive signals 148 can be generated for individual LEDs of the light source 102 for more detailed control.

[0049] In some examples, the non-integrated illuminator 100 is configured to receive a second optimized lighting recipe 202 from a second non-integrated illuminator 200 via a wired or wireless connection. In Figure 5 the example of, the second non-integrated illuminator 200 wirelessly transmits the second optimized lighting recipe 202, which is received by the non-integrated illuminator 200 via the transceiver 195. The transceiver 195 provides the second optimized lighting recipe 202 to the recipe generator 142. Then, the recipe generator 142 can use the second optimized lighting recipe 202 as part of the calculation to determine the optimized lighting recipe 120. In one example, the recipe generator 142 can generate an initial optimized lighting recipe 120 based on the measured sensor data 106 (ambient temperature data 114, plant temperature data 118, and / or spectral data 134), and then adjust the optimized lighting recipe 120 to be more closely related to the second optimized lighting recipe 202. In other examples, the second non-integrated illuminator 200 can provide other types of data, such as measured environmental parameters or lighting attributes.

[0050] In a further example using an optimized lighting recipe from other non-integrated illuminators, the non-integrated illuminator 100 can be in wireless communication with four other non-integrated illuminators 200, 300, 400, 500. At regular time intervals (such as five, ten, or fifteen minutes), the first non-integrated illuminator 100 generates a first optimized lighting recipe 120 based on environmental parameters 106 measured by a sensor 110 associated with the first non-integrated illuminator 100. As previously described, according to one example, the environmental parameters 106 can include ambient temperature data 114 or plant temperature data 116, and the first optimized lighting recipe 120 corresponds to a light intensity 126. Similarly, the other four non-integrated illuminators 200, 300, 400, 500 also determine optimized lighting recipes 202, 302, 402, 502. Before implementing the optimized lighting recipe 120 via the lighting property adjuster 144 and the light source driver 146, the non-integrated illuminator 100 receives the further optimized lighting recipes 202, 302, 402, 502 via the transceiver 195. Then, the lighting recipe generator 142 can adjust the optimized lighting recipe 120 based on the other optimized lighting recipes 202, 302, 402, 502. For example, the lighting recipe generator 142 can perform a majority vote across all optimized lighting recipes 120, 202, 302, 402, 502 to set the first optimized lighting recipe 120 as the most popular recipe across the entire system. In this way, anomalous and / or incorrect optimized lighting recipes can be effectively ignored by the first non-integrated illuminator 100. In another example, the lighting recipe generator 142 can calculate the average of all optimized lighting recipes 120, 202, 302, 402, 502 to set the first optimized lighting recipe 120 as the median recipe across the entire system. In either the majority vote or median example, certain lighting recipes that are considered more relevant to the first non-integrated illuminator 100 (such as the optimized lighting recipe 120 calculated by the first non-integrated illuminator and / or the optimized lighting recipes 202, 302, 402, 502 corresponding to non-integrated illuminators 200, 300, 400, 500 that are spatially close to the first non-integrated illuminator) can be given greater weight in the calculation. As long as the optimized lighting recipe 120 is updated to account for the other non-integrated illuminators 200, 300, 400, 500, the lighting property adjuster 144 and the light source driver 146 can apply the updated optimized lighting recipe 120 to the light sources 102a-c.

[0051] In some examples, the non-integrated illuminator 100 is configured to receive user feedback 138 from the user interface 300. The user feedback 138 may reflect the user's assessment of the impact of the current lighting regime on the health of plants P1 - P3 within the horticultural environment HE. The user feedback 138 may also reflect the user's assessment of the overall heat gain or cooling load within the horticultural environment HE. The user may input the user feedback 138 through any practical user interface 300 (such as a touch screen, buttons, microphone, etc.). The user interface 300 may be a component of a stand-alone computing device (such as a desktop computer, laptop computer, smart phone, tablet computer, etc.). In Figure 5 an example, the user feedback 138 is provided to the controller 104 via a wired connection. In other examples, the user feedback 138 is provided via a wireless connection facilitated by the transceiver 195. The lighting recipe generator 142 can then use the user feedback 138 as part of the calculation to determine the optimized lighting recipe 120. In this way, in addition to the measured environmental parameters 106, the optimized lighting recipe 120 will reflect the user's observations that may not be fully measurable by the sensors 110.

[0052] In one example, the optimized lighting recipe 120 is generated by implementing the optimization model 122 as a multi-armed bandit (MAB) problem. In this example, the MAB problem is implemented by setting a number of agents (N), a number of actions (K), and a time step size (t) to quantify the reward for action k. In this example, the agents represent the non-integrated illuminators 100 in the system, and the actions represent changes in the lighting attributes 108. In this particular example, the action refers to reducing the light intensity 126 by dimming, although other examples may change one or more other lighting attributes 108 (such as the spectrum 124, spatial position 128, and / or light direction 130). Thus, this example contemplates a horticultural environment HE having more than one non-integrated illuminator 100.

[0053] In this example, the optimization model 122 is initialized by performing each action (dimming step) at least once and capturing the results via sensors 110 (ambient temperature sensor 112, plant temperature sensor 116, light sensor 132, etc.) to determine the reward for each step. In this example, the time step size should be long enough such that the data captured by sensors 110 reflects the changes in dimming. For example, the time step size can be five minutes, ten minutes, or fifteen minutes. Further in this example, the reward is defined as a weighted average 140 based on the differences in plant temperature data 118 and cooling load (corresponding to ambient temperature data 114) for each action. The weighted average 140 of the differences includes or corresponds to the weighted average of the differences in the values of two or more measured environmental parameters 106 recorded during the modification of one or more lighting attributes 108. The weighted average of the difference values or the value determined by calculating the weighted average of the differences can include or correspond to the values averaged according to the weights pre-assigned to the respective environmental parameters 106. The cooling load represents the amount of energy required to remove excess heat from the horticultural environment HE (which can be expressed in joules or British thermal units). The amount of excess heat to be removed can be determined by comparing the ambient temperature with the desired temperature or temperature range for optimal growth in the horticultural environment HE. In other examples, the reward can also be calculated at least in part based on spectral data 134, vapor pressure deficit data, and / or photosynthesis efficiency. In still further examples, the reward can also be calculated at least in part based on user feedback 138. The weighted average 140 of the differences can be calculated as follows: First, determine the changes in plant temperature data 118 and cooling load from one action to the next; scale the calculated changes such that the changes in temperature and the changes in cooling load can be meaningfully averaged (e.g., scale both temperature and cooling load on a scale from 0 to 100, where a 1.5C change in plant temperature is scaled to 85 and a 985J change in cooling load is scaled to 45), and then average the scaled values according to predetermined weights. For example, if stable plant temperature data 118 is more important than stable cooling load, the change in plant temperature data can be assigned a greater weight than the cooling load. Generally, stable plant temperature data 118 and cooling load will result in a positive reward, while significant changes in plant temperature data and / or cooling load will result in a negative reward. These initialization steps can also be regarded as the "exploration" phase of the MAB problem.

[0054] In addition, an upper confidence bound (UCB) is determined for each dimming step. Generally, the UCB is a parameter considered by each player or agent when making their local decisions. The UCB is tuned for each problem based on the network structure and characteristics. In this example, the UCB reflects the confidence (or probability) that the associated dimming level will result in a reward determined at each other illuminator in the system by the first non-integrated illuminator 100.

[0055] Once initialized, each non-integrated illuminator 100 updates its dimming level at time step t. The best dimming level is selected based on adding (1) the potential estimated reward for each available dimming level and (2) the UCB associated with each available dimming level. The dimming level associated with the highest sum of the potential estimated reward and the UCB is selected as the best dimming level. Additionally, the reward associated with the current dimming level can be updated based on the measured environmental parameters in the same manner as the reward was determined during initialization. Updating the reward associated with the dimming level during implementation (also known as exploitation) improves the accuracy of the optimization over time.

[0056] In addition, before implementing the best dimming level at each time step t, one of the non-integrated illuminators 100 receives dimming level information from all the other non-integrated illuminators (via wired or wireless transmission). The non-integrated illuminator 100 can then update its best dimming level based on the dimming levels from the other non-integrated illuminators. For example, the non-integrated illuminator 100 can perform a majority vote of the dimming levels and select the most popular dimming level to implement. Additionally, the non-integrated illuminator 100 can average all the dimming levels to implement a median dimming level. In these examples, in the majority vote or median calculation, certain dimming levels (such as the dimming level calculated by the non-integrated illuminator 100 itself or the dimming level associated with an illuminator spatially close to the non-integrated illuminator) can be weighted more than other dimming levels. Additionally, the non-integrated illuminator 100 can also share its best dimming level with the other non-integrated illuminators in the system. Once the best dimming level has been updated or corrected via majority vote or averaging, the non-integrated illuminator 100 implements the updated or corrected dimming level. These steps are repeated at each time step t until the reward has been maximized, thus achieving the optimized lighting recipe 120.

[0057] Typically, the MAB problem is an example of reinforcement learning where an agent needs to make optimized actions while still learning the outcomes. Examples of the MAB problem can be observed in real-world situations such as global positioning system (GPS) path planning, website advertising placement, etc., especially in games and online applications. In the MAB problem, the agent must select a sequence of actions to maximize its total reward. At the start of the process, the agent has no knowledge at all about the rewards for each action and needs to learn through its actions ("exploration"). In the later stages, the agent has learned about the rewarding actions and it must exploit this knowledge when various situations arise ("exploitation"). There are several solutions available for the MAB problem and each solution differs in how they arbitrate between "exploration" and "exploitation" behaviors during problem-solving. In the above example initialization, each non-integrated illuminator explores each dimming level from 0 to 100%. In other examples, each non-integrated illuminator can explore only a portion of the dimming levels and rely on data shared by other non-integrated illuminators to learn about the rewards associated with other dimming levels. In this example, the first non-integrated illuminator can explore dimming levels 0 to 50%, while the second non-integrated illuminator can explore dimming levels 60% to 100%. In some examples, the initial dimming level for each of the non-integrated illuminators can correspond to a randomly assigned value. For example, the first non-integrated illuminator can be randomly set to a dimming level of 20%, while the second non-integrated illuminator can be randomly set to a dimming level of 60%.

[0058] In this example, the light-temperature regime in the horticultural environment HE is modeled as a multi-agent multi-armed bandit problem in a multi-agent network, where N agents (non-integrated illuminators 100) sequentially select actions from a finite number K of actions (dimming steps). The N agents also share knowledge with other non-integrated illuminators 100. Each agent must strive to find an action with a better reward than what is known in the network of non-integrated illuminators 100. Since each agent can only observe its own reward, the agents must pass their knowledge across the network to collaboratively estimate the true reward. Continuing with this example, finding the optimal dimming setting for each non-integrated illuminator 100 in the growing chamber is equivalent to minimizing the overall regret (R T ) of the illuminator network according to the following equation:

[0059]

[0060] R T represents the regret at a given point in time t. Typically, regret is the difference between the expected outcome (such as a specific temperature) and the measured outcome. In Equation 1, μ is the expected value of the random variable, is the expectation operator, t ∈ [t] is the moment when arm k is pulled, It is a random variable representing the arm selected by player i at time t, Δ k,m is the expected reward value gap for actions (dimming steps) k and m, K is a finite number of actions (in this case, 20 dimming steps, where each step represents a 5% incremental dimming step from 0% to 100% of the light output), and n T (k) is the number of times action k (dimming step) has been selected by the network for majority voting up to time t. The distributed upper confidence bound estimation reward algorithm is used to perform regret (R T ) minimization.

[0061] Figure 6 Schematically illustrates the controller 104 of the non-integrated illuminator 100 as depicted in Figure 5 . As illustrated, the controller 104 includes a processor 125 and a memory 175. The memory 175 may be configured to store a plurality of environmental parameters 106 measured by sensors 110 (see Figure 1 ), including ambient temperature data 114, plant temperature data 118, and spatial data 134. The memory 175 may also store a predetermined time interval 136, which represents the timing at which measurements collected by the sensors 110 are provided to the controller 104. The memory 175 may also be configured to store user feedback 138 received via the user interface 300 (see Figure 5 ) and a second optimized lighting recipe 202 received from a second non-integrated illuminator 200 (see Figure 5 ). The processor 125 executes a lighting recipe generator 142 to generate an optimized lighting recipe 120 by processing the environmental parameters 106 through an optimization model 122. The recipe generator 142 may also take into account the user feedback 138 and / or the second optimized lighting recipe 202 to generate the optimized lighting recipe 120. The processor 125 then executes a lighting property regulator 142 to adjust one or more lighting properties 108 stored in the memory 175 based on the optimized lighting recipe 120. The processor 125 then executes a light source driver 146 to generate a driver signal 148 for each light source 102 (see Figure 5 ) based on the adjusted lighting properties 108. Then, the controller 104 provides the driver signal 148 to the light source 102, causing the non-integrated illuminator 100 to illuminate according to the optimized lighting recipe 120. When new environmental parameters 106 are measured at the next timestamp or during the next time interval, the process may start again, and a new optimized lighting recipe 120 may be generated.

[0062] Figure 7Illustrated is a method 900 for illuminating a horticultural environment using one or more non-integrated illuminators. Method 900 includes providing 902 one or more non-integrated illuminators. The non-integrated illuminator includes one or more light sources to provide illumination and affect one or more environmental parameters of the horticultural environment based on one or more illumination attributes of the illumination. The non-integrated illuminator further includes a controller communicatively coupled to one or more sensors configured to measure one or more environmental parameters. The one or more sensors include at least one of an environmental temperature sensor configured to measure environmental temperature data or a plant temperature sensor configured to measure plant temperature data. The one or more sensors may further include a light sensor configured to measure spectral data.

[0063] Method 900 further includes measuring 904 one or more environmental parameters of the horticultural environment via the one or more sensors. Method 900 further includes generating 906 an optimized lighting recipe via the controller based on the measured environmental parameters and an optimization model. The optimization model is configured to calculate a weighted average of differences based on the measured environmental parameters recorded during a modification of one or more illumination attributes. The optimization model may be a reinforcement learning model. Method 900 further includes adjusting 908 one or more illumination attributes via the controller according to the optimized lighting recipe.

[0064] All definitions as defined and used herein shall be understood to control dictionary definitions, definitions in documents incorporated by reference, and / or the ordinary meaning of the defined terms.

[0065] Unless expressly stated to the contrary, as used herein in the specification and claims, the indefinite articles "a" and "an" shall be understood to mean "at least one".

[0066] As used herein in the specification and claims, the phrase "and / or" shall be understood to mean "either or both" of the elements so conjoined, i.e., elements that are present conjunctively in some cases and disjunctively in other cases. Multiple elements listed with "and / or" shall be construed in the same manner, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present aside from the elements specifically identified by the "and / or" clause, whether related or unrelated to those specifically identified.

[0067] As used herein in the specification and claims, "or" shall be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted inclusively, i.e., including at least one of several elements or of a list of elements, but also including more than one, and optionally additional unlisted items. Only terms that explicitly state the contrary, such as "only one of... " or "exactly one of... ", or when used in the claims, "consisting of... " will refer to including exactly one element of several elements or of a list of elements. In general, the term "or" as used herein shall only be interpreted to mean exclusive alternatives (i.e., "one or the other, but not both") when preceded by an exclusive term such as "either", "one of... ", "only one of... " or "exactly one of... ".

[0068] As used herein in the specification and claims, the phrase "at least one" in reference to a list of one or more elements shall be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each specifically listed element in the list of elements, and not excluding any combinations of elements in the list of elements. This definition also allows that optionally there may be additional elements, whether related or unrelated to those specifically identified in the list of elements to which the phrase "at least one" refers.

[0069] It should also be understood that, unless explicitly indicated to the contrary, in any method claimed herein that includes more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.

[0070] In the claims as well as in the specification above, all transitional phrases such as "comprising", "including", "carrying", "having", "containing", "involving", "holding", "composed of", etc., are to be understood to be open-ended, i.e., meaning including but not limited to. Only the transitional phrases "consisting of... " and "consisting essentially of... " shall be closed or semi-closed transitional phrases, respectively. The above examples of the subject matter described can be implemented in any of a variety of ways. For example, some aspects can be implemented using hardware, software, or a combination thereof. When any aspect is implemented at least in part in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single device or computer or distributed among multiple devices / computers.

[0071] The present disclosure may be implemented as a system, method, and / or computer program product at any possible level of integration of technical details. The computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions thereon for causing a processor to perform aspects of the present disclosure.

[0072] A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed to be a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0073] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or may be downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The network may include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the corresponding computing / processing device.

[0074] The computer-readable program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network connection, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some examples, an electronic circuit including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may execute the computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit to perform aspects of the present disclosure.

[0075] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to examples of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0076] The computer-readable program instructions may be provided to a processor of a special-purpose computer or other programmable data processing apparatus to produce a machine, such that the instructions executed via the processor of the computer or other programmable data processing apparatus create a means for implementing the functions / actions specified in the flowchart and / or one or more block diagrams. These computer-readable program instructions may also be stored in a computer-readable storage medium, which may direct a computer, a programmable data processing apparatus, and / or other devices to operate in a particular manner, such that the computer-readable storage medium storing the instructions includes an article of manufacture containing instructions for implementing aspects of the functions / actions specified in the flowchart and / or block diagram or blocks.

[0077] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other devices implement the functions / actions specified in the flowchart and / or block diagram blocks.

[0078] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various examples of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified (multiple) logical functions. In some alternative implementations, the functions described in the blocks may not occur in the order described in the figures. For example, in fact, two consecutive blocks shown may be executed substantially simultaneously, or these blocks may sometimes be executed in the reverse order, depending on the functions involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or actions or a combination of dedicated hardware and computer instructions.

[0079] Other implementations are within the scope of the following claims and other claims to which the applicant may be entitled.

[0080] Although various examples have been described and illustrated herein, those of ordinary skill in the art will readily conceive of various other devices and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and each such variation and / or modification is considered to be within the scope of the examples described herein. More generally, those skilled in the art will readily understand that all parameters, dimensions, materials, and configurations described herein are intended to be exemplary, and the actual parameters, dimensions, materials, and / or configurations will depend on the specific application(s) for which the teachings are used. Those skilled in the art will recognize or be able to use, without more than routine experimentation, many equivalents to the specific examples described herein. Accordingly, it should be understood that the foregoing examples are presented by way of example only, and that the examples may be practiced in a different manner than specifically described and claimed within the scope of the appended claims and their equivalents. The examples of the present disclosure are directed to each individual feature, system, article, material, kit, and / or method described herein. Moreover, any combination of two or more such features, systems, articles, materials, kits, and / or methods (if such features, systems, articles, materials, kits, and / or methods are not mutually inconsistent) is included within the scope of the present disclosure.

Claims

1. A non-integrated illuminator (100) arranged in a horticultural environment (HE), comprising: One or more light sources (102) configured to provide illumination based on one or more lighting attributes (108) and affect one or more environmental parameters (106) of the horticultural environment (HE); And A controller (104) communicatively coupled to one or more sensors (110) configured to measure the one or more environmental parameters (106), the one or more sensors (110) including at least one of an environmental temperature sensor (112) configured to measure environmental temperature data (114) or a plant temperature sensor (116) configured to measure plant temperature data (118), the controller (104) being configured to: Generate an optimized lighting recipe (120) based on the measured environmental parameters (106) and an optimization model (122), wherein the optimization model (112) is configured to calculate a weighted average (140) of differences in values of two or more of the measured environmental parameters (106) recorded during a modification of the one or more lighting attributes (108), wherein the weighted average of the difference values is averaged according to weights pre-assigned to the respective measured environmental parameters; and Adjust the one or more lighting attributes (108) according to the optimized lighting recipe (120).

2. The non-integrated illuminator (100) according to claim 1, wherein at least one of the one or more light sources (102) is a light-emitting diode (LED) or a high-pressure sodium (HPS) lamp.

3. The non-integrated illuminator (100) according to claim 1, wherein the one or more lighting attributes (108) include a spectrum (124), a light intensity (126), a spatial position (128), and / or a light direction (130).

4. The non-integrated illuminator (100) according to claim 3, wherein at least one of the one or more lighting attributes (108) is wavelength-dependent.

5. The non-integrated illuminator (100) according to claim 1, wherein the plant temperature sensor (116) is a thermal camera, a single-pixel thermopile sensor, or a multi-pixel thermopile array.

6. The non-integrated illuminator (100) according to claim 1, wherein the optimization model (122) is a reinforcement learning model.

7. The non-integrated illuminator (100) according to claim 1, wherein the one or more sensors (110) include a light sensor (132) configured to measure spectral data (134).

8. The non-integrated illuminator (100) according to claim 7, wherein the light sensor (132) is a multi-spectral camera or a photosynthetically active radiation (PAR) sensor.

9. The non-integrated illuminator (100) according to claim 1, wherein the controller (104) is further configured to receive a second optimized lighting recipe (202) from a second non-integrated illuminator (200) via a wired or wireless connection.

10. The non-integrated illuminator (100) according to claim 9, wherein the adjustment of the one or more lighting attributes (108) is further based on the second optimized lighting recipe (202).

11. The non-integrated illuminator (100) according to claim 9, wherein the controller (104) is further configured to transmit the optimized lighting recipe (120) to the second non-integrated illuminator (202) via the wired or wireless connection.

12. The non-integrated illuminator (100) according to claim 1, wherein the non-integrated illuminator (100) is a ceiling light or a grid light.

13. The non-integrated illuminator (100) according to claim 1, wherein the one or more environmental parameters (106) are provided to the controller (104) according to a predetermined time interval (136).

14. The non-integrated illuminator (100) according to claim 1, wherein the optimized lighting recipe (120) is further generated based on user feedback (138) received by the controller (104).

15. A method (900) of illuminating a horticultural environment, comprising: providing (902) a non-integrated illuminator, wherein the non-integrated illuminator comprises: one or more light sources for providing illumination based on one or more lighting attributes and influencing one or more environmental parameters of the horticultural environment; and a controller communicatively coupled to one or more sensors configured to measure the one or more environmental parameters, the one or more sensors including at least one of an environmental temperature sensor configured to measure environmental temperature data or a plant temperature sensor configured to measure plant temperature data; measuring (904) the one or more environmental parameters of the horticultural environment via the one or more sensors; generating (906) an optimized lighting recipe via the controller based on the measured environmental parameters and an optimization model, wherein the optimization model is configured to calculate a weighted average of differences in values of two or more of the measured environmental parameters recorded during a modification of the one or more lighting attributes, and wherein the weighted average of the difference values is averaged according to weights pre-assigned to the respective measured environmental parameters; and adjusting (908) the one or more lighting attributes via the controller according to the optimized lighting recipe.

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