Views: 12 Author: ICEVER Publish Time: 2026-06-03 Origin: Site
AI thinking has found its way into nearly every industry. Recently, AC Infinity launched its AI powered monitoring system, and major manufacturers are now designing their own AI central controllers.
Technology tends to trickle down from industry leaders, so it makes sense to start with the bigger picture of commercial systems and management models, and then see how AI is being applied in agriculture against the backdrop of the global energy landscape.
Table of Contents
Right now, AI is bringing three major changes to agriculture.
AI shifts farm management from experience based growing to data driven growing. This transforms human factors, which are often unpredictable or inconsistent, into a digital SOP model.
AI makes monitoring far more precise, which helps prevent potential losses before they happen.
AI makes energy management significantly more effective.
In this article, as an LED product factory, we are naturally positioned to share our perspective on energy, light design, and system management.
The majority of the content is original; any AI-generated material has been clearly labeled or replaced with text and images.
In current agricultural operations, the main pain point is inefficient resource utilization. This issue has become even more pressing given the sharp rise in global energy and material prices.
Can nutrients be supplied on demand?
Can light be delivered according to growth stages?
Can temperature be kept dynamically stable?
A system comprising a suite of professional equipment provides the greenhouse with a highly stable environment.
In commercial growing over the years, these questions have been handled by automated control systems. These systems break all energy use down into three stages:
Sensing → Rulesg → Execution
Sensors digitize environmental conditions inside the greenhouse so they can be read and perceived, for example temperature, humidity, PPFD, light intensity, soil composition, and oxygen levels.
Once conditions are digitized, rules are applied to control each subsystem, such as humidifiers, central ventilation, shade screens, lighting systems, and irrigation.
To keep the logic stable, buffer conditions are added to prevent subsystems from bouncing on and off around a threshold, or early triggers are set to avoid losing control entirely.
A system built with professional equipment gives the greenhouse a remarkably stable environment. It enables year round cultivation and consistent product quality, truly shielding farming from the disruptions of weather.
So, what can AI do on top of this?
Each loop of sensing, rules, and execution looks perfect on paper, but a great deal of energy is still wasted in practice. Less sophisticated automation equipment struggles to factor in both the external environment and plant characteristics.
An air conditioner reads only the temperature sensor, ignoring humidity.
A humidifier reads only the humidity sensor, ignoring temperature.
An exhaust fan reads only the humidity threshold, ignoring how cold the outside air might be.
A variety of different sensors serve as the "eyes" of the greenhouse and measuring data.
On top of that, plant transpiration, sudden environmental shifts, and the way systems work against each other all lead to energy losses that are hard to quantify. This is where traditional systems fall short. They lack a key piece: prediction and calculation.
The ideal scenario would be this: the moment the lighting system, triggered by a light sensor, turns on, the system already predicts how much heat those fixtures will add to the environment.
In other words, the flow becomes:
Sensing → Rules → Prediction → Calculation → Execution
First, let's look at which forms of energy are wasted within the lighting system:
1. "It's way too hot in here!"
The last thing a lighting system needs is a vicious heat cycle and product damage when external conditions change. The first wastes energy, and the second leads to direct financial loss.
LEDs maintain high PPE at suitable temperatures, converting electrical energy efficiently into light. But under extreme temperatures, a vicious cycle sets in.
Current flows in, junction temperature rises, voltage drops, current spikes, joule heating surges, and the cycle repeats from the start.
CurrentInjection→JunctionTemp(TJ)Rises→Voltage(Vf)Drops→Current(I)Surges→JouleHeating(I2R)Spikes→LoopBackSo, the very first step to saving energy within the lighting system itself is to avoid heat problems. Once a thermal cycle takes hold, energy is wasted on a large scale.
2. "I really don't need this light much."
A plant's light needs shift dynamically through every growth stage. Traditional automation can handle mixed light calculations, balancing sunlight with supplemental light to keep the whole system stable across seasons. But the moment you introduce the variable of a plant's light saturation point, the calculation becomes far more difficult. Oversaturation not only reduces photosynthetic efficiency but also wastes energy.
If we want to solve energy problems within the lighting system, we need to address both of these areas. So, what can AI do here?
Whether in indoor farming or greenhouse growing, lighting is undoubtedly the largest energy consumer. AI powered models can enable the reuse of heat generated by light fixtures, while also bringing distinct advantages to the lighting system itself.
Several key factors define grow lighting today: spectrum, light intensity, and light uniformity.
Different plant varieties, different growth stages, and different cultivation goals all have different needs. In advanced agriculture, the challenge is how to balance these three dimensions while also accounting for external light conditions and commercial cultivation goals, so that light is truly delivered on demand.
Dynamic Light Adjustment in Agriculture
The concept of multi channel lighting for plants is gaining real traction in the market, and behind it lies a growing appreciation for the three dimensions mentioned above.
Getting these aspects right leads directly to strong results at every growth stage, higher final yields, and stable extract or trait profiles. High value crops, such as flowers, medicinal plants, and cannabis, benefit the most.
Tissue culture or seedling stage | Requirement: Low light intensity plus high blue light.
At this stage, the root system is delicate and cannot handle the high transpiration pressure caused by intense light, which risks dehydration and seedling death. A high proportion of blue light, around 20 to 30 percent, keeps the plant form compact and promotes root development, building a strong structural foundation for later growth. |
Vigorous vegetative growth stage | Requirement: medium to high light intensity plus a balanced spectrum, meaning a white light base with a red and blue combination.
The plant is rapidly growing stems and leaves and gaining height. PPFD needs to increase to meet the demands of photosynthesis, and the spectrum should include more red light to speed up biomass accumulation while maintaining a certain proportion of blue light to prevent excessive internode spacing. |
Reproductive growth, flowering, and fruiting stage | Requirement: Very high light intensity plus high red light plus far red light.
Forming fruits and flowers is highly energy intensive, so light intensity is often pushed to the plant's saturation point. Adding 730 nanometer far red light at this stage modulates phytochrome and triggers the shade avoidance response, which significantly accelerates the flowering process and improves fruit set. |
AI Note: Spectral analysis of the light requirements for most plants at various growth stages.
Different lighting and intensities are required throughout all stages of cultivation.
Beyond maintaining the basic growth curve, growers also need to guide the most commercially important outputs in a targeted way.
Increase Yield？
Ensuring fruit shape?
Maintaining commercial traits?
Preserving medicinal compound concentrations?
Improves Texture？
Achieving this kind of flexibility calls for multi channel lighting. However, developing new lighting parameters is costly and also pulls lighting away from its core role as an execution device. So, this capability needs to be developed at the controller level. Set it once, and it stays available.
By managing multi channel lighting based on a preset knowledge base, it becomes possible to simulate light plans tailored to a commercial model, and to sense time shifts and seasonal changes for dynamic adjustment.
An AI Neural Network Knowledge Base encompassing botany, spectral theory, pest identification, cultivation practices, sensors, and control systems.
What is needed going forward, then, is to build preset knowledge bases for cultivation models and to deepen the exploration of growing patterns. This might seem a little unsettling, as if it is a blunt replacement for the grower.
But it can never truly take responsibility. In this domain, AI remains a tool for reducing errors while giving growers a stronger grasp of the growing environment. Ultimately, a localized, small scale proprietary model can deliver more stable control and better energy utilization.
Several of our controllers already come with API interfaces, so customers can connect to the software layer whenever they are ready in the future.
What we have shared above is an honest look at current pain points, along with ideas gathered from conversations with some of our partner manufacturers about product iteration.
It is meant simply for exchange and discussion. Across the industry, hardware production and testing for intelligent systems is already underway, and on the software side, countries around the world are making their own progress.
Agriculture is the foundation of human survival. In the face of a shifting energy landscape, it must advance together with technology. The lighting manufacturer of the future will likely do much more than sell light fixtures. It will fully adapt to new cultivation models.
Multi channel core functions will operate more reliably.
Built in IC chips will receive and respond to control signals.
Better PCB designs will accommodate dynamic input.
Chips will become even more efficient.
Agricultural transformation requires progress across the entire industry on multiple fronts. ICEVER intends to keep strengthening its technical capabilities to meet whatever needs the future may bring.
And finally, a brief word about what we do. If you have any questions about lighting products and related controls, feel free to reach out through the info email on the right. We would also love to hear about the pain points in your current projects and any new ideas they might spark.
Where light grows, so does knowledge. Hope everyone is inspired by sharing!
