LEDs, IoT, and AI revolutionize indoor agriculture with efficiency and sustainability

The review identified four major technological domains that are collectively shaping artificial lighting in indoor agriculture. These include advanced lighting control systems, IoT-integrated sensor networks, simulation-based modeling platforms, and complementary renewable energy sources.

A comprehensive new review shed light on the transformative power of smart lighting systems in indoor agriculture, positioning light-emitting diode (LED) technologies, Internet of Things (IoT) platforms, and artificial intelligence as the core drivers of next-generation crop production. As global demand for food security intensifies amid shrinking arable land and unpredictable climates, these innovations offer a lifeline to Controlled Environment Agriculture (CEA), a method that allows crops to thrive indoors under meticulously managed conditions.

Published in Sustainability, the study “Technologies Applied to Artificial Lighting in Indoor Agriculture: A Review,” analyzes 70 academic publications between 2019 and 2024 and outlines the primary tools reshaping CEA.

What technologies are revolutionizing indoor crop lighting systems?

The review identified four major technological domains that are collectively shaping artificial lighting in indoor agriculture. These include advanced lighting control systems, IoT-integrated sensor networks, simulation-based modeling platforms, and complementary renewable energy sources.

LED systems emerged as the dominant lighting infrastructure due to their spectral tunability, low energy consumption, and extended operational lifespans. These devices allow precise regulation of light intensity, photoperiods, and spectral composition - key factors in maximizing plant yield and quality.

IoT, on the other hand, plays a pivotal role in enhancing real-time adaptability and automation. By integrating smart sensors and wireless communication protocols, such as Wi-Fi, ZigBee, and LoRaWAN, IoT systems enable continuous data collection and remote control of lighting parameters. These setups facilitate automated adjustments based on plant growth stage, environmental conditions, and energy efficiency goals.

Next up, artificial intelligence and machine learning techniques, ranging from Support Vector Regression to neural networks, are being applied to optimize light application and predict plant responses. Digital twin simulations and cyber-physical models offer predictive analytics to improve energy usage and maximize biomass output. Coupled with solar-powered sensors and hybrid systems that combine artificial and natural lighting, these technologies are reducing dependency on grid electricity while enabling high-precision control in enclosed farming environments.

How do these technologies improve productivity and energy efficiency in indoor farms?

Artificial lighting in CEA must strike a balance between energy consumption and photosynthetic efficiency. The review highlights that adaptive lighting systems, particularly those based on LEDs, can drastically reduce power usage while tailoring spectra to specific crop needs. For example, specific combinations of red and blue wavelengths have been found to improve photosynthesis and stimulate desired morphological traits in crops like lettuce, rocket, and bok choy. Supplemental ultraviolet and infrared lighting was also shown to enhance metabolite production and growth cycles.

Optimizing Photosynthetic Photon Flux Density (PPFD) and spatial light distribution further enables farmers to deliver precise lighting doses, minimizing waste and improving crop yield. IoT-based systems enhance this efficiency by automatically switching or dimming lights based on sensor inputs, reducing unnecessary energy expenditure. Platforms like Arduino and Raspberry Pi, often paired with cloud-based applications such as Blynk, support real-time decision-making and predictive control.

Simulation tools are also being used to refine lighting design before physical implementation. Models such as the U-chord curvature technique and ray-tracing simulations help calculate ideal canopy penetration and lighting angles. These solutions not only minimize trial-and-error but also reduce energy waste by pre-determining optimal conditions for plant development.

Hybrid systems that combine smart artificial lighting with solar energy sources, such as photovoltaic panels and automated greenhouse shading devices, were found to cut operational costs significantly. One study cited in the review demonstrated a 29% increase in energy efficiency by incorporating automated roller shades to regulate natural light use in solar greenhouses.

What are the main barriers to implementation and the priorities for future research?

Despite the technological progress, the review emphasizes that high energy consumption, complex system integration, and limited scalability remain major challenges for widespread adoption. Energy costs continue to be the largest operational burden in indoor farming. Even with advanced LEDs, maintaining ideal light, temperature, and humidity conditions demands significant electricity.

System complexity poses another hurdle. Integrating multiple technologies - sensors, control units, AI algorithms, and communication networks - requires interdisciplinary expertise and substantial capital investment. These barriers are particularly acute for small and medium-sized growers who lack the infrastructure or funding for full automation.

To address these issues, the researchers call for the development of modular, cost-effective lighting systems that can be scaled to smaller operations. Improved sensor calibration, machine learning models capable of continuous learning, and more robust communication protocols are also high-priority areas for innovation.

Emerging trends include the integration of biofeedback systems, adaptive LED arrays, and predictive analytics that use real-time crop data to dynamically adjust lighting strategies. The review also points to growing interest in combining environmental sustainability with smart agriculture, through efforts such as energy-neutral indoor farms powered entirely by renewables.

In a nutshell, no single technology or model offers a universal solution. Rather, the integration of diverse systems such as LEDs, IoT, AI, renewable energy, and simulation is the key to making indoor agriculture both efficient and sustainable.