AI in Vertical Farming and Greenhouses

Controlled-environment agriculture covers two kinds of operations. Greenhouses use sunlight, supplemented by artificial light. Indoor vertical farms stack growing layers under LEDs inside a sealed building. In both, growers control temperature, humidity, carbon dioxide, light, irrigation and nutrients. AI's job is to choose settings that maximize yield and quality for each unit of energy and labor. Several variables interact: - **Light:** plants respond to the daily light integral (total light per day), photoperiod and spectrum. - **Vapor pressure deficit (VPD):** humidity and temperature together set the VPD, which drives transpiration. Too low and disease risk rises; too high and plants close their stomata. - **CO2:** enrichment raises photosynthesis only if light and temperature allow it. - **Nutrients:** solutions are managed by electrical conductivity (EC) and pH. Changing one setting shifts the others, so control is a balancing act that experienced growers take years to learn. Greenhouse AI has a notable proving ground: the Autonomous Greenhouse Challenge, run by Wageningen University & Research. Algorithm teams have competed against expert growers to grow crops such as cucumbers, tomatoes and lettuce remotely. The economics are the cautionary tale. AeroFarms and greenhouse grower AppHarvest both filed for bankruptcy in 2023, and Bowery Farming shut down in 2024. The core problem is that indoor farms replace free sunlight with purchased electricity, while their main products, leafy greens and herbs, sell at low prices. High capital costs, energy price spikes and tight margins left little room for error. Three misconceptions are common. AI does not make vertical farming profitable on its own; it trims costs at the margin while energy and capital dominate. Vertical farms are not automatically greener, because their carbon footprint depends on the electricity source. And greenhouses, not stacked indoor farms, produce most controlled-environment food.