Explainable AI for Greenhouse Energy Efficiency and Crop Yields

Researchers are developing explainable AI frameworks to help greenhouse operators reduce energy consumption without sacrificing crop yields. By using deep learning models that prioritize transparency, growers can understand exactly how variables like radiation, temperature, and drainage influence their operations. The study demonstrates that different AI architectures are needed for different goals, with some models better at predicting slow biological growth and others more effective at forecasting immediate energy spikes. This approach moves agricultural AI away from black box predictions toward practical tools for decarbonizing high tech horticulture.