Artificial Intelligence (AI) is disrupting industries by enabling computers to learn and perform tasks that once required human processing. In controlled environment agriculture (CEA), AI offers the potential to optimize crop management and environmental control for more efficient crop production, while some critical challenges remain for the full integration
of AI into the existing CEA production systems.
AI encompasses various technologies, with Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) being the driving forces (Figure 1). However, recent discussions around AI have predominantly focused on GenAI due to its ability to create new content such as text, images, and music by identifying patterns from extensive datasets available on the internet. Although the ability to generate realistic content is a significant milestone, GenAI is not intended for optimal process control and automation in physical environments.
This article focuses on ML, a broader and more foundational category within AI, which has widespread applications across various domains including CEA. We will provide a primer on ML, explain why it is transformative to the CEA industry, and highlight current challenges to effective ML solutions for efficient greenhouse control.
Read the full article at e-gro.org