Greenhouse production of leafy greens, such as lettuce, offers significant advantages over traditional field farming, including reduced land and water use, higher yields, and improved crop quality

The Department of Agriculture National Institute of Food and Agriculture awarded $591,491 to Texas A&M Agrilife Research on February 15, 2026, under the Agriculture and Food Research Initiative (AFRI) program to develop an AI-assisted decision support system for controlled environment agriculture greenhouse production of leafy greens.

The system integrates edge computing, IoT-based sensing, computer vision, and predictive analytics to collect and analyze real-time data on greenhouse microclimate and crop growth. The platform will enable growers to make informed decisions about resource allocation—including adjustments to lighting, temperature, and nutrient delivery—to optimize production efficiency and reduce waste. The project includes controlled experiments to understand how different environmental conditions affect crop growth and quality, with findings used to calibrate AI models for forecasting crop performance. Deliverables include a user-friendly dashboard providing actionable insights for managing greenhouse operations and outreach activities including workshops, virtual tours, and training sessions to support grower and industry stakeholder adoption.

Work is performed at College Station, Texas, with a period of performance from February 15, 2026, through February 14, 2029. This assistance type is a Project Grant under CFDA 10.310. Texas A&M Agrilife Research, the research division of the Texas A&M University System, is the recipient. The funding addresses production challenges including high costs, labor shortages, and inefficient resource management by improving resource use efficiency and reducing costs to enhance the economic sustainability of controlled environment agriculture.

However, greenhouse operations face major challenges, including high production costs, labor shortages, and inefficient resource management. This project addresses these challenges by developing an AI-assisted decision support system that integrates edge computing, IOT-based sensing, computer vision, and predictive analytics. The system will collect and analyze real-time data on greenhouse microclimate and crop growth, enabling growers to make informed decisions about resource allocation, such as when to adjust lighting, temperature, or nutrient delivery, to optimize production efficiency and reduce waste. Controlled experiments will be conducted to understand how different environmental conditions affect crop growth and quality, and these findings will be used to calibrate AI models for forecasting crop performance. The project will deliver a user-friendly dashboard for growers, providing actionable insights for managing greenhouse operations. By improving resource use efficiency and reducing costs, this research will enhance the economic sustainability of CEA, create opportunities for technology adoption, and ultimately provide consumers with affordable, high-quality produce. Outreach activities, including workshops, virtual tours, and training sessions, will ensure that growers and industry stakeholders can adopt these innovations.