Two recent studies-"High-Tunnel Temperature Forecasting with Machine Learning" and "Enhancing Solar Radiation Forecasting with Machine Learning"-demonstrate how advanced data-driven models can significantly improve the accuracy and reliability of short-term environmental predictions critical to greenhouse and high-tunnel management.
High tunnels and other protected cultivation systems provide growers with season extension and environmental control, but fluctuating temperature and solar radiation levels remain ongoing management challenges. Accurate forecasting enables growers to optimize ventilation, irrigation, shading, and heating strategies, reducing crop stress while improving resource efficiency.
The first study focused on improving temperature prediction within high tunnels using machine learning algorithms trained on historical environmental data. Researchers evaluated multiple modeling approaches to capture the complex interactions among ambient weather conditions, solar radiation, and internal tunnel microclimates. The models consistently outperformed conventional forecasting methods, offering more precise short-term temperature projections that can support real-time management decisions.
Reliable high-tunnel temperature forecasting helps growers anticipate heat stress, minimize frost damage, and improve energy efficiency. Enhanced predictive capability also supports automation systems that adjust ventilation or deploy shade cloth in response to expected conditions, reducing labor demands and operational uncertainty.
The companion study examined solar radiation forecasting, a critical factor influencing plant growth, evapotranspiration, and internal temperature dynamics in protected environments. By applying advanced machine learning techniques to meteorological datasets, researchers achieved improved accuracy in predicting incoming solar radiation compared to traditional statistical approaches.
Improved solar radiation forecasting provides growers with better insight into daily light availability, allowing more precise irrigation scheduling and crop management. Accurate radiation predictions also improve temperature modeling within high tunnels, since solar input is a primary driver of internal heat accumulation.
Together, the two studies underscore the potential of machine learning to transform environmental management in controlled and semi-controlled agricultural systems. By integrating high-resolution weather data with advanced modeling techniques, growers and researchers can move toward more responsive, automated, and data-informed production systems.
These findings highlight a broader shift toward digital agriculture tools that enhance sustainability, reduce risk, and improve productivity. As climate variability increases and labor constraints persist, machine learning-based forecasting systems offer a scalable solution to strengthen resilience in specialty crop production.
The research provides a foundation for continued development of intelligent forecasting tools that can be integrated into farm management platforms, ultimately helping growers make faster, more informed decisions in dynamic growing environments.
The full article can be read on the ASHS HortTech electronic Journal website at: https://doi.org/10.21273/HORTTECH05644-25 and https://doi.org/10.21273/HORTTECH05825-25
Established in 1903, the American Society for Horticultural Science is recognized around the world as one of the most respected and influential professional societies for horticultural scientists. ASHS is committed to promoting and encouraging national and international interest in scientific research and education in all branches of horticulture.
Comprised of thousands of members worldwide, ASHS represents a broad cross-section of the horticultural community-scientists, educators, students, landscape and turf managers, government, extension agents and industry professionals. ASHS members focus on practices and problems in horticulture: breeding, propagation, production and management, harvesting, handling and storage, processing, marketing and use of horticultural plants and products. To learn more, visit ashs.org.
