In the heart of Italy, researchers are pioneering a new approach to vertical farming that could revolutionize urban agriculture and reshape the energy sector’s role in food production. Carlos Alejandro Perez Garcia, from the Department of Agricultural and Food Sciences at the University of Bologna, has developed a predictive model that could optimize indoor growing conditions, making vertical farming more efficient and sustainable.
Vertical farming, a method of growing crops in stacked layers, is gaining traction as a solution to urban food production challenges. However, maintaining ideal indoor conditions is a complex task that requires precise control of environmental parameters. One such critical parameter is the vapor pressure deficit (VPD), a measure of the difference between the amount of moisture in the air and how much moisture the air can hold when saturated. VPD is a key indicator of vegetation health and crop growth status.
Garcia’s research, published in the *Journal of Agricultural Engineering* (translated from Italian as “Giornale di Ingegneria Agricola”), focuses on predicting VPD using the NeuralProphet algorithm, an advanced artificial intelligence tool. The model utilizes environmental data such as temperature, relative humidity, and solar radiation to forecast VPD with remarkable accuracy.
“The model shows high accuracy and reliability,” Garcia explains, “with a root mean squared error (RMSE) of 34.80 and a mean absolute error (MAE) of 25.28.” This level of precision enables growers to optimize indoor conditions, improving resource use efficiency and minimizing operational costs.
The implications for the energy sector are significant. Vertical farming facilities often require substantial energy inputs to maintain optimal growing conditions. By accurately predicting VPD, growers can fine-tune their environmental controls, reducing energy consumption and operational expenses. “This model can help produce high-quality crops through precise control of environmental parameters,” Garcia notes, highlighting the potential for cost savings and improved crop yields.
Moreover, the research indicates a promising application of advanced AI tools in vertical farming management. As urban populations continue to grow, the demand for locally produced, high-quality food will increase. Vertical farming, with its space-saving and resource-efficient characteristics, is well-positioned to meet this demand. Garcia’s model could play a crucial role in making vertical farming more sustainable and economically feasible.
The research also opens up new avenues for future developments in the field. As Garcia points out, “The model’s satisfactory performance in predicting VPD enables optimization of indoor growth conditions, thereby improving resources use efficiency and minimizing operational costs.” This could lead to further innovations in vertical farming, such as the integration of renewable energy sources and the development of more sophisticated environmental control systems.
In conclusion, Garcia’s research represents a significant step forward in the field of vertical farming. By leveraging the power of AI, growers can achieve greater precision and efficiency in their operations, ultimately contributing to a more sustainable and resilient food system. As the world grapples with the challenges of urbanization and climate change, innovations like Garcia’s model offer a glimpse into the future of agriculture.