Specific Objective of the Notice: To increase internationally recognized high-quality scientific
production, oriented towards smart specialization, aiming to stimulate a technology-based and
high-value-added economy, rationalizing and modernizing research, development, and
innovation infrastructure, prioritizing excellence, cooperation, and strengthening integration
into international research, development, and innovation networks.
The introduction of the smart plant growth chamber stands as a pivotal advancement in
precision agriculture, offering a compact and programmable system equipped with a digital
twin framework. This innovative solution has demonstrated its efficacy in conducting precise
experiments and optimizing plant growth conditions within a controlled environment. Despite
the promising features and advantages of the smart growth chamber, it is imperative to
acknowledge and address certain challenges, including delays caused by observed deviations
in the experimental processes.
The application of artificial intelligence, machine learning, and real-time monitoring has
undeniably improved the efficiency, replicability, and scalability of experiments. However, it is
crucial to recognize that the practical implementation of these cutting-edge technologies may
encounter occasional delays. Deviations in environmental parameters, unforeseen technical
issues, or the need for adjustments in the mapping system can contribute to disruptions in the
experimental timeline.
In the face of such challenges, it is essential to emphasize the adaptive nature of the smart
growth chamber. The feedback loop, involving data analysis and insights extraction, serves
not only to refine control parameters but also to address and mitigate delays resulting from
observed deviations. By understanding the complexities and potential disruptions in the
experimental process, researchers and farmers can proactively strategize and optimize their
approach.
The integration of a robust digital twin framework, mapping functions, and real-time monitoring
plays a crucial role in aligning the smart growth chamber with large-scale growing
environments. However, these components should be continuously refined and adapted to
minimize delays caused by deviations. Future iterations of the system should prioritize the
enhancement of these features to ensure seamless operation and reliable results.
While the smart growth chamber offers numerous advantages, including reduced
experimentation costs, improved experimental control, and AI-driven optimization,
acknowledging and actively addressing delays resulting from deviations is integral to its successful implementation. These challenges should be viewed not as obstacles but as
opportunities for refinement and innovation in the pursuit of advancing indoor cultivation
practices sustainably.
https://systec.fe.up.pt/snap |
