A Cyber-Physical System Approach for Precision Agriculture Focused on Crop Growth

This thesis presents a comprehensive study on the integration of cyber-physicalsystems (CPS) into hydroponic agriculture, focusing on the development of an advanced predictive model that employs machine learning techniques to optimize plant growth and resource management. As global demands for sustainable agriculture intensify, the need for efficient and precise farming methods becomes paramount. This research addresses this need by introducing a framework that monitors environmental factors and plant growth predictions non-destructively within controlled agricultural systems. The core of this study lies in the innovative application of real-time data acquisition combined with predictive analytics to create a dynamic model that adapts to varying conditions in hydroponic setups. Additionally, the thesis explores the practical implementation of sensor networks, providing a scalable solution that can be adapted across different scales of hydroponic farming. Key contributions of this research include the refinement of data collection methods in hydroponic systems, the development of a predictive model that integrates environmental and plant data. By bridging the gap between theoretical research and practical application, this study offers valuable insights into the future of agriculture, promoting more sustainable practices through the use of advanced technologies. This research not only advances the field of agricultural engineering but also serves as a vital resource for farmers, researchers, and policymakers interested in the future of sustainable farming. The findings suggest that the intelligent integration of CPS in agriculture could significantly enhance productivity and sustainability, marking a pivotal step toward the next generation of agricultural practices.