Supervisors info:
Σάμης Τρέβεζας, Επικ. Καθηγητής, Μαθηματικών, ΕΚΠΑ
Summary:
This master thesis presents an introduction to hydroponic greenhouse irrigation management, focusing on applying functional-structural plant models for water consumption estimation and prediction. Hydroponic greenhouse cultivation has become increasingly popular in recent years due to its numerous advantages over traditional farming methods, such as greater efficiency in resource use, higher crop yields, and the ability to grow plants year-round. However, precise and effective irrigation management is critical for achieving optimal crop growth and yield in hydroponic greenhouses.
To address this issue, functional-structural plant models, including the GreenLab model, are investigated to estimate and predict water consumption in hydroponic greenhouses with a particular focus on "Ekstasis" Tomato, where data are available. Through this research, innovative concepts emerge, leading to fresh approaches in the modeling. These models are fitted via maximum likelihood for parameter estimation. The results demonstrate that the above models provide a more accurate and precise approach to irrigation management, with biological interpretation, which significantly improves water use efficiency.
The study also investigates the effects of various environmental and crop factors on water consumption in hydroponic greenhouses, such as temperature, humidity, light intensity, and plant growth stage. By incorporating these factors into the models, a more comprehensive understanding of the irrigation requirements of hydroponic crops is obtained, enabling more precise and efficient irrigation management.
Overall, this thesis contributes to the hydroponic greenhouse irrigation management field by providing a systematic and data-driven methodology that can be applied in practical settings. The findings of this study may offer helpful insights into the sustainable cultivation of hydroponic crops, particularly in light of current global climate change concerns.
Keywords:
Statistics, Modeling, Precision agriculture, Mathematics, GreenLab model
