An extended growth model for lactuca sativa under controlled water deficit conditions

• Extended dynamic growth model for lettuce cultivation • Integration of substrate water content dynamics • Validation across multiple substrate and irrigation conditions • High predictive accuracy for dry weight production • Decision-support tool potential for indoor farming optimization Indoor vertical farming offers sustainable food production with higher yields and lower resource use through complete environmental control and advanced management systems. However, these systems require accurate models, based on either data-driven approaches or fundamental principles. This paper focuses on the extension of a proposed dynamic growth model for lettuce ( Lactuca sativa ), one of the most commonly cultivated crops in indoor farms due to its short growth cycle. The extension integrates the substrate water content as a dynamic state variable to simulate the effects of water stress on biomass accumulation and links it to the original model through a water stress coefficient dependent on these dynamics. Validation was carried out using experimental data from a commercial indoor farm, where lettuce was grown on two substrates (peat and wood fiber) under two different irrigation regimes. Following sensitivity and collinearity analyses, the model was calibrated with respect to light use efficiency and substrate water content at wilting point parameters. The extended model demonstrated high accuracy in predicting dry weight production under all experimental conditions. These results highlight the model’s potential as a decision-support tool for optimizing irrigation strategies and substrate selection in indoor farming systems.

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Publication Details

Journal
Smart Agricultural Technology
Published
2025-11-24
DOI
https://doi.org/10.1016/j.atech.2025.101666
Primary Topic
Greenhouse Technology and Climate Control
Type
article
Field-Weighted Citation Impact
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article

An extended growth model for lactuca sativa under controlled water deficit conditions

Gianni Ferretti, Arianna Catenacci, Davide Marino, Alessandro Antona
Smart Agricultural Technology
Greenhouse Technology and Climate Control
article

An extended growth model for lactuca sativa under controlled water deficit conditions

Gianni Ferretti, Arianna Catenacci, Davide Marino, Alessandro Antona
article en

Abstract

• Extended dynamic growth model for lettuce cultivation • Integration of substrate water content dynamics • Validation across multiple substrate and irrigation conditions • High predictive accuracy for dry weight production • Decision-support tool potential for indoor farming optimization Indoor vertical farming offers sustainable food production with higher yields and lower resource use through complete environmental control and advanced management systems. However, these systems require accurate models, based on either data-driven approaches or fundamental principles. This paper focuses on the extension of a proposed dynamic growth model for lettuce ( Lactuca sativa ), one of the most commonly cultivated crops in indoor farms due to its short growth cycle. The extension integrates the substrate water content as a dynamic state variable to simulate the effects of water stress on biomass accumulation and links it to the original model through a water stress coefficient dependent on these dynamics. Validation was carried out using experimental data from a commercial indoor farm, where lettuce was grown on two substrates (peat and wood fiber) under two different irrigation regimes. Following sensitivity and collinearity analyses, the model was calibrated with respect to light use efficiency and substrate water content at wilting point parameters. The extended model demonstrated high accuracy in predicting dry weight production under all experimental conditions. These results highlight the model’s potential as a decision-support tool for optimizing irrigation strategies and substrate selection in indoor farming systems.

Smart Agricultural TechnologyVol. 13
Ospedale Sandro Pertini (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 74%
Greenhouse Technology and Climate Control
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