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Predicting lettuce canopy photosynthesis with statistical and neural network modelsAn artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).
Document ID
20040088963
Acquisition Source
Headquarters
Document Type
Reprint (Version printed in journal)
Authors
Frick, J.
(NASA Headquarters Washington, DC United States)
Precetti, C.
Mitchell, C. A.
Date Acquired
August 21, 2013
Publication Date
November 1, 1998
Publication Information
Publication: Journal of the American Society for Horticultural Science. American Society for Horticultural Science
Volume: 123
Issue: 6
ISSN: 0003-1062
Subject Category
Man/System Technology And Life Support
Funding Number(s)
CONTRACT_GRANT: NAGW-2329
Distribution Limits
Public
Copyright
Other
Keywords
Non-NASA Center
NASA Discipline Life Support Systems

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