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The prediction and validation of greenhouse tomato yield using mathematical models and expert systems

University of British Columbia

Greenhouse tomato yields were predicted using two mathematical models developed in this study - the empirical and deterministic models. Weekly yield predictions for an entire growing season were compared with actual yields and results from an expert system model developed by the Agassiz Research Station. The deterministic math model involved using first principle equations of photosynthesis and respiration to simulate crop growth. Utilizing a known tomato yield conversion factor, net photosynthesis rates (Pnet) were converted to weekly yield predictions and compared with actual recorded yields. A deterministic model using two week cumulations of Pnet converted to yield was used successfully to predict actual tomato yields 6 weeks ahead of time with a root-mean-square-error (RMSE) of 0.38 kg/m2. The empirical math model employed regression techniques to fit historical greenhouse climate data to recorded yields. Correlations between light, temperature, and weekly tomato yields were derived into equations to predict yields for future growing seasons. An empirical model cumulating 3, 6, and 9 weeks of light and temperature data was developed to predict yields 4 weeks ahead of time with a RMSE of 0.45 kg/m2. When one-week-ahead predictions from the Agassiz expert system model were compared with actual recorded yields a RMSE of 0.401 kg/m2 was calculated. The expert system model utilizing trend recognition techniques was also used as a comparison with the two math models. When compared and ranked for prediction accuracy, application flexibility, and user-friendliness, the expert system was chosen as the overall best model for tomato yield prediction.

Thesis/Dissertation

application/pdf

2009-01-15

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http://hdl.handle.net/2429/3678

Chemical and Biological Engineering

University of British Columbia

UBCV

DSpace