| heal.abstract |
This study arises from the question whether fuzzy logic is feasible for modeling crop growth processes. The article focuses on developing a fuzzy model to predict total photosynthesis (TotPHT) of tomato crop canopy. The fuzzy model uses qualitative relationships to describe the effects of temperature, carbon dioxide concentration, and light intensity in three canopy layers to determine TotPHT. The fuzzy model was tuned for Israel (fuzzy model 1) and Gainesville, Florida, (fuzzy model 2) conditions. The predictions of TotPHT by the fuzzy models compared well with computations from a tomato crop growth model (TOMGRO) for 16 days crop growth intervals with r2 values of 0.970 and 0.963 for models 1 and 2. Additionally, the predictions of the fuzzy model 2 when compared to experimental data from a controlled chamber study in Gainesville gave an r2 value of 0.947. These results indicate that fuzzy logic may provide another possibility to model crop processes. Fuzzy models can incorporate intuitive knowledge and can be developed in a relatively short time.This study arises from the question whether fuzzy logic is feasible for modeling crop growth processes. The article focuses on developing a fuzzy model to predict total photosynthesis (TotPHT) of tomato crop canopy. The fuzzy model uses qualitative relationships to describe the effects of temperature, carbon dioxide concentration, and light intensity in three canopy layers to determine TotPHT. The fuzzy model was tuned for Israel (fuzzy model 1) and Gainesville, Florida, (fuzzy model 2) conditions. The predictions of TotPHT by the fuzzy models compared well with computations from a tomato crop growth model (TOMGRO) for 16 days crop growth intervals with r2 values of 0.970 and 0.963 for models 1 and 2. Additionally, the predictions of the fuzzy model 2 when compared to experimental data from a controlled chamber study in Gainesville gave an r2 value of 0.947. These results indicate that fuzzy logic may provide another possibility to model crop processes. Fuzzy models can incorporate intuitive knowledge and can be developed in a relatively short time. |
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