Hybrid Tree-Fuzzy Logic for Aquaponic Lettuce Growth Stage Classification Based on Canopy Texture Descriptors

Lettuce is one of the most popular crops for urban farming because it is easy to grow and it has high nutritional value. Moreover, it is adaptable and can be combined with other food options, or it can be eaten alone without too much preparation. Predicting lettuce growth can be crucial to find the optimum maturity and harvest time. This paper proposed to use a model of a hybrid tree-fuzzy logic approach, the classification tree was used to select the most significant features from the texture features then the fuzzy inference system was utilized in predicting the lettuce growth stage classification. The hybrid system produced accurate results with low percentage error and correct classifications. Based on these results, the most accurate prediction can be observed in the head development growth stage; the harvest growth stage has a slight variance, while the vegetative stage has the most variance. Overall, the trained hybrid system is reliable in predicting and identifying lettuce growth stage classification.