Fertigation Scheduling in Hydroponics System for Cucumber (Cucumis sativus L.) Using Artificial Neural Network and Genetic Algorithms

Abstract

A computer program for fertigation scheduling in a hydroponics system has been developed using Artificial Neural Network (ANN) and Genetic Algorithms (GA). The ANN model was used to establish the relationship between the environmental factors and outflow volume of fertigation in a hydroponics system for cucumber. The result showed that the predicted outflow volume agreed well with those of the measured values. The correlation coefficients (R2) between the predicted and measured values were 0.9673, 0.9432, and 0.8248 for vegetative, flowering and maturation stages, respectively. Optimum schedules for vegetative, flowering, and maturation stages were in a good coincidence at R2 of 0.8808 with the amount of fertigation required by the plants as calculated using the empirical method.