Irrigation Time Detection System (ITDS): Irrigation Scheduling Using Explicit Neural Network Modeling of Plant and Environmental Variables

Abstract

This study was conducted in four phases to construct models through them the plant water status can be estimated. These relations and models were implemented as a software and embed into a hardware device. The data were collected in the greenhouse and open field conditions during 2010-2015 and modeled by a multilayer feed forward neural network using MATLAB software. In the first phase, 7 variables including vapor pressure deficit, photosynthetically active radiation, wet bulb temperature, leaf temperature (Tl), air temperature (Ta), Ta and Tl difference (Ta-Tl) and relative humidity (RH) were measured for tomato and cucumber. High coefficient of determination (R) values (more than 0.7) was obtained in all fitted models when all variables are incorporated. However, starting from a complete set of variables, the result of stepwise backward elimination of variables showed that removing VPD, PAR, WbT and Tl has a trivial effect on R values. Therefore in the second phase, only 3 variables (Ta, Ta-Tl and RH) were measured to fit the model for 4 greenhouse and 10 open field crops. For each crop, the best model with the highest R value was obtained. These models were used to design an Irrigation Time Detection System (ITDS) in the third phase. The main parts of the system consist of an artificial neural network (ANN) module as the intelligent part of this system implemented on a cell phone coupled with a microcontroller based hardware device. Finally, in the fourth phase, ITDS was tested successfully on tomato and cucumber in the greenhouse.