Design and fabrication of an intelligent control system for determination of watering time for turfgrass plant using computer vision system and artificial neural network

Abstract

The majority of the volume in a plant cell is water. Therefore, the growth and metabolism of plants are highly dependent on the changes in plant water content. To optimize plant growth in water limited and drought stress conditions, many mechanisms have been considered. The aim of this study was to design and develop an intelligent system, based on an Artificial Neural Network (ANN) and machine vision that would optimize plant growth in limited water situations. To this end, color, morphological and textural features were extracted from a set of turfgrass plant images under drought stress conditions and were analyzed to determine plant water requirement. To maximize classification accuracy, an optimum set of features [h (hsl color space), L (Lab color space), H (HSV color space) and PDF1 (Probability Density Functions)] were selected using a genetic algorithm. Then a data classification operation was conducted using an ANN. The classifier accuracy for three plant situations (fresh, at the edge of wilting and wilted) as well as its total accuracy were 91.3, 77.8, 97.9 and 90.7%, respectively. The automated irrigation system could measure and determine the plant wilting condition by investigation of four extracted features and then determine and apply the correct amount of water required for optimum plant growth in water limited situations.