Fruit maturity detection using neural network and an odor sensor: Toward a quick detection

Maturity detection is very important for fruit farmhouses. In a previous study, we revealed a type of odor sensor that responds to the strength of the fruits smell as well as to the fruits maturities. The smell data consists of a dead time and a step response of a first-order lag element. We focus on the step response of first-order lag element, which is a form that rises exponentially to a constant value. This paper presents a quick detection method of fruit maturity in a few seconds of the rising signal of the odor sensor. Using neural network, the method performs without waiting for the sensor to fully reach up to a constant value. First, a neural network is trained for sample data with two kinds of maturities: fully ripe and immature. By testing the neural network with untrained data, we confirmed that the network is able to detect the fully-ripened, middle-ripened, and unripe fruits.