Identification of Artificially Ripened Fruits Using Machine Learning

Abstract

Ripening of fruit is a natural process. Ethylene is responsible for ripening process which is produced naturally in fruit. But dealers and sellers often use chemicals like CaC2 (Calcium carbide) to quicken this process so their product will enter the market early and they can maximize profit. Fruits are kept in storage with chemicals. This chemical mixes with moisture and produces ethylene which causes ripening of the fruit. Since the ethylene is high in quantity and it contacts with the surface area of the fruit it causes uniform ripening of the fruit unlike when the fruit is ripened naturally it causes uneven ripening of the fruit since natural ethylene present in the fruits is un-uniformly distributed. Consuming such fruit is harmful to human health. It can cause headache, stomach irritation, throat irritation, digestion problem and since the chemical used is carcinogenic it can also cause cancer and it also degrades the taste and quality of the fruit. Finding out the artificially ripened fruit with the human eye is difficult. So, the proposed system gets an image of fruit under the test and compare it with the features of naturally ripened fruit and artificially ripened fruit and give the output with the probability. This method makes usage of smartphone which runs the android application and the convolutional neural network to detect the artificially ripened fruit.

Keywords: Tensorflow, Convolutional Neural Network (CNN), Android, Artificially Ripened Fruits