A Grading System For Fruits Maturity Using Neural Networks Approach

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Kestina Rai
Maitreyee Dutta, Sunil Aggarwal


Jatropha Curcas is a non edible oil crop predominantly used to produce bio-diesel. In addition to bio-diesel production, the by-product of Jatropha Curcas’ trans-esterification process can be used to make a wide range of products. Traditionally, human experts perform the identification of Jatropha curcas. Its quality depends on type and size of defects as well as skin color and fruit size. Then a Grading System of Jatropha (GSJ) by using color histogram method was developed to distinguish the level of ripeness of the fruits based on the color intensity. Although this automated approach was better than the human expert identification but it only deals with one aspect of the fruit, that is, color. In this paper we propose an artificial neural network approach to build an expert system to measure the ripeness of the fruit based not only on the color intensity but also on other features of the fruit like fruit size, shape of the fruit, texture, etc. because this type of a system can learn from examples like humans and can give better results.


Keywords: Artificial Neural Network, Back Propagation Network, Feedforward ANN, Pattern Recognition, Learning algorithms.


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