A Neural Network Based Recognition and Classification of Commonly Used Indian Non Leafy Vegetables

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Ajit Danti 1 Manohar Madgi 2 Basavaraj S. Anami 2,*

1. JNN College of Engineering, Shimoga 577204, Karnataka, India

2. K.L.E. Institute of Technology, Hubli 580030, Karnataka, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2014.10.08

Received: 17 Apr. 2014 / Revised: 4 Jun. 2014 / Accepted: 17 Jul. 2014 / Published: 8 Sep. 2014

Index Terms

Machine vision, Digital image processing, Neural Networks classifier, Color features, Vegetable Recognition, Agricultural/horticultural produce


A methodology to characterize the commonly used Indian non-leafy vegetables’ images is developed. From the captured images of Indian non-leafy vegetables, color components, namely, RGB and HSV features are extracted, analyzed and classified. A feed forward backpropagation artificial neural network (BPNN) is used for the classification. The results show that it has good robustness and a very high success rate in the range of 96-100% for eight types of vegetables. The work finds usefulness in developing recognition system for super market, automatic vending, packing and grading of vegetables, food preparation and Agriculture Produce Market Committee (APMC).

Cite This Paper

Ajit Danti, Manohar Madgi, Basavaraj S. Anami,"A Neural Network Based Recognition and Classification of Commonly Used Indian Non Leafy Vegetables", IJIGSP, vol.6, no.10, pp. 62-68, 2014.DOI: 10.5815/ijigsp.2014.10.08


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