Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System
Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System
Israr Hussain, Qianhua He and Zhuliang Chen
South China University of Technology, P. R.China
ABSTRACT
Automatically fruit recognition by using machine vision is considered as challenging task due to similarities
between various types of fruits and external environmental changes e-g lighting. In this paper, fruit
recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the
previous techniques have some limitations because they were examined and evaluated under limited
dataset, furthermore they have not considered external environmental changes. Another major contribution
in this paper is that we established fruit images database having 15 different categories comprising of
44406 images which were collected within a period of 6 months by keeping in view the limitations of
existing dataset under different real-world conditions. Images were directly used as input to DCNN for
training and recognition without extracting features, besides this DCNN learn optimal features from
images through adaptation process. The final decision was totally based on a fusion of all regional
classification using probability mechanism. Experimental results exhibit that the proposed approach have
efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also
effectively meet real world application requirements.
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