Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision

Joko Siswantoro, Anton Satria Prabuwono, Azizi Abdullah, Bahari Indrus


Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood.


Kalman filter; linear model; natural produce; neural network; recognition.

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DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2017.11.2.5


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