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A vegetable category recognition system: a comparison study for caffe and Chainer DNN frameworks

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Abstract

Deep neural network (DNN) has a deep hierarchy that connects multiple internal layers for feature detection and recognition. In our previous work, we proposed a vegetable recognition system which was based on Caffe framework. In this paper, we propose a vegetable category recognition system using DNN frameworks. We present a Vegeshop tool and website for users. Our system can be accessed ubiquitously from anywhere. We evaluate the performance of our vegetable category recognition using 15 kind of vegetables. Also, we evaluate the performance of learning accuracy and loss for vegetable recognition system which is based on Caffe and Chainer frameworks. In addition, we present the performance of recognition rate for different vegetables with different pixel sizes. The evaluation results show that the learning rate is more than 80%. We noticed that the performance of this recognition system is degraded when the color of the object is yellow. In this case, our system does not recognize the outline of the object by light intensity. From these studies, we found that the results of Caffe are higher than Chainer. For both frameworks, when pixel sizes is \(256\times 256\), the results of accuracy is increased rapidly with the increase in iterations.

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Correspondence to Makoto Ikeda.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Ikeda, M., Oda, T. & Barolli, L. A vegetable category recognition system: a comparison study for caffe and Chainer DNN frameworks. Soft Comput 23, 3129–3136 (2019). https://doi.org/10.1007/s00500-017-2959-y

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