Recognition of Edible Vegetables and Fruits for Smart Home Appliances

We present a state of the art method for vegetable and fruit recognition based on convolutional neural networks. We developed our solution around the concept of a smart kitchen/refrigerator equipped with an on-board camera. With this objective in mind, we adopted a dataset that was specifically collected and annotated according to the eating characteristics of the portrayed items. We performed two types of experiment: we first trained and evaluated different state-of-the-art neural architectures on the task of vegetable and fruit recognition. Secondly, we designed and tested a solution that exploits the hierarchical nature of such classes to further improve the final performance of our system. Experimental results demonstrate the quantitative superiority of the proposed solution compared to existing approaches.

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