Abstract: This paper introduces a great approach to detection of fruits using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system using machine learning facts. The proposed system has applied convolutional neural network (CNN) to the tasks of detecting fruit images. Because of the wide diversity of types of fruit, image detection of fruit items is generally very difficult. However, deep learning has been shown recently to be a very powerful image detection technique, and CNN is a state-of-the-art approach to deep learning. A dataset is constructed that contains most frequent fruit items in a publicly available fruit-logging system, and used it to evaluate detection performance. CNN showed higher accuracy than did traditional support-vector-......
Key Words: visual fruit detection; deep convolutional neural network; real-time performance; rapid training; detection accuracy and graph.
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