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To solve the problems occurred in the recognition of oil-tea camellia, such as the big error, lack of universality and much time consuming, the paper propose a new algorithm for fruit recognition, where Region Of Interest (RIO), Histogram of Oriented Gradients (HOG) temperature and Least Square Support Vector Machine (LS-SVM) are applied. First, the images are detected from HSV (hue, saturation, value) color information. The HOG temperature, calculated using four regions of interest (ROI), is input to an LS-SVM classifier, which detects the fruit. The performance of the model was verified by experiments. The vector sizes were effectively reduced and a higher detection speed was achieved without compromising accuracy (relative to conventional approaches). The detection accuracy can respectively achieve 95.5%, 89.4% and 96.7% for isolated fruit, overlapped fruit and background, which is shown the excellent performance of the proposed algorithm.

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