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Multi-feature fusion improved recognition accuracy, and PCANet demonstrated considerable advantage in classification performance. The best result was achieved by PCANet with PCA-processed spectroscopic and texture features with correct classification rates of 98.66% and 98.57% for the training and prediction sets, respectively. In summary, the proposed method provides an accurate identification of rice variety and can be easily extended to the classification, attribution and grading of other agricultural products. The objective of this s
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