Identification of paddy varieties based on novel seed angle features

• Explore a novel feature extraction method.
• Find whether the proposed features have high dominant power.
• Proposed features gave an accuracy of 97.6% as compared to Color–Shape–Texture.

The purpose of this article was to explore a new feature extraction method for classifying paddy seeds using a feature extraction algorithm to achieve the Horizontal–Vertical and Front–Rear angles. The method used fusion of angle features for classification, which were then compared to features such as seed color, shape, and texture. Experiments show that the proposed features work better in classifying paddy seeds in comparison with some of the standard features, and that the proposed features have an excellent discriminating property for seeds. The discriminating power of these features was assessed using the neural network architectures for the unique identification of seeds of four Paddy (Rice) grains: viz. Karjat-6(K6), Karjat-2(K2), Ratnagiri-4(R4) and Ratnagiri-24(R24). The classification accuracies of Color–Shape–Texture obtained was 95.2% while the proposed method gave an accuracy of 97.6%.