Weed Seeds Classification Based on PCA, 2DPCA, Column-directional 2DPCA and (2D)2PCA

Weed seeds classification is significant to seed selection and weed prevention and control in both agriculture and grass industry. There have already been some classification methods based on image-processing which mainly deal with the seedling of weeds. We propose to classify weeds with their digital images by using PCA, 2DPCA, column-directional 2DPCA and (2D)²PCA. With 20 repeated experiments, we prove that applying these dimensionality-reduction algorithms to seed images processing is feasible and can achieve an ideal result. We evaluate these methods by both the average results and the optimal results from which we know (2D)2PCA is more efficient and accurate than others. 2DPCA and c2DPCA do not outperform PCA in our experiment but they need fewer dimensions to reach their respective highest recognition accuracy.