Optimization method for crop growth features based on improved locality preserving projection

Abstract: Nowadays, the evaluation for crop growth is based on various growth characteristics, which often brings a huge amount of information processing. Furthermore, the complex information can not directly reflect some key features of crops. Thus, the feature extraction and optimization plays an important role in the process. In this paper, the locality preserving projection (LPP) is used to achieve the dimensionality reduction of high dimensional data while keeping the invariance of its internal local structure. After being projected via the algorithm, the adjacent sample is able to maintain the original neighboring state while the original distant samples don't keep the old state. Obviously, this result is not satisfactory for data optimization. In order to strengthen the effect of category separation, firstly, the dimension of sample data is preliminary reduced by using two-dimensional principal component analysis (2DPCA) to retain the spatial information. Secondly, two sub-graphs (gathered sub-graph and separated sub-graph) instead of original nearest neighbor graph are used to describe the relationship between homogeneous and heterogeneous data. The gathered sub-graph is improved on the basis of K-nearest neighbor graph. The addition of category information makes the homogeneous non-adjacent samples stay closer to each other after projection. The separated sub-graph is constructed to solve the problem that the application of K-nearest neighbor graph may reduce the accuracy of classification when the data are projected into low-dimensional space. Then the optimized global matrix and the improved objective function are provided to design the complete optimization method for feature extraction. Through the above steps, the category information of sample data is added for LPP algorithm. Finally, the feature parameters set are obtained by improved LPP algorithm to extract local information of samples. The data of crop growth features are further projected to low-dimensional space. The final extracted information is able to replace the original sample data without losing the data which can reflect the key information of sample set. In order to evaluate the performance of improved LPP algorithm to achieve dimensionality reduction and optimizing for crop growth characteristics, a set of data from cabbage was chosen as test sample. In the process of dimensionality reduction from 30 to 10 using different algorithms (PCA, 2DPCA, LPP and improved LPP), the improved LPP has higher overall performance with less running time, which is only longer than Basic LPP algorithm. By analyzing the performance of improved LPP algorithm for dimensionality reduction, the data of some cabbage and lettuce were chosen as test data. The contrast experiments using different algorithms (PCA, 2DPCA, LPP and improved LPP) for dimensionality reduction were carried out, and all the test data in the database achieved dimensionality reduction via the above-mentioned algorithms. Meanwhile, it accomplished data classification by SVM after accomplishing dimensionality reduction. The experiments show that the improved LPP algorithm has better adaptability, and the highest SVM classification accuracy rate of this method can reach up to 96%. Compared with other methods, the improved LPP has superior performances in terms of multidimensional data analysis and optimization. The method has good prospects, and is able to meet the demands for the information perception of new agriculture as well as the optimization of crop growth characteristic parameters.