Abstract
Locality preserving projection (LPP) retains only partial information, and category information of samples is not considered, which causes misclassification of feature extraction. An improved locality preserving projection algorithm is proposed to optimize the extraction of growth characteristics. Firstly, preliminary dimensionality reduction of sample data is constructed by using two-dimensional principal component analysis (2DPCA) to retain the spatial information. Then, two optimized subgraphs are defined to describe the neighborhood relation between different categories of data. Finally, feature parameters set are obtained to extract local information of samples by improved LPP algorithm. The experiments show that the improved LPP algorithm has good adaptability, and the highest SVM classification accuracy rate of this method can reach more than 96%. Compared with other methods, the improved LPP has superior optimized performance in terms of multidimensional data analysis and optimization.
Highlights
Nowadays, precision agriculture has become a new trend in agricultural development both at home and abroad
The experiments show that the improved Locality preserving projection (LPP) algorithm has good adaptability, and the highest SVM classification accuracy rate of this method can reach more than 96%
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 pakchoi is chosen to act as test sample
Summary
Precision agriculture has become a new trend in agricultural development both at home and abroad It has higher requirements about the intelligence, real-time, and accuracy of monitor for greenhouse crops. Large amounts of data will be generated in the process of collecting and extracting crop growth characteristics. The high digits and the complexity of the data will cause difficulties in data processing, such as the great amount of calculation, the increased storage space, the interference, and noise. These factors above will have a bad influence on the accurate judgment for crops growth [1,2,3,4]. It is necessary to optimize the crop growth data collected by various methods
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