Zhaoyi Wu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Zhaoyi Wu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Didi Ma
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Jun Zhou
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Lei Yan
School of Technology Beijing Forestry University Beijing China
1 shared publication - Quancheng Liu
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Shuxiang Fan
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Yuxuan Ma
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms
Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification.
Open AccessClassification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms
Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification.