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Articles by Zhu Wenjing
Total Records ( 2 ) for Zhu Wenjing
  Sun Jun , Jin Xiaming , Mao Hanping , Wu Xiaohong , Gao Hongyan , Zhu Wenjing and Liu Xiao
  This study was carried out to detect nitrogen content in lettuce leaves rapidly and non-destructively using visible and near infrared (VIS-NIR) hyperspectral imaging technology. Principal Component Analysis (PCA) was performed on the average spectra to reduce the spectral dimensionality and the principal components (PCs) were extracted as the input vectors of prediction models. Partial Least Square Regression (PLSR), Back Propagation Artificial Neural Network (BP-ANN), Extreme Learning Machine (ELM), Support Vector Machine Regression (SVR) were, respectively applied to relate the nitrogen content to the corresponding PCs to build the prediction models of nitrogen content. R2p of the PLSR model for nitrogen content was 0.91 and RMSEP was 0.32. BP model of structure 5-2-1 with R2p of 0.92 and RMSEP of 0.21, ELM model of structure 5-10-1 with R2p of 0.95 and RMSEP of 0.19 and SVR model for nitrogen with R2p of 0.96 and RMSEP of 0.18, all got good prediction performance. Compared with the other three models, SVR model has the better performance for predicting nitrogen content in lettuce leaves. This work demonstrated that the hyperspectral imaging technique coupled with PCA-SVR exhibits a considerable promise for nondestructive detection of nitrogen content in lettuce leaves.
  Sun Jun , Jiang Shuying , Mao Hanping , Zhang Xiaodong , Zhu Wenjing and Wang Yan
  The feature extraction and optimization of lettuce leaf image are the important premise of classification recognition of lettuce nitrogen levels. The lettuce samples of different nitrogen levels were cultivated in soilless cultivation using nitrogen nutrition of different concentrations. When the lettuce leaf images were collected, image features have been extracted, including texture features, shape features and color features. Because of the redundancy of characteristic values, there were influences in the accuracy and efficiency of image recognition. Genetic algorithm was used to optimize 11 eigenvalues and the Principal Component Analysis (PCA) dimension reduction method was used to choose 12 principal component feature values whose cumulative contribution rate reached 98.24%. Later, the Support Vector Machine (SVM) was used as classifier. The 90 samples were chosen as training samples and the remaining 30 samples were chosen as the test samples. The result shows that, the prediction accuracy of SVM classifier based on genetic algorithm feature optimization reaches 93.33% and that based on PCA features optimization reaches 76.67%. So the genetic algorithm feature optimization is more suitable for lettuce leaf image feature optimization.
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