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Keywords = SGWT

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21 pages, 6449 KiB  
Article
Nondestructive Detection of Corky Disease in Symptomless ‘Akizuki’ Pears via Raman Spectroscopy
by Yue Yang, Weizhi Yang, Hanhan Zhang, Jing Xu, Xiu Jin, Xiaodan Zhang, Zhengfeng Ye, Xiaomei Tang, Lun Liu, Wei Heng, Bing Jia and Li Liu
Sensors 2024, 24(19), 6324; https://doi.org/10.3390/s24196324 - 29 Sep 2024
Cited by 2 | Viewed by 1333
Abstract
‘Akizuki’ pear (Pyrus pyrifolia Nakai) corky disease is a physiological disease that strongly affects the fruit quality of ‘Akizuki’ pear and its economic value. In this study, Raman spectroscopy was employed to develop an early diagnosis model by integrating support vector machine [...] Read more.
‘Akizuki’ pear (Pyrus pyrifolia Nakai) corky disease is a physiological disease that strongly affects the fruit quality of ‘Akizuki’ pear and its economic value. In this study, Raman spectroscopy was employed to develop an early diagnosis model by integrating support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) modeling techniques. The effects of various pretreatment methods and combinations of methods on modeling results were studied. The relative optimal index formula was utilized to identify the SG and SG+WT as the most effective preprocessing methods. Following the optimal preprocessing method, the performance of the majority of the models was markedly enhanced through the process of model reconditioning, among which XGBoost achieved 80% accuracy under SG+WT pretreatment, and F1 and kappa both performed best. The results show that RF, GBDT, and XGBoost are more sensitive to the pretreatment method, whereas SVM and CNN are more dependent on internal parameter tuning. The results of this study indicate that the early detection of Raman spectroscopy represents a novel approach for the nondestructive identification of asymptomatic ‘Akizuki’ pear corky disease, which is of paramount importance for the realization of large-scale detection across orchards. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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17 pages, 6279 KiB  
Article
Data-Wise Spatial Regional Consistency Re-Enhancement for Hyperspectral Image Classification
by Lijian Zhou, Erya Xu, Siyuan Hao, Yuanxin Ye and Kun Zhao
Remote Sens. 2022, 14(9), 2227; https://doi.org/10.3390/rs14092227 - 6 May 2022
Cited by 5 | Viewed by 2060
Abstract
Effectively using rich spatial and spectral information is the core issue of hyperspectral image (HSI) classification. The recently proposed Diverse Region-based Convolutional Neural Network (DRCNN) achieves good results by weighted averaging the features extracted from several predefined regions, thus exploring the use of [...] Read more.
Effectively using rich spatial and spectral information is the core issue of hyperspectral image (HSI) classification. The recently proposed Diverse Region-based Convolutional Neural Network (DRCNN) achieves good results by weighted averaging the features extracted from several predefined regions, thus exploring the use of spatial consistency to some extent. However, such feature-wise spatial regional consistency enhancement does not effectively address the issue of wrong classifications at the edge of regions, especially when the edge is winding and rough. To improve the feature-wise approach, Data-wise spAtial regioNal Consistency re-Enhancement (“DANCE”) is proposed. Firstly, the HSIs are decomposed once using the Spectral Graph Wavelet (SGW) to enhance the intra-class correlation. Then, the image components in different frequency domains obtained from the weight map are filtered using a Gaussian filter to “debur” the non-smooth region edge. Next, the reconstructed image is obtained based on all filtered frequency domain components using inverse SGW transform. Finally, a DRCNN is used for further feature extraction and classification. Experimental results show that the proposed method achieves the goal of pixel level re-enhancement with image spatial consistency, and can effectively improve not only the performance of the DRCNN, but also that of other feature-wise approaches. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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