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Extracting Feature Bands for Damaged Rice Leaves by Planthoppers Using Multi-spectral Imaging Technology
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作者 曹鹏飞 李宏宁 +2 位作者 杨卫平 林立波 冯洁 《Agricultural Science & Technology》 CAS 2013年第11期1642-1645,1669,共5页
[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] T... [Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] The experiment, using multi-spectral imaging system, acquired the multi-spectral images of damaged rice leaves from band 400 to 720 nm by interval of 5 nm. [Result] According to the principle of band index, it was calculated that the bands at 515, 510, 710, 555, 630, 535, 505, 530 and 595 nm were having high band index value with rich information and little correlation. Furthermore, the experiment used two classification methods and calcu-lated the classification accuracy higher than 90.00% for feature bands and ful bands of damaged rice leaves by planthoppers respectively. [Conclusion] It can be con-cluded that these bands can be considered as effective feature bands to identify damaged rice leaves by planthoppers quickly from a large scale of crops. 展开更多
关键词 feature bands Multi-spectral imaging Damaged rice leaves Planthop-pers Classification accuracy
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Wheat FHB resistance assessment using hyperspectral feature band image fusion and deep learning
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作者 Kun Liang Zhizhou Ren +2 位作者 Jinpeng Song Rui Yuan Qun Zhang 《International Journal of Agricultural and Biological Engineering》 SCIE 2024年第2期240-249,共10页
The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight(FHB)hazards,so it is important to identify and evaluate resistant varieties.The traditional resistance pheno... The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight(FHB)hazards,so it is important to identify and evaluate resistant varieties.The traditional resistance phenotype identification is still largely dependent on time-consuming manual methods.In this paper,the method for evaluating FHB resistance in wheat ears was optimized based on the fusion feature wavelength images of the hyperspectral imaging system and the Faster R-CNN algorithm.The spectral data from 400-1000 nm were preprocessed by the multiple scattering correction(MSC)algorithm.Three feature wavelengths(553 nm,682 nm and 714 nm)were selected by analyzing the X-loading weights(XLW)according to the absolute value of the peaks and troughs in different principal component(PC)load coefficient curves.Then,the different fusion methods of the three feature wavelengths were explored with different weight coefficients.Faster R-CNN was trained on the fusion and RGB datasets with VGG16,AlexNet,ZFNet,and ResNet-50 networks separately.Then,the other detection models SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet were used to compare with the Faster R-CNN model.As a result,the Faster R-CNN with VGG16 was best with the mAP(mean Average Precision)ranged from 97.7%to 98.8%.The model showed the best performance for the Fusion Image-1 dataset.Moreover,the Faster R-CNN model with VGG16 achieved an average detection accuracy of 99.00%,which was 23.89%,1.21%,0.75%,0.62%,and 8.46%higher than SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet models.Therefore,it was demonstrated that the Faster R-CNN model based on the hyperspectral feature image fusion dataset proposed in this paper was feasible for rapid evaluation of wheat FHB resistance.This study provided an important detection method for ensuring wheat food security. 展开更多
关键词 Fusarium head blight resistance evaluation hyperspectral feature band image fusion deep learning Faster R-CNN
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Origion of Band-like and Atom-like Features of the Valence Band Auger Emission from Thansition Metals
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作者 Jianmin YUAN(Department of Applied Physics, National University of Defense Technology, Changsha 410073, China)L.Intsche and J.Nome(Institut fr Theoretische Physik B, TU Clausthal D-38678 Clausthal-Zellerfeld, Germany)To whom correspondence should be addre 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 1999年第4期375-376,共2页
The occurrence of both band-like and atom-like Auger spectra involving valence band electron of d-transition metals is discussed based on the two-step model of the Auger electron emission, i.e.an initial core-hole is ... The occurrence of both band-like and atom-like Auger spectra involving valence band electron of d-transition metals is discussed based on the two-step model of the Auger electron emission, i.e.an initial core-hole is first generated and the Auger transition occurs between the core-hole andthe valence states, The occupied vaIence states relax to screen the core-hole which results in a redistribution of the valence electrons, The electronic states concerned by the Auger transitionare calculated by the FLAPW method. There is a clear relation between band-like and atom-like features of the spectra and the different responses of these metals to the existence of a core-hole. 展开更多
关键词 ATOM Origion of band-like and Atom-like features of the Valence band Auger Emission from Thansition Metals Rev
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