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Citrus black spot detection using hyperspectral imaging

International Journal of Agricultural and Biological Engineering 2014年第6期7卷 20-27页

作者： Daegwan Kim Thomas F.Burks Mark A.Ritenour Jianwei Qin Asan Institute for Life Sciences Asan Medical CenterSongpa-guSeoul 138-736Korea Department of Agricultural&Biological Engineering University of FloridaGainesvilleFL 32611USA Indian River Research and Education Center(IRREC) University of FloridaFt.PierceFL 34945USA Environmental Microbial and Food Safety Laboratory(EMFSL) Agricultural Research ServiceU.S.Department of AgricultureBeltsvilleMD 20705USA

This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot(CBS).Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot,wind scar,or *** angle mapper(SAM)and spectral information divergence(SID)hyperspectral analysis approaches were used to classify fruit samples into two classes:CBS or *** classification accuracy for CBS with SAM approach was 97.90%,and 97.14% with *** combination of hyperspectral images and two classification approaches(SID and SAM)have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel *** study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.

关键词： citrus black spot hyperspectral imaging spectral angle mapper spectral information divergence imaging processing

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Lithological Mapping Using Landsat 8 OLI in the Meso-Cenozoic Tarfaya Laayoune Basin (South of Morocco): Comparison between ANN and SID Classification

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Open Journal of Geology 2021年第12期11卷 658-681页

作者： Amine Bouwafoud Mustapha Mouflih Abdelmajid Benbouziane Hassan II University of Casablanca Faculty of Sciences Ben M’sik Geosciences and Application Laboratory (LGA) Sidi Othmane Casablanca Morocco

In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs. For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping. The two supervised classification methods by machine learning (Artificial Neural Network and spectral information divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications. The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area. Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.

关键词： Tarfaya-Laayoune Basin Geological Mapping Supervised Classification Artificial Neural Network spectral information divergence

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