Classification of African Mosaic Virus Infected Cassava Leaves by the Use of Multi-Spectral Imaging

Mama Sangare¹, Thérèse Atcham Agneroh², Olivier Kossan Bagui³, Issiaka Traore¹, Abdramane Ba¹, Jeremie Thouakesseh Zoueu^3*

¹Laboratoire d’Optique, de Spectroscopie et des Sciences de l’Atmosphere, Université des Sciences, Techniques et Technologies de Bamako (USTTB), Bamako, Mali.
²Laboratoire de Phytopathologie et Biologie Végétale, Institut National Polytechnique Felix Houphouët-Boigny, Yamoussoukro, Cote d’Ivoire.
³Laboratoire d’Instrumentation Image et Spectroscopie, Institut National Polytechnique Felix Houphouët-Boigny, Yamoussoukro, Cote d’Ivoire.

DOI: 10.4236/opj.2015.58025                

Abstract

In this work, we have used multispectral imaging technology to classify cassava leaves infected by African mosaic virus by the use of their unique spectral finger print. The spectra are extracted from transmission, reflection and diffusion of their multispectral images; they have been then analyzed with statistical multivariate analysis techniques. Principal component analysis (PCA) has been used followed by K-means and Ascending Hierarchical Classification (AHC) to endorse the classification. The contribution of this work is the use of multispectral imagery which binds both spatial and spectral information to differentiate and sort infected leaves. The results show that the multimodal and imaging spectroscopy may allow blind identification and characterization of infected leaves.

Keywords

Classification, Infected Leaves, African Cassava Mosaic Virus, Multispectral Imaging

Conflicts of Interest

The authors declare no conflicts of interest.

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