Abstract
The use of hyperspectral imaging systems in studying plant properties, types, and conditions has significantly increased due to numerous economical and financial benefits. It can also enable automatic identification of plant phenotypes. Such systems can underpin a new generation of precision agriculture techniques, for instance, the selective application of plant nutrients to crops, preventing costly losses to soils, and the associated environmental impact to their ingress into watercourses. This paper is concerned with the analysis of hyperspectral images and data for monitoring and classifying plant conditions. A spectral-texture approach based on feature selection and the Markov random field model is proposed to enhance classification and prediction performance, as compared to conventional approaches. Two independent hyperspectral datasets, captured by two proximal hyperspectral instrumentations with different acquisition dates and exposure times, were used in the evaluation. Experimental results show promising improvements in the discrimination performance of the proposed approach. The study shows that such an approach can shed a light on the attributes that can better differentiate plants, their properties, and conditions.
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AlSuwaidi, A., Grieve, B., Yin, H. (2017). Towards Spectral-Texture Approach to Hyperspectral Image Analysis for Plant Classification. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_28
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DOI: https://doi.org/10.1007/978-3-319-68935-7_28
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