Abstract
Automated disease detection using the features of infected regions of a diseased plant image is a growing field of research in precision agriculture. Usually, infected regions are identified by applying different threshold based segmentation techniques. However, due to various factors like non-uniform illumination or noises, these techniques fail to provide sufficient information for classifying diseases accurately. In the paper, a novel region identification method based on Fermi energy has been proposed to detect the infected portion of the diseased rice images. From the infected region, neighboring gray level dependence matrix (NGLDM) based texture features are extracted to classify different diseases of rice plants. Performance of the proposed method has been evaluated by comparing classification accuracy with other segmentation algorithms, demonstrating superior result.
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Phadikar, S., Sil, J., KumarDas, A. (2013). Region Identification of Infected Rice Images Using the Concept of Fermi Energy. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_82
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DOI: https://doi.org/10.1007/978-3-642-31552-7_82
Publisher Name: Springer, Berlin, Heidelberg
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