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Image Segmentation for Feature Extraction: A Study on Disease Diagnosis in Agricultural Plants

Image Segmentation for Feature Extraction: A Study on Disease Diagnosis in Agricultural Plants

C. Deisy, Mercelin Francis
Copyright: © 2018 |Pages: 26
ISBN13: 9781522557753|ISBN10: 152255775X|EISBN13: 9781522557760
DOI: 10.4018/978-1-5225-5775-3.ch013
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MLA

Deisy, C., and Mercelin Francis. "Image Segmentation for Feature Extraction: A Study on Disease Diagnosis in Agricultural Plants." Feature Dimension Reduction for Content-Based Image Identification, edited by Rik Das, et al., IGI Global, 2018, pp. 232-257. https://doi.org/10.4018/978-1-5225-5775-3.ch013

APA

Deisy, C. & Francis, M. (2018). Image Segmentation for Feature Extraction: A Study on Disease Diagnosis in Agricultural Plants. In R. Das, S. De, & S. Bhattacharyya (Eds.), Feature Dimension Reduction for Content-Based Image Identification (pp. 232-257). IGI Global. https://doi.org/10.4018/978-1-5225-5775-3.ch013

Chicago

Deisy, C., and Mercelin Francis. "Image Segmentation for Feature Extraction: A Study on Disease Diagnosis in Agricultural Plants." In Feature Dimension Reduction for Content-Based Image Identification, edited by Rik Das, Sourav De, and Siddhartha Bhattacharyya, 232-257. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5775-3.ch013

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Abstract

This chapter explores the prevailing segmentation methods to extract the target object features, in the field of plant pathology for disease diagnosis. The digital images of different plant leaves are taken for analysis as most of the disease symptoms are visible on leaves apart from other vital parts. Among the different phases of processing a digital image, the substantive focus of the study concentrates mainly on the methodology or algorithms deployed on image acquisition, preprocessing, segmentation, and feature extraction. The chapter collects the existing literature survey related to disease diagnosis methods in agricultural plants and prominently highlights the performance of each algorithm by comparing with its counterparts. The main aim is to provide an insight of creativeness to the researchers and experts to develop a less expensive, accurate, fast and an instant system for the timely detection of plant disease, so that appropriate remedial measures can be taken.

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