Image Segmentation for Feature Extraction

Author:

Deisy C.1,Francis Mercelin1

Affiliation:

1. Thiagarajar College of Engineering, India

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.

Publisher

IGI Global

Reference38 articles.

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4. An empirical investigation of olive leave spot disease using auto-cropping segmentation and fuzzy C-means classification;S. M.Al-Tarawneh;World Applied Sciences Journal,2013

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2. Deep Learning and Machine Learning Based Efficient Framework for Image Based Plant Disease Classification and Detection;2022 International Conference on Advanced Computing Technologies and Applications (ICACTA);2022-03-04

3. Application of machine learning techniques in rice leaf disease detection;Materials Today: Proceedings;2021-12

4. Mathematical and Visual Understanding of a Deep Learning Model Towards m-Agriculture for Disease Diagnosis;Archives of Computational Methods in Engineering;2020-02-27

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