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Symmetry 2018, 10(7), 270; https://doi.org/10.3390/sym10070270

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Computer Science and Engineering Department, Technological Educational Institute of Thessaly, 41110 Larissa, Greece
Received: 16 May 2018 / Revised: 1 July 2018 / Accepted: 6 July 2018 / Published: 9 July 2018
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
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Abstract

Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications. View Full-Text
Keywords: image processing; disease diagnosis; plant disease; segmentation; classification; image filtering image processing; disease diagnosis; plant disease; segmentation; classification; image filtering
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Petrellis, N. A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis. Symmetry 2018, 10, 270.

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