A smart phone image processing application for plant disease diagnosis

Petrellis, Nikos

doi:10.1109/mocast.2017.7937683

Cited by 52 publications

(44 citation statements)

References 9 publications

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“…Histograms may be normalized by dividing the number of pixels with the same color level with the total number of pixels in the image. Different histograms may be defined for each one of the segmented regions of an image [40,71,72]. Histograms like these, have already been presented in Figure 1c,d and Figure 2c-e as they appear in the Plant and Skin Disease applications developed by the author.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 95%

“…A mask with four levels of gray (an extension of the binary mask) is used to represent the distinguished regions (normal skin, lesion spots, halo, background) as shown in Figure 2b. Similarly, the same method is applied for plant disease diagnosis where the thresholds in the gray level are used to distinguish the normal plant part from the spots and the background as shown in Figure 1b ( [71,72]). Binary masks are also used in other applications of precision agriculture.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

“…Their definition may slightly differ in each approach. The histogram of a gray image can represent the number of pixels that have the same intensity [40,71,72]. For example, if the value of the histogram at the position 50 of the horizontal axis is 520, this means that there are 520 pixels in the image that have gray level equal to 50.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

“…The framework described in this section was implemented for plant disease recognition as a mobile application (Plant Disease) [71,72]. The same classification method was also adapted in a similar mobile application called Skin Disease that has been developed for skin disorder diagnosis.…”

Section: An Image Processing Technique Appropriate For Mobile Classifmentioning

confidence: 99%

“…RGB to L*a*b color conversion is performed to the images retrieved. The author of the present review has recently presented a smartphone application that can diagnose plant (e.g., citrus and vine) diseases [71,72].…”

Section: Introductionmentioning

confidence: 99%

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

Petrellis

2018

Symmetry

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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.

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Section: Image Enhancement Filtering Methodsmentioning

confidence: 95%

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

Section: An Image Processing Technique Appropriate For Mobile Classifmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Petrellis

2018

Symmetry

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Intelligent microscopic approach for identification and recognition of citrus deformities

Safdar

Khan

Shah

et al. 2019

Microscopy Res & Technique

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Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial‐based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self‐collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.

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