Applications of Image Color Features

Chaki, Jyotismita; Dey, Nilanjan

doi:10.1007/978-981-15-5761-3_5

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…There are different types of colour models. In the present work, the RGB model; hue, saturation, value (HSV) model; L*a*b* model of the International Commission on Illumination (CIE, for its acronym in French) (1976), and luma in-phase quadrature (YIQ) model, which separates colour from brightness, were used [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Recognition of Maize Phenology in Sentinel Images with Machine Learning

Murguia-Cozar

Macedo-Cruz

Fernández-Reynoso

et al. 2021

Sensors

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The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran’s I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.

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Section: Methodsmentioning

confidence: 99%

Recognition of Maize Phenology in Sentinel Images with Machine Learning

Murguia-Cozar

Macedo-Cruz

Fernández-Reynoso

et al. 2021

Sensors

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“…Because of the irregularity in the diseased cucumber leaves, many existing classifier cannot meet the needs of the disease recognition system (Zhang et al, 2017). The usage of the image colour is the most important issues in the creation efficient content-based image retrieval (Chaki & Dey, 2021).The major advantage of the deep learning method is it does not have to be explicitly identified in the image (Barbedo, 2018). Deep learning can also be applied in the area of disease identification using the images (Too et al, 2018).…”

mentioning

confidence: 99%

Self‐adaptive‐deer hunting optimization‐based optimal weighted features and hybrid classifier for automated disease detection in plant leaves

Sahu

Minz

2022

Expert Systems

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This main intention of this paper is to adopt a new disease detection model for plant leaves. The proposed model involves several steps such as pre‐processing, leaf segmentation, abnormality segmentation, feature extraction and detection. Image scaling and contrast enhancement are performed during the pre‐processing phase. Once the pre‐processing is done, the segmentation phase starts with leaf segmentation by binary thresholding method and abnormality segmentation by K‐means clustering. Further, the local binary pattern and grey‐level co‐occurrence matrix features are extracted and a dimensionality reduced technique called principle component analysis is determined. As the main novelty, the weighted feature extraction is performed, in which the weight functions are optimized by the self‐adaptive deer hunting optimization (SA‐DHOA). Another contribution of this paper is to implement a hybrid classifier for disease detection. Here, the extracted weighted features are subjected to a support vector machine, and the abnormality segmented image is subjected to convolutional neural network (CNN), which is a deep learning algorithm that can learn the features automatically. Here, the same SA‐DHOA is used to improve the performance of CNN, which is termed as SA‐DHOA‐SCNN. Finally, the performance analysis confirms the maximum success rate of the proposed model over other conventional methods.

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Applications of Image Color Features

Cited by 2 publications

References 18 publications

Recognition of Maize Phenology in Sentinel Images with Machine Learning

Recognition of Maize Phenology in Sentinel Images with Machine Learning

Self‐adaptive‐deer hunting optimization‐based optimal weighted features and hybrid classifier for automated disease detection in plant leaves

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