A Novel Fruit Recognition Technique using Multiple Features and Artificial Neural Network

Naskar, Saswati; Bhattacharya, Tanmay

doi:10.5120/20453-2808

Cited by 18 publications

(9 citation statements)

References 8 publications

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“…Dilation operation is worked to get the specific boundary and median filter are used to get a more specific boundary. Complement, erosion and dilation processes are shown in the equations (6) and (8). The texture is a repeated pattern of information or arrangement of the structure with regular intervals.…”

Section:  mentioning

confidence: 99%

“…That constraint can work as future work. [8] Jana et al (2016) exploited proposes for the solution to solve a viewpoint invariant for fruit intra-class recognition by combining color and texture features and using a Neural Network (NN) classifier. The standard deviation of each RGB color channel is extracted from the color histogram for color features, and the gray-level co-occurrence matrix is calculated with four directions for texture features.…”

Section: Introductionmentioning

confidence: 99%

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Fruit Recognition Using Color and Morphological Features Fusion

San¹,

Aung²,

Khaing³

2019

IJIGSP

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It is still difficult to recognize the kind of fruit which are of different colors, shapes, and textures. This paper proposes a features fusion method to recognize five different classes of fruits that are the images from the fruit360 dataset. We are processed with four stages: preprocessing, boundary extraction, feature extractions, and classification. Pre-processing is performed to remove the noise by using the median filter, and boundary extraction are operated with the morphological operation. In feature extraction, we have extracted two types of features: color, and morphological features of the image. Color features are extracted from the RGB color channel, and morphological features are extracted from the image that detected the boundary of fruit by using morphological operations. These two types of features are combined in a single feature descriptor.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fruit Recognition Using Color and Morphological Features Fusion

San¹,

Aung²,

Khaing³

2019

IJIGSP

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“…Naskar S.et.al [9] has introduced a system for identifying fruits in which multiple attributes like color, shape and texture are considered. They have used ANN [Artificial Neural Network]technique in which they have utilized log Gabor filter to recognize the texture .Log Gabor filter is basically an advanced version of Gabor filter.…”

Section: Literature Surveymentioning

confidence: 99%

Segmentation and Classification of Fruit Images Independent of Image Orientation using Height Width Vectors

R¹,

Gopika²,

Apoorva³

et al. 2019

IJITEE

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Detecting fruits automatically through image processing is a bewildering job because of certain properties of several types of fruits. For each image in the database preprocessing removes background and separates the foreground layer.The images are segmented using image threshold method. The classification is done by using a classifier named KNN with the desirable aim of exact and fast classification of fruit.We have considered 170 images examining various cases like single fruit and multiple fruits of same type, different orientation of same type,cut fruit and full fruit images of same type ,different colored fruit images of same type, multiple fruits of different types.. Our system successfully recognizes fruit images with 97.6% accuracy and provides an F- measure of 88%.

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“…Not only fruits, it plays an important role in recognizing visual attribute that can easily differentiate images. Previously several texture feature extraction procedure have been developed like GLCM (Grey Level Cooccurrence Matrix) approach, SFTA algorithm or DWT (Discrete wavelet transform) method for image classification [24]. Shape is one of the primary visual features in CBIR.…”

Section: Feature Extractionmentioning

confidence: 99%

Quality evaluation of apple fruit: A Survey

Sindhi¹,

Pandya²,

Vegad³

2016

IJCA

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Disease recognition has been huge research area nowadays because inspection of quality of fruits at an early stage prevents spreading of disease to the other areas of fruit as well as helps to reduce great economic losses in agricultural sectors and industries. Different types of diseases exist in different fruits. The focus of the present research work is on quality evaluation of apple fruit. The basic process for defect detection in fruits is basically divided into two major steps; feature extraction and classification .Feature extraction involves extracting features like color, texture and shape from fruit image. The output of this are feature vectors which are given as an input to the classifier. Finally, the classifier categorizes them into appropriate classes. The accuracy of this process depends on many factors like number of input images, method chosen for pre processing, features extracted, classifier chosen, etc.

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A Novel Fruit Recognition Technique using Multiple Features and Artificial Neural Network

Cited by 18 publications

References 8 publications

Fruit Recognition Using Color and Morphological Features Fusion

Fruit Recognition Using Color and Morphological Features Fusion

Segmentation and Classification of Fruit Images Independent of Image Orientation using Height Width Vectors

Quality evaluation of apple fruit: A Survey

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