Fruit Classification by Extracting Color Chromaticity, Shape and Texture Features: Towards an Application for Supermarkets

Garcia, Farid; Cervantes, Jair; López, Asdrúbal; Alvarado, Matı́as

doi:10.1109/tla.2016.7587652

Cited by 37 publications

(16 citation statements)

References 15 publications

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“…Fourier-descriptor is used for shape feature extraction with SVM to classify mangoes and has achieved almost 100% classification results in [35]. Shape, texture and color (HSV) feature extraction is used to classify 20 categories of fruits in [36]. Color, shape, and texture feature extracted with PCA, biogeography-based optimization (BBO) and feed forward neural network (FNN) to classify 18 categories of fruits in [37] which achieves 89.11% accuracy.…”

Section: Shape Feature Extractionmentioning

confidence: 99%

Machine Vision based Fruit Classification and Grading - A Review

Naik¹,

Patel²

2017

IJCA

105

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Section: Shape Feature Extractionmentioning

confidence: 99%

Machine Vision based Fruit Classification and Grading - A Review

Naik¹,

Patel²

2017

IJCA

105

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“…Garcia [12] proposed a novel feature extraction method Firstly, the RGB space in the image of the fruit was captured, the image was mapped to the HSV space. The saturated background color of the fruit was divided from the background information.…”

Section: Methods and Resultsmentioning

confidence: 99%

A study on classification of fruit type and fruit disease

Yang¹,

Kichida²

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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with the rise of biotechnology, the similarity between fruits is growing, which has some impact on our lives. Hence, it needs to design a system to classify the types of fruits. If the fruit is placed over a certain period of time, it will be infected. Meanwhile, it is also important to classify the disease of fruits. In this paper, we introduce several approaches to classify the type of fruits and fruit diseases automatically. Finally, we make an overall comparison based on these methods.

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“…In addition, there are a number of works which focus on a single feature of fruit and not consider more than one feature. For example, some feature base fruit recognition models, like [20] focuses on color chromaticity, model [21] focuses on wavelet, model [22] focuses on shape feature and only consider recognizing fruit without detecting or locating it from the global image. In this paper, by global image we refer an image with multiple objects and rich background where a local image is an image with single object and simple background.…”

Section: A Fruit Detection and Classification Using Selected Featurementioning

confidence: 99%

“…3(a), are taken with a very noisy background which can totally distract the machine learning model and can point out a completely wrong target object and the entire algorithm can be failed. But, models like [20,21] and [22] do not consider fruit images with reach background. Most of the existing models work better on an image with single fruit in white background.…”

Section: Introductionmentioning

confidence: 99%

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

Khan¹,

Debnath²

2019

IJIGSP

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In this paper, an efficient approach has been proposed to localize every clearly visible object or region of object from an image, using less memory and computing power. For object detection we have processed every input image to overcome several complexities, which are the main limitations to achieve better result, such as overlap between multiple objects, noise in the image background, poor resolution etc. We have also implemented an improved Convolutional Neural Network based classification or recognition algorithm which has proved to provide better performance than baseline works. Combining these two detection and recognition approaches, we have developed a competent multi-class Fruit Detection and Recognition (FDR) model that is very proficient regardless of different limitations such as high and poor image quality, complex background or lightening condition, different fruits of same shape and color, multiple overlapped fruits, existence of non-fruit object in the image and the variety in size, shape, angel and feature of fruit. This proposed FDR model is also capable of detecting every single fruit separately from a set of overlapping fruits. Another major contribution of our FDR model is that it is not a dataset oriented model which works better on only a particular dataset as it has been proved to provide better performance while applying on both real world images (e.g., our own dataset) and several states of art datasets. Nevertheless, taking a number of challenges into consideration, our proposed model is capable of detecting and recognizing fruits from image with a better accuracy and average precision rate of about 0.9875.

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Fruit Classification by Extracting Color Chromaticity, Shape and Texture Features: Towards an Application for Supermarkets

Cited by 37 publications

References 15 publications

Machine Vision based Fruit Classification and Grading - A Review

Machine Vision based Fruit Classification and Grading - A Review

A study on classification of fruit type and fruit disease

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

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