Automated strawberry grading system based on image processing

Xu, Liming; Yanchao, Zhao

doi:10.1016/j.compag.2009.09.013

Cited by 202 publications

(78 citation statements)

References 17 publications

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“…Another way is to use images of yams for shape determination. Such an approach has been widely used in fruit/crop grading, classification and removal before shipment [3][4][5][6]. Computational and statistical methodologies have been provided [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Kayano

Kyo

Hachiya

2017

Advances in Agriculture

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This paper proposes Bayesian methods for the shape estimation of Chinese yam (Dioscorea opposita) using a few key diameters of yam. Shape prediction of yam is applicable to determining optimal cutoff positions of a yam for producing seed yams. Our Bayesian method, which is a combination of Bayesian estimation model and predictive model, enables automatic, rapid, and lowcost processing of yam. After the construction of the proposed models using a sample data set in Japan, the models provide whole shape prediction of yam based on only a few key diameters. The Bayesian method performed well on the shape prediction in terms of minimizing the mean squared error between measured shape and the prediction. In particular, a multiple regression method with key diameters at two fixed positions attained the highest performance for shape prediction. We have developed automatic, rapid, and low-cost yam-processing machines based on the Bayesian estimation model and predictive model. Development of such shape prediction approaches, including our Bayesian method, can be a valuable aid in reducing the cost and time in food processing.

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Section: Introductionmentioning

confidence: 99%

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Kayano

Kyo

Hachiya

2017

Advances in Agriculture

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“…The dominant color feature extraction technique has been explained in [1]. In that the histograms over the 'a' channel of the Lab color model was created.…”

Section: Strip Histogram Analysismentioning

confidence: 99%

“…The automated strawberry grading system was presented in [1]. In that color, shape and size parameters were used for the grading.…”

Section: Introductionmentioning

confidence: 99%

Quality Inspection and Classification of Mangoes using Color and Size Features

Vyas¹,

Talati²,

Naik³

2014

IJCA

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“…were computed [6]. Liming and Yanchao [7] developed an automated strawberry grading system using image processing technique and graded the strawberry adopting one or two or three indices among shape, color and size. …”

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confidence: 99%

Digital Measurement of Maturity Indices of Mangoes using Selected Image Features

Fmim¹,

Smd²,

Fmd³

et al. 2017

J Food Process Technol

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Maturity is the most important factor to determine the storage-life and quality of fruits like mangoes. Fruit maturity can be recognized by different attributes and among them skin color is the most significant criteria for judging maturity. Typically, human experts visually detect the fruit color to identify the maturity stages which is very prone to error. In this paper, a method of digital image processing has been proposed to classify mangoes into six maturity stages according to the United States department of agriculture (USDA) standard classification. The experimentation considers sample images of more than 100 mangoes of different stages. A total of 24 image features are extracted and then correlation based and information gain based evaluation has been performed in order to select the most informative feature sets. Categorization is done using the decision tree which provides up to 96% classification accuracy. Several computer vision based methods have been developed to determine the state of the maturity of different fruits. A spectral analysis of bananas obtained under white and ultra-violet has been performed [3]. A Computer Vision System to estimate the antioxidant and phenol content on carrots based on the fruit's surface color was accomplished [4]. An index of the tomatoes ripeness is proposed, which allows classifying the fresh fruit into 6 classes, according to the USDA international standard [5]. A Computer Vision System to discriminate varieties of French olives is developed where Pit images (frontal and profile) were used and characteristics such as the histograms of the RGB model and form descriptors (area, perimeter, length, width, etc.) were computed [6]. Liming and Yanchao [7] developed an automated strawberry grading system using image processing technique and graded the strawberry adopting one or two or three indices among shape, color and size. Digital Measurement of Maturity Indices of Mangoes Using Selected Image FeaturesA method to classify mango fruits into their maturity stages was developed using the fuzzy logic and RGB color sensor model. The advantage of fuzzy approaches is, approximate inference can be performed by fuzzy IF-THEN [8]. Another method to categorize mangoes according to their maturity indices was proposed using their color and size features and histogram analysis. The advantage of histogram analysis is low computational complexity [9].As it was seen, a large number of works to estimate the maturity

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Automated strawberry grading system based on image processing

Cited by 202 publications

References 17 publications

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Quality Inspection and Classification of Mangoes using Color and Size Features

Digital Measurement of Maturity Indices of Mangoes using Selected Image Features

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