Digital Image Processing for Classification of Coffee Cherries

Sandoval, Zulma; Prieto, Flavio; Betancur, Julián

doi:10.1109/cerma.2010.54

Cited by 11 publications

(5 citation statements)

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“…Different roasting conditions result in different final product quality, that, different colors, flavors, aroma, and acidity [ 29 ]. Futhermore, the chemical coffee bean properties depend on other factors such as species and geographic location [ [29] , [30] , [31] , [32] , [33] ]. Different roasting profiles in two coffee species were evaluated in this study.…”

Section: Resultsmentioning

confidence: 99%

Impact of different roasting conditions on the chemical composition, antioxidant activities, and color of Coffea canephora and Coffea arabica L. samples

Freitas,

Rodrigues Borges,

Dias Castro

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Impact of different roasting conditions on the chemical composition, antioxidant activities, and color of Coffea canephora and Coffea arabica L. samples

Freitas,

Rodrigues Borges,

Dias Castro

et al. 2023

Heliyon

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“…Sandoval et al [6] conducted research using ordinary images; the classification method used was the Naïve Bayes method. Classifying coffee fruit maturity levels is based on color, texture, and shape features extracted from coffee fruit images.…”

Section: Fig 1 Details Of Previous Studiesmentioning

confidence: 99%

Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method

‘Ulhaq,

Hidayat,

Dharmawan

2024

Jurnal Ilmu Komputer dan Informasi

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The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.

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“…According to International Coffee Organization (ICO), the estimated average number of global coffee consumption in the past 4 years was higher than 8 × 10 6 tons 1 . Identification of coffee beans has been studied by traditional reagent-based laboratory chemical methods 2 , 3 , spectroscopy techniques 4 , 5 and digital imaging techniques 6 , 7 . Reagent-based chemical methods are time consuming, reagent dependent and complex to operate.…”

Section: Introductionmentioning

confidence: 99%

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

Zhang

Liu

2018

Sci Rep

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Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF).Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.Coffee is one of the most popular beverage in the world. Coffee variety is among the key factors influencing the coffee quality and price. According to International Coffee Organization (ICO), the estimated average number of global coffee consumption in the past 4 years was higher than 8 × 10 6 tons 1 . Identification of coffee beans has been studied by traditional reagent-based laboratory chemical methods 2,3 , spectroscopy techniques 4,5 and digital imaging techniques 6,7 . Reagent-based chemical methods are time consuming, reagent dependent and complex to operate. Spectroscopy and imaging techniques have been widely adopted as rapid, non-destructive and accurate techniques. Hyperspectral imaging (HSI), a technique integrating both spectroscopy and imaging techniques, has drawn raising attentions from researchers of different fields. HSI acquires spectral and spatial information simultaneously. The hyperspectral image is a three-dimensional (3D) data cube (x, y, λ) with the two-dimensional spatial information (x, y) and the third dimension of spectral information (λ). Each pixel has a spectrum in the hyperspectral image together with a gray-scale image at each wavelength. Hyperspectral imaging has been reported to detect coffee quality [8][9][10][11][12][13] , and the use of pixel-wise spectra have not been discussed in coffee quality determination.One of the main advantages of hyperspectral imaging is to form and visualize the distribution maps of the samples. It reveals not only the physical attributes but also the chemical compositions within or between samples. Theoretically, visualization of the physical attributes and the chemical...

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Digital Image Processing for Classification of Coffee Cherries

Cited by 11 publications

References 1 publication

Impact of different roasting conditions on the chemical composition, antioxidant activities, and color of Coffea canephora and Coffea arabica L. samples

Impact of different roasting conditions on the chemical composition, antioxidant activities, and color of Coffea canephora and Coffea arabica L. samples

Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

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