Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

Monsalve, Manuel Alejandro Tamayo; Mercado-Ruiz, Esteban; Villa-Pulgarin, Juan Pablo; Bravo-Ortíz, Mario Alejandro; Arteaga-Arteaga, Harold Brayan; Mora-Rubio, Alejandro; Alzate-Grisales, Jesús Alejandro; Arias-Garzón, Daniel; Romero-Cano, Víctor; Orozco-Arias, Simón; Osorio, Gustavo; Tabares-Soto, Reinel

doi:10.1109/access.2022.3166515

Cited by 15 publications

(14 citation statements)

References 41 publications

(40 reference statements)

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“…Other studies that addressed coffee classification issues, regardless of the approach, include but are not limited to [38][39][40][41][42][43][44][45].…”

Section: Electronic Nosementioning

confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

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“…Other studies that addressed coffee classification issues, regardless of the approach, include but are not limited to [38][39][40][41][42][43][44][45].…”

Section: Electronic Nosementioning

confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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“…Tamayo-Monsalve et al [11] evaluated different CNN architectures to classify spectral images of coffee fruits in various stages of ripening. However, using spectral images for coffee fruit classification has challenges, such as resource-intensive storage and processing, the need for specialized equipment, and extensive computational resources for effective analysis.…”

Section: Related Workmentioning

confidence: 99%

Enhancing green coffee quality assessment through deep learning

Pereira Neto,

Sousa,

Moreira

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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Coffee is the world’s most consumed commodity beverage, vital for the Brazilian market. Assessing coffee bean quality through visual features is essential for market value. However, human-based visual analysis has limitations. Deep neural networks, particularly CNNs, offer a promising solution by automating this process. In this work, we propose an evaluation of deep learning models and training strategies to classify green coffee beans automatically. We evaluate four CNN architectures: AlexNet, ResNet-50, MobileNet V3, and EfficientNet B4. After a hyperparameter optimization step, the models were fine-tuned, and we evaluated the impact of data augmentation strategies on the classification performance through the USK-Coffee dataset. EfficientNet B4 excels, achieving 0.8844 accuracy when trained with data augmentation. Our findings showcase deep learning’s potential for coffee quality assessment, aiding professionals in classifying and guaranteeing coffee quality and value.

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“…Multispectral images of coffee fruit are obtained using a special camera modified with specifications in the form of a wide electromagnetic spectrum, controlled illumination space, and narrow LED bandwidth [4], [5]. The camera produces 15 color channels, including violet, royal blue, blue, azure, cyan, green, lime, yellow, amber, red-orange, red, deep red, far red, and 2 Near Infrared (NIR).…”

Section: Introductionmentioning

confidence: 99%

“…In research [4] utilizing the ability of the Convolutional Neural Network (CNN) method to extract patterns from high-dimensional multispectral images of coffee fruit. The high complexity of CNN allows the model to capture more complex features in multispectral images.…”

Section: Introductionmentioning

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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Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

Abstract: 2019 under the Hermes Project through Construcción del Prototipo de un Sistema de Visión Multiespectral Basado en Iluminación Light Emitting Diode (LED) under Grant 48996.

Cited by 15 publications

References 41 publications

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Enhancing green coffee quality assessment through deep learning

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

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