Peaberry and normal coffee bean classification using CNN, SVM, and KNN: Their implementation in and the limitations of Raspberry Pi 3

Gope, Hira Lal; Fukai, Hidekazu

doi:10.3934/agrfood.2022010

Cited by 16 publications

(15 citation statements)

References 19 publications

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“…It is seen in all the states that in the three states, the peaberry's width and thickness did not present considerable variation, with a difference lower than 1 mm, consistent with the fact that their shape is ellipsoidal (Gope & Fukai, 2022;González et al, 2019), implying that the peaberries were more homogeneous disregarding the state.…”

Section: Orthogonal Dimensionssupporting

confidence: 58%

Peaberry shape and size influence on different coffee postharvest processes

Duque‐Dussán,

Figueroa‐Varela,

Sanz‐Uribe

2023

J Food Process Engineering

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In many coffee‐producing countries, the ellipsoidal‐shaped seeds called peaberries are often labeled as a defect because of their shape and reduced size, going against the market demand for large‐sized standard coffee beans. Nevertheless, the peaberry natural occurrence on the coffee plantations is significant, accounting for 5%–7% of the total harvested coffee for Coffea arabica L. and Coffea canephora, the most planted species worldwide. Nevertheless, recent growth in the peaberry market has happened due to exceptional cupping scores for this specific bean type; however, the relationship between these scores and the shape of the bean was not yet recorded in the literature. Therefore, this research aimed to evaluate and compare the impact of the shape and size of the peaberry against the standard beans in different postharvest processes: Drying, roasting (colorimetry and inner roasting profiles), grinding (compressive and shear force tests) and overall quality by cupping analysis. Coffea arabica L. var. Cenicafé 1 was used throughout all the experiments, where advanced methods were used to increase the accuracy of the results and deeply characterize the process behavior. The results of this research allow to understand the peaberry postharvest behavior better and add significant value to this often‐underrated bean condition. The peaberries demonstrated shape influence in the different evaluated parameters, allowing them to dry faster, roast evenly, avoid burnt spots, and collapse at homogeneous forces while attaining the same high cup scores as a standard coffee bean.Practical applicationsUnderstanding the peaberries' shape and size influence different postharvest processes is crucial to comprehend their value. By gaining insights, into how these unique beans interact with stages of processing, producers can control and predict their behavior leading to more consistent and optimized results. The findings of this research provide an opportunity to fully incorporate peaberries into the coffee product without considering them as defective beans or expanding market trends. Generally, peaberries were often seen as undesirable because they did not match the bean size and shape. However, their exceptional performance in drying, roasting, grinding and cupping dispels these misconceptions, highlighting their value and contribution to coffee quality. As a result, coffee producers can embrace peaberries as valuable beans without discarding or downgrading them. This newfound appreciation for peaberries does not only reduce waste, but it also diversifies coffee offerings to meet consumer preferences and enriches the industry.

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Section: Orthogonal Dimensionssupporting

confidence: 58%

Peaberry shape and size influence on different coffee postharvest processes

Duque‐Dussán,

Figueroa‐Varela,

Sanz‐Uribe

2023

J Food Process Engineering

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“…6 ). Because normal fertilization leads to the independent development of two embryos into regular coffee beans whereas the maturation of only a single embryo generates peaberry coffee bean [ 30 ]. The characterization of lipid metabolism along flavonoid biosynthesis-associated genes could help to reveal how abortion of a single embryo from two embryos leads to peaberry coffee beans instead of regular beans.…”

Section: Resultsmentioning

confidence: 99%

“…Usually coffee fruit cherry has two embryos, their fertilization generate two independent hemispherical shape beans. However, sometimes only one embryo is further developed to yield round shape thicker bean which is commonly known as a peaberry [ 30 ]. The probability of peaberries occurrence is extremely low under normal conditions.…”

Section: Introductionmentioning

confidence: 99%

Comparative transcriptome analysis in peaberry and regular bean coffee to identify bean quality associated genes

et al. 2023

BMC Genom Data

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Background The peaberry bean in Arabica coffee has exceptional quality compared to the regular coffee bean. Understanding the molecular mechanism of bean quality is imperative to introduce superior coffee quality traits. Despite high economic importance, the regulatory aspects of bean quality are yet largely unknown in peaberry. A transcriptome analysis was performed by using peaberry and regular coffee beans in this study. Results The result of phenotypic analysis stated a difference in the physical attributes of both coffee beans. In addition, transcriptome analysis revealed low genetic differences. Only 139 differentially expressed genes were detected in which 54 genes exhibited up-regulation and 85 showed down-regulations in peaberry beans compared to regular beans. The majority of differentially expressed genes had functional annotation with cell wall modification, lipid binding, protein binding, oxidoreductase activity, and transmembrane transportation. Many fold lower expression of Ca25840-PMEs1, Ca30827-PMEs2, Ca30828-PMEs3, Ca25839-PMEs4, Ca36469-PGs. and Ca03656-Csl genes annotated with cell wall modification might play a critical role to develop different bean shape patterns in Arabica. The ERECTA family genes Ca15802-ERL1, Ca99619-ERL2, Ca07439-ERL3, Ca97226-ERL4, Ca89747-ERL5, Ca07056-ERL6, Ca01141-ERL7, and Ca32419-ERL8 along lipid metabolic pathway genes Ca06708-ACOX1, Ca29177-ACOX2, Ca01563-ACOX3, Ca34321-CPFA1, and Ca36201-CPFA2 are predicted to regulate different shaped bean development. In addition, flavonoid biosynthesis correlated genes Ca03809-F3H, Ca95013-CYP75A1, and Ca42029-CYP75A2 probably help to generate rarely formed peaberry beans. Conclusion Our results provide molecular insights into the formation of peaberry. The data resources will be important to identify candidate genes correlated with the different bean shape patterns in Arabica.

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“…The model reports an accuracy of over 80% [20]. Previous studies have shown the availability of image processing and machine learning techniques, in [21] is reported the application of techniques such as Convolutional neural networks (CNNs), Support vector machines (SVMs), and k-Nearest-neighbors (KNNs), for the classification of peaberries and normal beans. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market.…”

Section: Related Workmentioning

confidence: 99%

Automatic Tariff Classification System using Deep Learning

Cuaya-Simbro¹,

Hernandez-Vera²,

Ruiz³

et al. 2022

IJACSA

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The tariff fraction is the universal form of identifying a product. It is very useful because it helps to know the tariff that the product must pay when entering or leaving the country, in this case Mexico. Coffee is a complicated product to identify correctly due to its variants, which at first glance are not distinguishable, which can cause confusion and the tariff to be charged incorrectly. Therefore, the main objective of this project was to develop a system based on Deep Learning models, which allow to identify the tariff code of coffee to import or export this product through the analysis of digital images in real time, generating automatically a general report with this information for the customs broker. The developed system allows speeding up the process of assigning the tariff fraction, and also allows the correct assignment of the tariff fraction, avoiding confusion with other products and the wrong collection of the tariff. It is important to mention that the system, although for the moment it is focused on the country of Mexico, can be used in all customs offices since the tariff fraction is universal. The evaluation of the models was carried out with cross-validation, obtaining an effectiveness of more than 80%, and the tariff fraction assignment model had an effectiveness of 90%.

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Peaberry and normal coffee bean classification using CNN, SVM, and KNN: Their implementation in and the limitations of Raspberry Pi 3

Cited by 16 publications

References 19 publications

Peaberry shape and size influence on different coffee postharvest processes

Peaberry shape and size influence on different coffee postharvest processes

Comparative transcriptome analysis in peaberry and regular bean coffee to identify bean quality associated genes

Automatic Tariff Classification System using Deep Learning

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