Ripening stage classification of Coffea arabica L. var. Castillo using a Machine learning approach with the electromechanical impedance measurements of a contact device

Pineda, Maria F.; Tinoco, Héctor A.; Lopez-Guzman, Juliana; Perdomo-Hurtado, Luis; Cardona, Carlos I.; Rincon-Jimenez, Alexander; Betancur-Herrera, Natalia

doi:10.1016/j.matpr.2022.04.669

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…A bifactorial experimental design in blocks was considered with three replications per variety; the first factor was the variety (C1 and RC), while the second factor was the moisture content (% wb): 53%, 42%, 32%, 22% and 11% with a ± 1% error margin. The block was defined as the experimental unit: the coffee load, three per variety, each composed of 50 kg of coffee berries at ripe stages 4, 5 and 6 (Pineda et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Thermophysical properties of parchment coffee: New Colombian varieties

Duque‐Dussán

Sanz‐Uribe²,

Lubert

et al. 2023

J Food Process Engineering

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The thermophysical properties of coffee have a special partaking during the drying process since a material‐depending heat and mass transfer occurs between the bean and the drying air. Conditional to the thermophysical properties of the parchment coffee, the drying can be more or less efficient, affecting the final quality and seed safety. Several coffee varieties have been studied; however, the National Coffee Research Center of Colombia has developed new highly productive coffee varieties resistant to different diseases: Cenicafé 1 and Castillo®. Nevertheless, the thermophysical properties of these specific varieties were not yet investigated; moreover, the availability of information related to these properties of different coffee varieties in the literature is relatively scarce. Thus, this study targeted to determine the parchment coffee thermophysical properties of these new varieties at five different moisture contents % (wb): 53%, 42%, 32%, 22% and 11%, using optimized techniques and methods to ensure high accuracy and exactness. It was found that the new varieties have larger, heavier, and denser beans; it was also seen that the bulk thermal conductivity and the bulk‐specific heat are higher in these varieties than in the older ones. It was also revealed that the length, width, thickness, and surface area did not change as the moisture was removed, whereas the bulk density, kernel density, mass, bulk‐specific heat, and bulk thermal conductivity decreased as the moisture was reduced. Displaying better thermophysical properties will improve the drying and roasting processes; hence, a better final product can be expected from these varieties. Practical applications Knowing the thermal and physical properties of these new varieties will allow the growers and coffee processing facilities to predict, simulate and control different post‐harvesting processes such as pulping, fermentation, drying, storing, and roasting. Also, the already developed mathematical models to estimate coffee drying times can be updated, improving the accuracy of the predictions, bed porosities, mass, and heat transfer in order to safeguard the innocuousness of the product.

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Section: Methodsmentioning

confidence: 99%

Thermophysical properties of parchment coffee: New Colombian varieties

Duque‐Dussán

Sanz‐Uribe²,

Lubert

et al. 2023

J Food Process Engineering

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“…Cenicafé 1 was used throughout all the experiments. Forty kilograms of coffee beans at ripe stages 4, 5 and 6 (Pineda et al, 2022) were picked from the center's main experimental field Naranjal (GPS Coordinates: 4.972250, −75.652547) in the municipality of Chinchiná. The ripeness of the coffee berries varies from 1 to 8, where 1, 2, and 3 stand for unripe (green) berries, 4, 5, and 6 ripe stages, and 7 and 8 depict overripe (Peñuela‐Martínez, Guerrero & Sanza‐Uribe, 2022).…”

Section: Methodsmentioning

confidence: 99%

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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“…Coffee demand has increased along with the demand for high-quality products. The supply of high-quality coffee is attributed mainly to improvements in selective harvesting, preferably of ripe fruits (Pineda et al, 2022). Enhancing selective harvest has allowed for the emergence of special products.…”

Section: Introductionmentioning

confidence: 99%

Detection of coffee fruits on tree branches using computer vision

Bazame

Molin

Althoff

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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Ripening stage classification of Coffea arabica L. var. Castillo using a Machine learning approach with the electromechanical impedance measurements of a contact device

Cited by 4 publications

References 31 publications

Thermophysical properties of parchment coffee: New Colombian varieties

Thermophysical properties of parchment coffee: New Colombian varieties

Peaberry shape and size influence on different coffee postharvest processes

Detection of coffee fruits on tree branches using computer vision

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