Classification of Fresh Cocoa Beans with Pulp Based on Computer Vision

Ona, Angel J. Ona; Grijalva, Felipe; Proano, Kevin; Acuna, Byron; García, Marcelo Monge

doi:10.1109/andescon50619.2020.9272188

Cited by 2 publications

(2 citation statements)

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“…The method involves defect identification and classification using CNN techniques, followed by grade level determination using ANFIS techniques that combine artificial neural networks and fuzzy logic systems. [18] proposed a new method for classifying cocoa beans' pulp using computer vision techniques like k-means clustering, morphological operations, a bag of visual words, and a support vector machine classifier. This method aids in removing the pulp and efficiently estimates the quality of cocoa beans, dividing them into excellent and poor-quality classes.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…This approach streamlines quality assessment, improves efficiency, and reduces costs without sample destruction. Classification of cacao variety 1 [3] Quality Assessment Morphological features 15 [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Fermentation Fermentation level 8 [26,27,[29][30][31][32][33]35] Fermentation index 1 [28] Cacao Bean's Roasting Degree 2 [34,36] [36] demonstrated continuous monitoring of volatile compounds during cocoa refining using a gas sensor system. Trained Kernel Distribution Models (KDMs) characterized volatile profiles, identifying deviations in processing variables.…”

Section: Fermentationmentioning

confidence: 99%

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A review of machine learning applications in Cacao Post-harvest management

Zhaira Marianne Ferrancol Ferran,

Luisa Ann Tamayo Ongquit,

Edwin Romeroso Arboleda

2024

Int. J. Sci. Res. Arch.

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Cacao beans play a crucial role in chocolate production, making them a highly relevant crop. The post-harvesting stage of cacao beans, encompassing classification, quality assessment, and fermentation, holds significant importance. With the increasing demand for marketable cacao beans, the need for reliable, accurate, and fast technologies has emerged. This study provides a comprehensive review of machine learning techniques in the post-harvesting stage of cacao beans from 2016 to the present. Analyzing 36 studies, it focuses on classification, quality assessment, and fermentation. The proposed framework includes the application domain, learning algorithms, performance metrics, and reported impacts. Notably, it explores various machine learning applications like classification, quality assessment, and fermentation, highlighting commonly used algorithms like ANN, CNN, and SVM. In terms of performance metrics, GLCM achieved the highest accuracy (99.61%) in cacao classification, ANFIS excelled in quality assessment (99.715%), and k-NN emerged as the most accurate for fermentation. This review serves as a valuable resource for researchers in the cacao bean sector, offering insights into machine learning advancements.

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Section: Quality Assessmentmentioning

confidence: 99%