Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model
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Date
2023-07
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Elseviere
Abstract
Cut-Test technique employs visual inspection of interior coloration, compartmentalization, and defects of beans
for effective classification of cocoa beans. However, due to its subjective nature and natural variations in
visual perception, it is intrinsically limited, resulting in disparities in verdicts, imprecision, discordance, and
time-consuming and labor-intensive classification procedures. Machine Learning (ML) techniques have been
proposed to address these challenges with significant results, but there is still a need for improvement. In this
paper, we propose a color and texture extraction technique for image representation, as well as a generalized,
less complex Neural Network model, to help improve the performance of machine classification of Cut-Test
cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa
cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method
combined with the developed Artificial Neural Networks provides a more homogeneous classification rate for
all grades, obtaining 85.36%, 85%, 83%, and 83% for accuracy, precision, recall, and F1 measure, respectively.
The proposed model outperforms other ML models, such as Support Vector Machines, Decision Trees, Random
Forests, and Naïve Bayes, on the same dataset. Additionally, the proposed ANN model demonstrates relatively
better generalization when compared with earlier work by Santos on the same dataset. The proposed techniques
in this work are robust on the cut-test dataset and can serve as an accurate Computer-Aided Diagnostic tool
for cocoa bean classification.
Description
This article is published by Elsevier, 2023 and is also available at https://doi.org/10.1016/j.engappai.2023.106736
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Citation
Engineering Applications of Artificial Intelligence 125 (2023) 106736