Manuscript received February 17, 2022; revised April 5, 2022; accepted August 3, 2022.
Abstract—In Thailand, the pineapple is a valuable crop
whose price is determined by its sweetness. An optical
refractometer or another technique that requires expert
judgment can be used to determine a fruit's sweetness.
Furthermore, determining the sweetness of each fruit takes
time and effort. This study employed the Alexnet deep
learning model to categorize pineapple sweetness levels based
on physical attributes shown in images. The dataset was
classified into four classes, i.e., M1 to M4, and sorted in
ascending order by sweetness level. The dataset was divided
into two parts: training and testing datasets. Training
accounted for 80% of the dataset while testing accounted for
20%. This study's experiments were repeated five times, each
with a distinct epoch and working with data that had been
prepared. According to the experiment, the Alexnet model
produced the greatest results when trained with balancing
data across 10 epochs and 120 figures per class. The model's
accuracy and F1 score were 91.78% and 92.31%, respectively.
Keywords—pineapple sweetness, deep learning, Alexnet, data
augmentation, balanced data, fruit classification
Cite: Sarunya Kanjanawattana*, Worawit Teerawatthanaprapha, Panchalee Praneetpholkrang, Gun Bhakdisongkhram, and Suchada Weeragulpiriya, "Pineapple Sweetness Classification Using Deep Learning Based on Pineapple Images," Journal of Image and Graphics, Vol. 11, No. 1, pp. 47-52, March 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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