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.

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