IIUM Repository

Detection of sweetness level for fruits (watermelon) with machine learning

Wan Nazulan, Wan Nurul Suraya and Asnawi, Ani Liza and Mohd Ramli, Huda Adibah and Jusoh, Ahmad Zamani and Ibrahim, Siti Noorjannah and Mohamed Azmin, Nor Fadhillah (2020) Detection of sweetness level for fruits (watermelon) with machine learning. In: 2020 IEEE Conference on Big Data and Analytics (ICBDA), 17-19 Nov. 2020, Kota Kinabalu, Malaysia (Online Conference).

[img] PDF - Published Version
Restricted to Registered users only

Download (2MB) | Request a copy
PDF (scopus) - Supplemental Material
Download (299kB) | Preview


The inspection and grading of the watermelon are done manually but it is a tedious job and it is difficult for the graders to maintain constant vigilance. Thus, the image processing has widely been used for identification, detection, grading and quality evaluation in the agricultural field. The objective of this work is to investigate the sweetness parameter for the fruit’s detection and classification algorithm in machine learnings. This study applies image processing techniques to detect the color and shape of watermelon’s skin for grading based on the sweetness level using K-means clustering method via the Python platform. 13 samples of watermelon images are used to test the functionality of the proposed detection system in this study. Then, each watermelon is grouped into Grade A (high level of sweetness), Grade B (medium level of sweetness), and Grade C (low level of sweetness) based on its color and shape detection results. At the end of this research, the proposed technique resulted in an inaccurate prediction for 2 watermelon samples out of 13 samples which indicates the system has an 84.62% accuracy in detecting the watermelon sweetness level.

Item Type: Conference or Workshop Item (Invited Papers)
Additional Information: 4327/86522
Uncontrolled Keywords: fruits detection, watermelon, sweetness level, machine learning, k-mean clustering
Subjects: T Technology > T Technology (General) > T10.5 Communication of technical information
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: DR. Ani Liza Asnawi
Date Deposited: 29 Dec 2020 22:30
Last Modified: 24 Mar 2021 11:52
URI: http://irep.iium.edu.my/id/eprint/86522

Actions (login required)

View Item View Item


Downloads per month over past year