FruitSense: an android-based fruit quality detection mobile application using machine learning and image processing

Diva, Evelyn Levina and Shah, Umm E Mariya and Mohammed Abdullah, Samar Ghazal and Rajagopal, Heshalini and Subaramaniam, Kasthuri and Mehmood, Yasir and Mahmood, Atif (2026) FruitSense: an android-based fruit quality detection mobile application using machine learning and image processing. In: 31st International Conference on Artificial Life and Robotics 2026 - Oita, Japan., Oita, Japan.

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Abstract

Fruit quality is crucial for human nutrition and health, however, traditional visual inspection for fruit freshness is time-consuming and inconsistent. The growing adoption of AI offers new possibilities for improving traditional methods. Accordingly, FruitSense was developed to classify the ripeness of bananas, apples, and tomatoes using machine learning techniques. Implemented with TensorFlow Lite and Google Teachable Machine, it analyzes color, defects, and size. A diverse dataset of fruit images representing various ripeness stages was obtained from Kaggle to train separate models for each fruit. The models were integrated into an Android app for real-time ripeness detection. Evaluation of the application through system, usability, and performance testing revealed that it meets the project objectives and was positively received.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	Machine Learning, deep learning, image processing, Support Vector Machine (SVM), fruit quality detection, mobile application, agricultural technology
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Economics and Management Sciences > Department of Finance Kulliyyah of Economics and Management Sciences
Depositing User:	Dr Umm e Mariya Shah
Date Deposited:	04 May 2026 17:05
Last Modified:	04 May 2026 17:05
Queue Number:	2026-04-Q3133
URI:	http://irep.iium.edu.my/id/eprint/128747

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