Implementation of Convolution Neural Networks for Classifying the Ripeness of Guava Fruit on Android

Mochammad Andika Putra Mubarok Mubarok; Budi Nugroho; Yisti Vita Via

doi:10.56480/jln.v5i3.1601

Authors

Mochammad Andika Putra Mubarok Universitas Pembangunan Nasional Veteran, Jawa Timur, Surabaya, Indonesia
Budi Nugroho Universitas Pembangunan Nasional Veteran, Jawa Timur, Surabaya, Indonesia
Yisti Vita Via Universitas Pembangunan Nasional Veteran, Jawa Timur, Surabaya, Indonesia

DOI:

https://doi.org/10.56480/jln.v5i3.1601

Abstract View:

32

PDF downloads:

35

Keywords:

Classification of guava ripeness, CNN, VGG16, EfficientNetB0, Android

Abstract

Determining the ripeness level of guava manually is often subjective and requires specific expertise. To address this issue, this study developed an Android-based system to classify guava ripeness using two Convolutional Neural Network (CNN) architectures: VGG16 and EfficientNetB0. The dataset includes images of guava categorized into three ripeness levels: unripe, semi-ripe, and ripe. Both CNN models were implemented and compared based on accuracy, computational efficiency, and inference time after being converted into TensorFlow Lite format for Android integration. Test results show that EfficientNetB0 performs better for mobile use, achieving 93.5% accuracy and faster average inference time than VGG16. This system is expected to help farmers and consumers identify guava ripeness quickly, easily, and accurately using an Android device.

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