Automated Fruit Nutrition Classification Using MobileNet-Based Convolutional Neural Networks on Deep Learning
Year: 2025
Type: conference-paper
Abstract: Indonesia boasts diverse tropical fruits like ciplukan, harendong, and kecapi, which are nutrient-rich but underutilized. To address this, an automated fruit recognition system was developed using Convolutional Neural Network (CNN) with MobileNet architecture, leveraging Depthwise Separable Convolution (DSC) for efficiency. The model was trained on 5000 images of five local fruits (224 × 224 pixels), split into 70% training, 20% validation, and 10% testing. Optimized for Android, the system enables real-time identification with minimal hardware requirements (e.g., 2 GB RAM for low-end devices). Evaluation metrics (accuracy, precision, recall) achieved 97.43% accuracy, demonstrating MobileNet’s effectiveness. This study highlights deep learning’s potential in preserving and promoting Indonesia’s indigenous fruits.
Institution: Nusa Putra University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414979491