Development of Apple Fruit Classification System using Convolutional Neural Network (CNN) MobileNet Architecture on Android Platform - Masparudin, Masparudin; Fitri, Iskandar; Sumijan, Sumijan (Sistemasi: Jurnal Sistem Informasi, 2024)
In the current digital era, image classification of fruits, particularly apples, has become crucial for various applications, ranging from agriculture to retail. This research focuses on the utilization of Convolutional Neural Network (CNN) with the MobileNet architecture to classify apple fruit images. Using the Python programming language, three models were successfully trained: Model 1 for apple fruit types, Model 2 for apple fruit diseases, and Model 3 for apple fruit ripeness levels. All three models underwent training and validation, with the final results at epoch 10: Model 1 for apple types achieved an accuracy of 100% and a loss of 0.0046, Model 2 for apple diseases achieved an accuracy of 100% and a loss of 0.0075, while Model 3 for apple ripeness levels achieved an accuracy of 99.76% and a loss of 0.0439. Subsequently, these models were tested on an Android device, and there were two testing scenarios. In the first scenario, each model was tested with 15 images individually. The results showed 100% accuracy for Models 1 and 2, while Model 3 achieved a lower accuracy of 86.67%. In the second scenario, all three models were tested simultaneously using 30 test images, resulting in an accuracy of 55.55%. Several factors, such as limitations in the apple image dataset, particularly in the ripeness dataset, object backgrounds, image capture distances, color and texture similarities, as well as lighting quality, influenced the classification outcomes. To enhance future performance, improved data preprocessing and a combination of detection and classification techniques are needed. This research provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.