Classification of Green Apple Varieties using Convolutional Neural Network based on RGB Color with MobileNetV2 - Alnofri Rano Masiku; Nelson Nainggolan; Siska Ayu Widiana; Mahardika Inra Takaendengan (Universitas Teknokrat Indonesia, 2026)
Manual classification of green apple varieties is often time-consuming, labor-intensive, and prone to human subjectivity. This research aims to develop an automated classification model for green apple types based on RGB color features using Convolutional Neural Network (CNN) with MobileNetV2 architecture. The dataset comprises 1,170 images of three green apple varieties: Golden Delicious, Granny Smith, and Manalagi. Image preprocessing steps include cropping, resizing, background removal, and RGB conversion to enhance feature extraction. The model training and evaluation utilize 5-fold Cross Validation to ensure robustness and generalization. Experimental results demonstrate that the proposed model achieves an average accuracy of 96%, precision of 96.33%, recall of 96.33%, and F1-Score of 96.33%. Furthermore, the model is implemented in a web-based application using the Flask framework to predict apple varieties from input images. Testing on new images shows classification confidence levels of 80.92% for Granny Smith, 87.38% for Manalagi, and 78.43% for Golden Delicious apples. This study confirms that CNN with MobileNetV2 and RGB color features effectively classifies green apple varieties, offering practical implications for agricultural automation and quality control.