Mango Ripeness Classification Using Deep Learning-Based Convolutional Neural Network Models
DOI:
https://doi.org/10.57152/predatecs.v4i1.2111Keywords:
Convolutional Neural Network, Data Augmentation, Deep Learning, Mango Ripeness, ResNet50V2Abstract
Manual assessment of mango ripeness is often subjective, time-consuming, and labor-intensive, making it inefficient for large-scale applications. This study investigates a deep learning–based approach for mango ripeness classification using Convolutional Neural Networks (CNN) and a publicly available image dataset obtained from Kaggle. Three CNN architectures, ResNet50V2, InceptionV3, and MobileNetV2, were evaluated using transfer learning and optimized with Adam and RMSprop to classify mangoes as unripe, ripe, or rotten. Data augmentation techniques were applied to improve model generalization and reduce overfitting. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that all models achieved competitive performance, with ResNet50V2 obtaining the highest test accuracy of 81.8%, while InceptionV3 demonstrated more stable validation results. These findings indicate that CNN-based transfer learning is effective for mango ripeness classification and can serve as a baseline reference for further development of automated fruit classification systems.
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