Non-Destructive Classification of Ataulfo Mango Ripeness Using Color Images and Machine Learning

Automatic classification of Ataulfo mango (Mangifera indica L.) ripeness is essential to ensure consistent quality, standardize post-harvest processes, and reduce the subjectivity of traditional visual inspection, which is unreliable and error-prone. This paper aims to develop a computationally efficient image classification system for Ataulfo mango ripeness by combining explicit color and texture feature extraction with a traditional machine learning model, thereby reducing the high computational costs typically associated with deep convolutional architectures. For this purpose, a dataset containing 10,400 images was created and divided into four maturity categories: green-ripe, partially ripe, firm-ripe, and soft-ripe. We select an optimal Multilayer Perceptron trained on compact 33-dimensional feature vectors and compare its performance with classical machine learning algorithms and pretrained deep neural networks, including MobileNetV2, MobileNetV3, and ResNet18. Our proposal achieves an accuracy of 0.8821, a macro-F1 score of 0.8784, and an AUC of 0.9751, which are better than those of classical classifiers and MobileNet-family models, while reducing computational cost by three orders of magnitude (GFLOPs). The ResNet18 model achieved a 3.56% relative improvement in macro-F1 score compared to our proposal, but its computational cost increased by four orders of magnitude in GFLOPS. In all evaluated architectures, the remaining classification errors occur between adjacent maturity stages and likely reflect the visual similarity inherent in the continuous ripening process. These findings demonstrate that manual feature engineering and model selection via hyperparameter tuning remain highly competitive and more sustainable for low-cost edge implementations in agriculture.