# Automated Mango Variety Classification Using Deep Feature Extraction and Machine Learning Classifier Integration

## TL;DR

This paper presents an AI system that automatically identifies mango varieties with high accuracy and speed, reducing manual effort and post-harvest losses.

## Contribution

A novel hybrid framework combining deep learning and machine learning for efficient mango variety classification is introduced.

## Key findings

- Hybrid models achieved 100% test accuracy for mango variety classification.
- EfficientNetB0–LDA and ResNet50–Logistic Regression reduced inference time by up to 330 times.
- The framework offers state-of-the-art accuracy with lower computational cost.

## Abstract

Manual mango variety classification is time-consuming, error-prone, and contributes significantly to post-harvest losses in developing economies. This study aims to develop a computationally efficient and highly accurate artificial intelligence framework for automated mango variety classification suitable for real-time applications. Eight deep transfer learning models were evaluated as feature extractors and combined with ten classical machine-learning classifiers. Model performance was assessed using accuracy, log loss, memory usage, training time, and inference latency. The hybrid models EfficientNetB0–Linear Discriminant Analysis (LDA) and ResNet50–Logistic Regression achieved 100% test accuracy while reducing inference time by up to 330 times compared to full Convolutional Neural Network (CNN) models. These findings demonstrate that hybrid deep-learning and machine-learning architectures can deliver state-of-the-art accuracy with substantially lower computational cost. Future research will focus on large-scale real-world validation and embedded hardware deployment for industrial fruit sorting systems.