Vit-CNN Fusion for Robust Mango Quality Evaluation based on Classification Across Multiple Public Datasets

Mango quality is crucial for economic viability, waste reduction and public health, yet traditional assessment methods often suffer from subjectivity, inefficiency and can be destructive. Existing deep learning approaches, specifically convolutional neural networks (CNNs), achieve higher accuracies but are unable to generalise across diverse datasets because they cannot capture contextual features.

To address these limitations, this research explores the application of machine learning algorithms, particularly deep learning techniques such as convolutional neural networks (CNNs) and Vision Transformers (ViTs), for an objective and efficient mango classification task. The research work concentrated on two publicly available datasets viz. Mango Ripeness Dataset, Mango Classification Dataset based on Mango. A comparative study of these datasets is performed to analyse their performance when fed to variants of CNNs, such as ResNet, MobileNet and ShuffleNet. By leveraging image analysis and feature extraction, this study aims to study the behaviour of different variants of the convolutional neural network on different datasets related to mangoes.

Comparative experiments with CNN-ViT fusion achieve superior accuracy (98.19% on the Mango Ripeness Dataset and 100% on the Mango Classification Dataset), consistently. Additional ablation studies and cross-dataset validation confirm the robustness and scalability of the approach. This work establishes a reproducible framework for automated mango quality evaluation, paving the way for practical deployment in agricultural quality control and supply chain management.