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Researchers from Faridpur Engineering College in Bangladesh have developed AI models to automate mango ripeness classification, significantly improving accuracy over manual sorting methods. The study revealed that Convolutional Neural Networks (CNNs) outperformed traditional machine learning techniques, achieving a remarkable accuracy of 96.28% with Gradient Boosting. This advancement is crucial for Bangladesh's mango export industry, which relies heavily on accurate ripeness classification to minimize post-harvest losses.
The research highlights the inefficiencies of manual sorting, which is subjective and prone to errors, leading to financial losses for exporters. By utilizing a dataset of 975 images of the 'Himsagor' mango variety, the study demonstrated the potential of AI to enhance sorting processes. The findings suggest that AI-driven classification systems could revolutionize the agricultural sector, ensuring better quality control and reducing waste.
• AI models significantly improve mango ripeness classification accuracy.
• Convolutional Neural Networks outperform traditional machine learning methods.
CNNs are deep learning models that autonomously learn to recognize image features, proving effective in mango classification.
Gradient Boosting is a machine learning technique that achieved high accuracy in classifying mango ripeness.
Transfer Learning involves using pre-trained models like VGG16, but performed below CNNs in this study.
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