Predicting Fruit Quality with a Hybrid Machine Learning and Image Processing Approach

Researchers developed a hybrid approach combining image processing and deep learning to assess fruit freshness, achieving over 90% accuracy in real-time classification of apples and oranges. The method uses a CNN for binary classification and an image processing algorithm for spoilage quantification, synthesized via logistic regression to enable real-time use without the CNN. The approach requires fruits to be isolated on a white or transparent background, with future improvements planned for automated background removal.

arXiv:2606.26165v1 Announce Type: new Abstract: Fruit spoilage is a significant issue in agriculture, leading to substantial economic losses. Addressing this, our study introduces a hybrid approach combining image processing and deep learning to assess fruit freshness. We developed an image processing algorithm that quantifies spoilage on a scale from 0 fully fresh to 100 fully rotten . Alongside, we trained a convolutional neural network CNN to perform binary classification fresh or rotten using a large dataset of fruit images. The outcomes of both methods were synthesized using logistic regression to enhance the accuracy of freshness predictions. Subsequently, this logistic regression model was utilized to enable the image processing algorithm to provide binary classification based on its percentage output, thus eliminating the need for the CNN in real-time applications. Our approach, which does not require high computational resources, achieved real-time performance and was validated with over 90% accuracy on a dataset comprising apples and oranges. The primary limitation lies in the requirement for fruits to be isolated on a background that must be either white or transparent, suggesting future improvements could include advanced segmentation models to automate background removal. This study's results highlight the potential of integrating simple image processing techniques with machine learning to provide practical solutions in the agricultural sector.