Predicting Fruit Quality with a Hybrid Machine Learning and Image Processing Approach
Quick Answer
This paper shows that A hybrid approach combining image processing and CNNs predicts fruit freshness with over 90% accuracy, using logistic regression to streamline real-time classification without high computational demands.
Quick Take
This method addresses agricultural spoilage issues effectively, though it requires fruits to be isolated on specific backgrounds.
Key Points
- Developed an image processing algorithm to quantify fruit spoilage from 0 to 100.
- Trained a CNN for binary classification of fresh versus rotten fruit images.
- Achieved over 90% accuracy on a dataset of apples and oranges.
- Eliminated the need for CNN in real-time applications using logistic regression.
- Future improvements may include advanced segmentation for background removal.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | 22 pages, 13 figures, 2 tables |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| ACM classes: | I.4.8; I.2.10; I.5.4; I.2.6 |
| Cite as: | arXiv:2606.26165 [cs.CV] |
| (or arXiv:2606.26165v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26165 arXiv-issued DOI via DataCite |
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| Journal reference: | University of Michigan Undergraduate Research Journal 18: 20 (2026) |
| Related DOI: | https://doi.org/10.3998/umurj.9836
DOI(s) linking to related resources |
Submission history
From: Amir Reza Hashemi [view email]
[v1]
Wed, 24 Jun 2026 07:16:14 UTC (5,006 KB)
— Originally published at arxiv.org
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