Apple quality sorting based on deep learning
Sorting apples for quality is a complex process that requires accurate classification of defects. Greefa conducted a validation test to evaluate the applicability of deep learning in automatically recognizing and distinguishing defects. By using artificial intelligence, the sorting process can be performed more efficiently and accurately, with less manual control.
Innovation package, use case and type test
Voedselverwerking
Dark Fruit Technology
Status: completed
Technical functionality
Broad knowledge question
How accurately can deep learning classify fruit?
This research focuses on the extent to which deep learning is suitable for classifying fruit and distinguishing different defects. The test assesses whether this technology can contribute to a more stable and efficient sorting process.
Approach
Testing deep learning in a realistic environment
The validation test took place on a sorting machine at FruitMasters in Geldermalsen. For this test, 1,400 Kanzi apples were selected by two inspectors to create a representative set of possible defects. The apples were then sorted by type, with each defect being assigned to a specific class. The test results were recorded and compared both manually and by the sorting system.
Goal
Comparison with existing sorting methods
The goal of this validation test is to evaluate the deep learning method in a controlled but realistic setting. In addition, the new method is compared to existing sorting techniques to determine whether deep learning provides improved accuracy and efficiency.
Result and reflection
Better detection and more stable sorting results
The deep learning method proves highly accurate in distinguishing apples with and without defects.
Successes
The system achieved high specificity (>99%) and sensitivity (>94%) in defect classification.
Deep learning can better differentiate between various defect types than traditional sorting methods.
The method works independently of apple variety, enabling broader applicability.
Lessons learned
Test results, based on a wide range of defects, show low variation (0.5%–2.5%).
Repeating the test with the same apple variety is not expected to produce significant differences.
Less manual reinspection is needed, potentially leading to a more efficient and labor-saving sorting process.