Abstract
In this study, an automatic orange grading detection method based on computer vision was proposed. First, the images of orange were collected and preprocessed. Secondly, orange and background are segmented by edge detection and image segmentation. Based on the image segmentation, four main features of orange include fruit surface color, fruit size, fruit surface defect and fruit shape were extracted. These features were studied using BP neural network. Finally, the automatic orange grading detection was performed by using neural network. The experimental results showed that the grading accuracy of this grading method was 94.38% and the classification accuracy of the first grade is 100%. Compared with the artificial method, it has the features of high correct identification rate and good real-time performance.
Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 3
Claim · model-written summary
The orange grading method achieved 94.38% accuracy.
Verbatim
e0“the grading accuracy of this grading method was 94.38%”
t0“Oranges”
Claim · model-written summary
First-grade orange classification accuracy was 100%.
Verbatim
e0“the classification accuracy of the first grade is 100%”
t0“Oranges”
Claim · model-written summary
Compared with manual grading, the method had a high correct identification rate and good real-time performance.
Verbatim
e0“Compared with the artificial method, it has the features of high correct identification rate and good real-time performance.”
t0“Oranges”