Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production
Problem
Apple defect detection and quality grading in agricultural production requires automated, accurate monitoring of fruit quality.
Why it matters
Automated quality monitoring affects efficiency and accuracy of apple production.
What the AI did
A deep learning system integrating image-processing and machine learning algorithms performs apple defect segmentation and quality grading; ablations showed Jump Loss improves performance.
Results
Defect segmentation: 93% precision, 90% recall, 91% accuracy, 92% mIoU; grading: 91% precision, 90% recall and accuracy.
Impact
Outperformed U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet baselines on the authors' experiments.
Record
| Venue | Agriculture |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Xiaotong Gao, Songwei Li, Xiaotong Su, Yan Li, Lingyun Huang, Weidong Tang, Yuanchen Zhang, Min Dong |
| Citations | 28 (OpenAlex, at import time) |
| References | 48 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/agriculture14071098 |
| Open access | gold |
| Keywords | apple defect detection, quality grading, deep learning, image segmentation, Jump Loss |
Importance rating
real but narrower advance
Abstract claims potential to automate detection and grading in the apple industry and modernize agricultural production; a real but sector-specific advance.
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