Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism
Fruit freshness monitoring represents one of the key research foci in the quality control of fruits and vegetables. Traditional manual inspection methods are characterized by subjectivity and inefficiency, which renders them unsuitable for large-scale and real-time detection demands. Automated detec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/11/1987 |
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| author | Yuan Shu Jipeng Zhang Yihan Wang Yangyang Wei |
| author_facet | Yuan Shu Jipeng Zhang Yihan Wang Yangyang Wei |
| author_sort | Yuan Shu |
| collection | DOAJ |
| description | Fruit freshness monitoring represents one of the key research foci in the quality control of fruits and vegetables. Traditional manual inspection methods are characterized by subjectivity and inefficiency, which renders them unsuitable for large-scale and real-time detection demands. Automated detection methods based on deep learning have increasingly attracted attention. In this study, a fruit freshness classification method based on the ResNet-101 network and a Non-local Attention mechanism is proposed. By embedding a Non-local Attention module into ResNet-101, subtle surface feature variations of the fruit are captured, thereby enhancing the model’s capacity to identify rotten areas and detect variations in color under complex backgrounds. Experimental results show that the improved model achieves a precision of 94.7%, a recall of 94.24%, and an F1-score of 94.24%, outperforming conventional ResNet-101, ResNet-50, and VGG-16 models. In particular, under complex environmental conditions, the model demonstrates significantly improved robustness in image processing. The combination of the Non-local Attention mechanism with the ResNet-101 model can substantially enhance the accuracy and stability of fruit freshness detection, which is applicable to real-time monitoring tasks in intelligent agriculture and smart logistics. |
| format | Article |
| id | doaj-art-e93d1f3872d94e52bc43de2eae0ef6d7 |
| institution | OA Journals |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-e93d1f3872d94e52bc43de2eae0ef6d72025-08-20T02:33:08ZengMDPI AGFoods2304-81582025-06-011411198710.3390/foods14111987Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention MechanismYuan Shu0Jipeng Zhang1Yihan Wang2Yangyang Wei3Architecture and Design College, Nanchang University, Nanchang 330031, ChinaArchitecture and Design College, Nanchang University, Nanchang 330031, ChinaSchool of Art, Wuhan Business University, Wuhan 430056, ChinaArchitecture and Design College, Nanchang University, Nanchang 330031, ChinaFruit freshness monitoring represents one of the key research foci in the quality control of fruits and vegetables. Traditional manual inspection methods are characterized by subjectivity and inefficiency, which renders them unsuitable for large-scale and real-time detection demands. Automated detection methods based on deep learning have increasingly attracted attention. In this study, a fruit freshness classification method based on the ResNet-101 network and a Non-local Attention mechanism is proposed. By embedding a Non-local Attention module into ResNet-101, subtle surface feature variations of the fruit are captured, thereby enhancing the model’s capacity to identify rotten areas and detect variations in color under complex backgrounds. Experimental results show that the improved model achieves a precision of 94.7%, a recall of 94.24%, and an F1-score of 94.24%, outperforming conventional ResNet-101, ResNet-50, and VGG-16 models. In particular, under complex environmental conditions, the model demonstrates significantly improved robustness in image processing. The combination of the Non-local Attention mechanism with the ResNet-101 model can substantially enhance the accuracy and stability of fruit freshness detection, which is applicable to real-time monitoring tasks in intelligent agriculture and smart logistics.https://www.mdpi.com/2304-8158/14/11/1987ResNet-101Non-local Attentionfruit freshness detectiondeep learningimage processingintelligent agriculture |
| spellingShingle | Yuan Shu Jipeng Zhang Yihan Wang Yangyang Wei Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism Foods ResNet-101 Non-local Attention fruit freshness detection deep learning image processing intelligent agriculture |
| title | Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism |
| title_full | Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism |
| title_fullStr | Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism |
| title_full_unstemmed | Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism |
| title_short | Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism |
| title_sort | fruit freshness classification and detection based on the resnet 101 network and non local attention mechanism |
| topic | ResNet-101 Non-local Attention fruit freshness detection deep learning image processing intelligent agriculture |
| url | https://www.mdpi.com/2304-8158/14/11/1987 |
| work_keys_str_mv | AT yuanshu fruitfreshnessclassificationanddetectionbasedontheresnet101networkandnonlocalattentionmechanism AT jipengzhang fruitfreshnessclassificationanddetectionbasedontheresnet101networkandnonlocalattentionmechanism AT yihanwang fruitfreshnessclassificationanddetectionbasedontheresnet101networkandnonlocalattentionmechanism AT yangyangwei fruitfreshnessclassificationanddetectionbasedontheresnet101networkandnonlocalattentionmechanism |