Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation
Real-time, on-device segmentation of rubber tree powdery mildew is crucial for smart agriculture, yet existing deep learning models often fail to balance accuracy with computational efficiency. While large models can be precise, their high complexity hinders deployment on terminal devices; conversel...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525005507 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849228333840072704 |
|---|---|
| author | Yuheng Li Lisha Ye Yu Zhang Lijuan Zhou Qian Chen |
| author_facet | Yuheng Li Lisha Ye Yu Zhang Lijuan Zhou Qian Chen |
| author_sort | Yuheng Li |
| collection | DOAJ |
| description | Real-time, on-device segmentation of rubber tree powdery mildew is crucial for smart agriculture, yet existing deep learning models often fail to balance accuracy with computational efficiency. While large models can be precise, their high complexity hinders deployment on terminal devices; conversely, smaller models often struggle to identify small, multi-scale disease spots in complex backgrounds. To bridge this gap, this study develops and validates light-PMSeg, a lightweight instance segmentation framework designed for accurate, real-time disease grading. Our contributions are fourfold: First, we establish a high-performance baseline, PMSeg, which integrates a Mixed Aggregation Network (MANet) and a Channel-wise Cross Fusion Transformer (CCFT) to significantly enhance sensitivity to small targets and multi-scale feature extraction. Second, to achieve on-device feasibility, we systematically compress PMSeg into light-PMSeg through a novel combination of slimming pruning and knowledge distillation (KD), which restores performance after parameter reduction. Third, we constructed a rubber tree powdery mildew segmentation dataset PM-Dataset containing 6,200 images. Finally, we propose an automatic grading algorithm based on the segmentation masks to provide actionable disease severity levels. Experimental results demonstrate the superiority of our framework. The final light-PMSeg model achieves a mAP50 (Mask) of 90.1% and an F1-score of 85.4% at a speed of 148.6 FPS. Crucially, this is achieved while reducing the model's parameters by 79.6% compared to the unpruned PMSeg, confirming its suitability for deployment on low-storage devices. This study delivers a validated, end-to-end solution that effectively reconciles high segmentation accuracy with a lightweight architecture, presenting a practical and robust method for intelligent powdery mildew monitoring. |
| format | Article |
| id | doaj-art-e5a189a2e723425ba274e5a6aeb302f2 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-e5a189a2e723425ba274e5a6aeb302f22025-08-23T04:49:52ZengElsevierSmart Agricultural Technology2772-37552025-12-011210131910.1016/j.atech.2025.101319Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillationYuheng Li0Lisha Ye1Yu Zhang2Lijuan Zhou3Qian Chen4School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China; Key Laboratory of Internet Information Retrieval of Hainan Province, Haikou 570228, ChinaSchool of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China; Key Laboratory of Internet Information Retrieval of Hainan Province, Haikou 570228, ChinaSchool of Tropical Agriculture and Forestry, Hainan University, Danzhou 571737, China; Sanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, ChinaSchool of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China; Key Laboratory of Internet Information Retrieval of Hainan Province, Haikou 570228, China; Corresponding authors at: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China.School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China; Key Laboratory of Internet Information Retrieval of Hainan Province, Haikou 570228, China; Corresponding authors at: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China.Real-time, on-device segmentation of rubber tree powdery mildew is crucial for smart agriculture, yet existing deep learning models often fail to balance accuracy with computational efficiency. While large models can be precise, their high complexity hinders deployment on terminal devices; conversely, smaller models often struggle to identify small, multi-scale disease spots in complex backgrounds. To bridge this gap, this study develops and validates light-PMSeg, a lightweight instance segmentation framework designed for accurate, real-time disease grading. Our contributions are fourfold: First, we establish a high-performance baseline, PMSeg, which integrates a Mixed Aggregation Network (MANet) and a Channel-wise Cross Fusion Transformer (CCFT) to significantly enhance sensitivity to small targets and multi-scale feature extraction. Second, to achieve on-device feasibility, we systematically compress PMSeg into light-PMSeg through a novel combination of slimming pruning and knowledge distillation (KD), which restores performance after parameter reduction. Third, we constructed a rubber tree powdery mildew segmentation dataset PM-Dataset containing 6,200 images. Finally, we propose an automatic grading algorithm based on the segmentation masks to provide actionable disease severity levels. Experimental results demonstrate the superiority of our framework. The final light-PMSeg model achieves a mAP50 (Mask) of 90.1% and an F1-score of 85.4% at a speed of 148.6 FPS. Crucially, this is achieved while reducing the model's parameters by 79.6% compared to the unpruned PMSeg, confirming its suitability for deployment on low-storage devices. This study delivers a validated, end-to-end solution that effectively reconciles high segmentation accuracy with a lightweight architecture, presenting a practical and robust method for intelligent powdery mildew monitoring.http://www.sciencedirect.com/science/article/pii/S2772375525005507Powdery mildewGrading segmentation networkModel pruningKnowledge distillation |
| spellingShingle | Yuheng Li Lisha Ye Yu Zhang Lijuan Zhou Qian Chen Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation Smart Agricultural Technology Powdery mildew Grading segmentation network Model pruning Knowledge distillation |
| title | Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation |
| title_full | Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation |
| title_fullStr | Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation |
| title_full_unstemmed | Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation |
| title_short | Lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation |
| title_sort | lightweight grading segmentation network for powdery mildew based on model pruning and knowledge distillation |
| topic | Powdery mildew Grading segmentation network Model pruning Knowledge distillation |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525005507 |
| work_keys_str_mv | AT yuhengli lightweightgradingsegmentationnetworkforpowderymildewbasedonmodelpruningandknowledgedistillation AT lishaye lightweightgradingsegmentationnetworkforpowderymildewbasedonmodelpruningandknowledgedistillation AT yuzhang lightweightgradingsegmentationnetworkforpowderymildewbasedonmodelpruningandknowledgedistillation AT lijuanzhou lightweightgradingsegmentationnetworkforpowderymildewbasedonmodelpruningandknowledgedistillation AT qianchen lightweightgradingsegmentationnetworkforpowderymildewbasedonmodelpruningandknowledgedistillation |