A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture
In this paper, we propose a knowledge distillation framework specifically designed for semantic segmentation tasks in agricultural scenarios. This framework aims to address several prevalent challenges in smart agriculture, including limited computational resources, strict real-time constraints, and...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/13/1418 |
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| author | Zhiyong Li Lan Xiang Jun Sun Dingyi Liao Lijia Xu Mantao Wang |
| author_facet | Zhiyong Li Lan Xiang Jun Sun Dingyi Liao Lijia Xu Mantao Wang |
| author_sort | Zhiyong Li |
| collection | DOAJ |
| description | In this paper, we propose a knowledge distillation framework specifically designed for semantic segmentation tasks in agricultural scenarios. This framework aims to address several prevalent challenges in smart agriculture, including limited computational resources, strict real-time constraints, and suboptimal segmentation accuracy on cropped images. Traditional single-level feature distillation methods often suffer from insufficient knowledge transfer and inefficient utilization of multi-scale features, which significantly limits their ability to accurately segment complex crop structures in dynamic field environments. To overcome these issues, we propose a multi-level distillation strategy that leverages feature and embedding patch distillation, combining high-level semantic features with low-level texture details for joint distillation. This approach enables the precise capture of fine-grained agricultural elements, such as crop boundaries, stems, petioles, and weed clusters, which are critical for achieving robust segmentation. Additionally, we integrated an enhanced attention mechanism into the framework, which effectively strengthens and fuses key crop-related features during the distillation process, thereby further improving the model’s performance and image understanding capabilities. Extensive experiments on two agricultural datasets (sweet pepper and sugar) demonstrate that our method improves segmentation accuracy by 7.59% and 6.79%, without significantly increasing model complexity. Further validation shows that our approach exhibits strong generalization capabilities on two widely used public datasets, proving its applicability beyond agricultural domains. |
| format | Article |
| id | doaj-art-60aef9bc67ab4d2faf1ca56e5b3607f0 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-60aef9bc67ab4d2faf1ca56e5b3607f02025-08-20T02:35:53ZengMDPI AGAgriculture2077-04722025-06-011513141810.3390/agriculture15131418A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision AgricultureZhiyong Li0Lan Xiang1Jun Sun2Dingyi Liao3Lijia Xu4Mantao Wang5College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaObservation and Research Station of Land Ecology and Land Use in Chengdu Plain, Ministry of Natural Resources, Chengdu 610045, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaIn this paper, we propose a knowledge distillation framework specifically designed for semantic segmentation tasks in agricultural scenarios. This framework aims to address several prevalent challenges in smart agriculture, including limited computational resources, strict real-time constraints, and suboptimal segmentation accuracy on cropped images. Traditional single-level feature distillation methods often suffer from insufficient knowledge transfer and inefficient utilization of multi-scale features, which significantly limits their ability to accurately segment complex crop structures in dynamic field environments. To overcome these issues, we propose a multi-level distillation strategy that leverages feature and embedding patch distillation, combining high-level semantic features with low-level texture details for joint distillation. This approach enables the precise capture of fine-grained agricultural elements, such as crop boundaries, stems, petioles, and weed clusters, which are critical for achieving robust segmentation. Additionally, we integrated an enhanced attention mechanism into the framework, which effectively strengthens and fuses key crop-related features during the distillation process, thereby further improving the model’s performance and image understanding capabilities. Extensive experiments on two agricultural datasets (sweet pepper and sugar) demonstrate that our method improves segmentation accuracy by 7.59% and 6.79%, without significantly increasing model complexity. Further validation shows that our approach exhibits strong generalization capabilities on two widely used public datasets, proving its applicability beyond agricultural domains.https://www.mdpi.com/2077-0472/15/13/1418knowledge distillationsemantic segmentationmulti-scale featuresembedding patchattention mechanismgeneralization capability |
| spellingShingle | Zhiyong Li Lan Xiang Jun Sun Dingyi Liao Lijia Xu Mantao Wang A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture Agriculture knowledge distillation semantic segmentation multi-scale features embedding patch attention mechanism generalization capability |
| title | A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture |
| title_full | A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture |
| title_fullStr | A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture |
| title_full_unstemmed | A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture |
| title_short | A Multi-Level Knowledge Distillation for Enhanced Crop Segmentation in Precision Agriculture |
| title_sort | multi level knowledge distillation for enhanced crop segmentation in precision agriculture |
| topic | knowledge distillation semantic segmentation multi-scale features embedding patch attention mechanism generalization capability |
| url | https://www.mdpi.com/2077-0472/15/13/1418 |
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