LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11
Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter tritica...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/2/196 |
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| author | Ameer Tamoor Khan Signe Marie Jensen |
| author_facet | Ameer Tamoor Khan Signe Marie Jensen |
| author_sort | Ameer Tamoor Khan |
| collection | DOAJ |
| description | Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training. |
| format | Article |
| id | doaj-art-adcbe7550ad14afcbb219f221aa98b60 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-adcbe7550ad14afcbb219f221aa98b602025-01-24T13:16:05ZengMDPI AGAgriculture2077-04722025-01-0115219610.3390/agriculture15020196LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11Ameer Tamoor Khan0Signe Marie Jensen1Department of Plant and Environmental Sciences, University of Copenhagen, 1172 Copenhagen, DenmarkDepartment of Plant and Environmental Sciences, University of Copenhagen, 1172 Copenhagen, DenmarkAccurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training.https://www.mdpi.com/2077-0472/15/2/196plant phenotypingYOLOv11leaf segmentationprecision agriculturedeep learning |
| spellingShingle | Ameer Tamoor Khan Signe Marie Jensen LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 Agriculture plant phenotyping YOLOv11 leaf segmentation precision agriculture deep learning |
| title | LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 |
| title_full | LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 |
| title_fullStr | LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 |
| title_full_unstemmed | LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 |
| title_short | LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11 |
| title_sort | leaf net a unified framework for leaf extraction and analysis in multi crop phenotyping using yolov11 |
| topic | plant phenotyping YOLOv11 leaf segmentation precision agriculture deep learning |
| url | https://www.mdpi.com/2077-0472/15/2/196 |
| work_keys_str_mv | AT ameertamoorkhan leafnetaunifiedframeworkforleafextractionandanalysisinmulticropphenotypingusingyolov11 AT signemariejensen leafnetaunifiedframeworkforleafextractionandanalysisinmulticropphenotypingusingyolov11 |