MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring
Monitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, this task presents a challenge, as crop leaf images...
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MDPI AG
2025-07-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/7/238 |
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| author | Zhihan Cheng He Yan |
| author_facet | Zhihan Cheng He Yan |
| author_sort | Zhihan Cheng |
| collection | DOAJ |
| description | Monitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, this task presents a challenge, as crop leaf images often feature substantial overlap, obstructing the precise differentiation of individual leaf edges. Moreover, existing segmentation methods fail to preserve fine edge details, a deficiency that compromises precise morphological analysis. To overcome these challenges, we introduce MSFUnet, an innovative network for semantic segmentation. MSFUnet integrates a multi-path feature fusion (MFF) mechanism and an edge-detail focus (EDF) module. The MFF module integrates multi-scale features to improve the model’s capacity for distinguishing overlapping leaf areas, while the EDF module employs extended convolution to accurately capture fine edge details. Collectively, these modules enable MSFUnet to achieve high-precision individual leaf segmentation. In addition, standard image augmentations (e.g., contrast/brightness adjustments) were applied to mitigate the impact of variable lighting conditions on leaf appearance in the input images, thereby improving model robustness. Experimental results indicate that MSFUnet attains an MIoU of 93.35%, outperforming conventional segmentation methods and highlighting its effectiveness in crop leaf growth monitoring. |
| format | Article |
| id | doaj-art-54bcc3ce4f8f4e0ab2fec36b3fbf19c9 |
| institution | Matheson Library |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | AgriEngineering |
| spelling | doaj-art-54bcc3ce4f8f4e0ab2fec36b3fbf19c92025-07-25T13:09:37ZengMDPI AGAgriEngineering2624-74022025-07-017723810.3390/agriengineering7070238MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status MonitoringZhihan Cheng0He Yan1College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, ChinaMonitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, this task presents a challenge, as crop leaf images often feature substantial overlap, obstructing the precise differentiation of individual leaf edges. Moreover, existing segmentation methods fail to preserve fine edge details, a deficiency that compromises precise morphological analysis. To overcome these challenges, we introduce MSFUnet, an innovative network for semantic segmentation. MSFUnet integrates a multi-path feature fusion (MFF) mechanism and an edge-detail focus (EDF) module. The MFF module integrates multi-scale features to improve the model’s capacity for distinguishing overlapping leaf areas, while the EDF module employs extended convolution to accurately capture fine edge details. Collectively, these modules enable MSFUnet to achieve high-precision individual leaf segmentation. In addition, standard image augmentations (e.g., contrast/brightness adjustments) were applied to mitigate the impact of variable lighting conditions on leaf appearance in the input images, thereby improving model robustness. Experimental results indicate that MSFUnet attains an MIoU of 93.35%, outperforming conventional segmentation methods and highlighting its effectiveness in crop leaf growth monitoring.https://www.mdpi.com/2624-7402/7/7/238crop leaf image segmentationleaf growth monitoringmulti-path feature fusionsemantic segmentation |
| spellingShingle | Zhihan Cheng He Yan MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring AgriEngineering crop leaf image segmentation leaf growth monitoring multi-path feature fusion semantic segmentation |
| title | MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring |
| title_full | MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring |
| title_fullStr | MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring |
| title_full_unstemmed | MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring |
| title_short | MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring |
| title_sort | msfunet a semantic segmentation network for crop leaf growth status monitoring |
| topic | crop leaf image segmentation leaf growth monitoring multi-path feature fusion semantic segmentation |
| url | https://www.mdpi.com/2624-7402/7/7/238 |
| work_keys_str_mv | AT zhihancheng msfunetasemanticsegmentationnetworkforcropleafgrowthstatusmonitoring AT heyan msfunetasemanticsegmentationnetworkforcropleafgrowthstatusmonitoring |