MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields
Machine vision application in agriculture has spurred significant interest in crop-missing detection. This study targets critical challenges, such as comprehensive aerial imagery coverage, tiny seedlings easily mistaken for weeds, and the absence of adaptive learning in traditional row classificatio...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2469372 |
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