Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments
The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions. Predominant meth...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-06-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721724000059 |