Tailoring convolutional neural networks for custom botanical data
Abstract Premise Automated disease, weed, and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet, and ConvNeXt often underperform on smaller, specialised datasets typical of such projects. Methods...
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Main Authors: | Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks |
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Format: | Article |
Language: | English |
Published: |
Wiley
2025-01-01
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Series: | Applications in Plant Sciences |
Subjects: | |
Online Access: | https://doi.org/10.1002/aps3.11620 |
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