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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Bibliographic Details
Main Authors: Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Applications in Plant Sciences
Subjects:
Online Access:https://doi.org/10.1002/aps3.11620
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