A novel deep learning method for maize disease identification based on small sample-size and complex background datasets

Research article (Ecological Informatics, 2023) · cited 84× · AI/ML
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A novel deep learning method for maize disease identification based on small sample-size and complex background datasets

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A novel deep learning method for maize disease identification based on small sample-size and complex background datasets is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). A novel deep learning method for maize disease identification based on small sample-size and complex background datasets. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-novel-deep-learning-method-for-maize-disease-identification-based-on-small-sample-size-and-complex-background-datasets
MLA “A novel deep learning method for maize disease identification based on small sample-size and complex background datasets.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-novel-deep-learning-method-for-maize-disease-identification-based-on-small-sample-size-and-complex-background-datasets.
BibTeX @misc{4ortxyz_a-novel-deep-learning-method-for-maize-disease-identification-based-on-small-sample-size-and-complex-background-datasets_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A novel deep learning method for maize disease identification based on small sample-size and complex background datasets}}, year = {2026}, url = {https://4ort.xyz/entity/a-novel-deep-learning-method-for-maize-disease-identification-based-on-small-sample-size-and-complex-background-datasets}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A novel deep learning method for maize disease identification based on small sample-size and complex background datasets — https://4ort.xyz/entity/a-novel-deep-learning-method-for-maize-disease-identification-based-on-small-sample-size-and-complex-background-datasets (retrieved 2026-05-24)

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