A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease
Summary
A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease is a scholarly article[1].
Key Facts
A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-deep-convolution-neural-network-model-based-on-feature-concatenation-approach-for-classification-of-tomato-leaf-diseas
MLA“A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-deep-convolution-neural-network-model-based-on-feature-concatenation-approach-for-classification-of-tomato-leaf-diseas.
BibTeX@misc{4ortxyz_a-deep-convolution-neural-network-model-based-on-feature-concatenation-approach-for-classification-of-tomato-leaf-diseas_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease}}, year = {2026}, url = {https://4ort.xyz/entity/a-deep-convolution-neural-network-model-based-on-feature-concatenation-approach-for-classification-of-tomato-leaf-diseas}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease — https://4ort.xyz/entity/a-deep-convolution-neural-network-model-based-on-feature-concatenation-approach-for-classification-of-tomato-leaf-diseas (retrieved 2026-05-24)
