Comparison of convolutional neural network (CNN) methods using AlexNet and VGG16 architectures for disease detection in apples

Q: What does this research mean for the field?

Convolutional neural network architectures, specifically AlexNet and VGG16, can be applied and evaluated for their effectiveness in detecting diseases in apples. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Anang Pramono; Zuhriah Virlani Putri

doi:10.1504/ijaac.2028.10078919

June 1, 2026International Journal of Automation and Control

Comparison of convolutional neural network (CNN) methods using AlexNet and VGG16 architectures for disease detection in apples

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Key Points

The aim is to compare the effectiveness of AlexNet and VGG16 convolutional neural networks for detecting diseases in apples.
Implemented two CNN architectures: AlexNet and VGG16.
Used a dataset of apple images to train and evaluate models.
Conducted performance analysis based on accuracy and detection rates.
AlexNet achieved a detection accuracy of 92% compared to 85% for VGG16.
Reported faster processing time for AlexNet at 2 seconds per image versus 4 seconds for VGG16.
Indicated that AlexNet outperforms VGG16 in identifying certain types of apple diseases.

Implication

Compares disease detection efficacy of AlexNet and VGG16 architectures in apples, revealing significant differences.

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Cite This Study

Pramono et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc4e4dee9eb8c0dce667d https://doi.org/10.1504/ijaac.2028.10078919

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Comparison of convolutional neural network (CNN) methods using AlexNet and VGG16 architectures for disease detection in apples

Key Points

Implication

Abstract

Discussion

Cite This Study

Comparison of convolutional neural network (CNN) methods using AlexNet and VGG16 architectures for disease detection in apples

Key Points

Implication

Abstract

Discussion

Cite This Study

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