PD36-C is a 1.25 million parameter CNN achieving 99.53% average test accuracy on 38 plant disease classes from the New Plant Diseases Dataset, with a Qt-based app enabling edge deployment.
Identification of disease using deep learning and evaluation of bacteriosis in peach leaf
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EfficientNetB5 with CBAM reaches 93.3% accuracy on a 1,366-image peach leaf damage dataset and EfficientNetB3 with CBAM reaches 93% macro F1 after transfer to a 180-image local domain.
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A Compact and Efficient 1.251 Million Parameter Machine Learning CNN Model PD36-C for Plant Disease Detection: A Case Study
PD36-C is a 1.25 million parameter CNN achieving 99.53% average test accuracy on 38 plant disease classes from the New Plant Diseases Dataset, with a Qt-based app enabling edge deployment.
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Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift
EfficientNetB5 with CBAM reaches 93.3% accuracy on a 1,366-image peach leaf damage dataset and EfficientNetB3 with CBAM reaches 93% macro F1 after transfer to a 180-image local domain.