A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases
Abstract Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-t...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04479-2 |
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| author | Hassan Ali Noora Shifa Rachid Benlamri Aitazaz A. Farooque Raziq Yaqub |
| author_facet | Hassan Ali Noora Shifa Rachid Benlamri Aitazaz A. Farooque Raziq Yaqub |
| author_sort | Hassan Ali |
| collection | DOAJ |
| description | Abstract Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-trained EfficientNet-B0 base, enhanced through architectural modifications such as the integration of a global max pooling (GMP) layer, dropout, regularization, and full-model fine-tuning. To address class imbalance and improve generalization, the study adopts a holistic training strategy that integrates data augmentation, stratified data splitting, and class weighting, alongside transfer learning. The model is evaluated on the PlantVillage (PV) dataset and a curated Apple PV (APV) dataset and compared against EfficientNet-B0, EfficientNet-B3, Inception-v3, ResNet50, and VGG16 models. The fine-tuned model demonstrates outstanding test accuracies of 99.69% and 99.78% for classifying plant diseases using the APV and PV datasets, respectively. The fine-tuned model outperforms EfficientNet-B0, EfficientNet-B3, and VGG16 on both datasets and shows superior performance compared to Inception-v3 and ResNet-50 on the PV dataset. Both EfficientNet-B0 and the fine-tuned model demonstrate the lowest memory consumption and floating-point operations per second (FLOPs). Also, as compared to the EfficientNet-B0 model, the fine-tuned model achieves an 11% increase in accuracy on the APV dataset and a 49.5% accuracy improvement on the PV dataset, with approximately a 7-8% increase in both memory usage and FLOPs. The fine-tuned model thus emerges as an effective solution for plant leaf disease classification, delivering outstanding accuracy with optimized memory consumption and FLOPs, making it suitable for resource-constrained environments. This study demonstrates that fine-tuned CNN approaches, when combined with transfer learning, advanced data pre-processing, and architectural optimizations, can significantly enhance the accuracy of diseased leaf classification in crops with efficient implementation in limited-resource settings. |
| format | Article |
| id | doaj-art-3aee03a9fb504ca999c5e4737ba83097 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3aee03a9fb504ca999c5e4737ba830972025-08-20T03:45:57ZengNature PortfolioScientific Reports2045-23222025-07-0115112610.1038/s41598-025-04479-2A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseasesHassan Ali0Noora Shifa1Rachid Benlamri2Aitazaz A. Farooque3Raziq Yaqub4Centre of Excellence for Sustainability and Food SecurityDepartment of Electrical Engineering, University of Doha for Science and TechnologyCentre of Excellence for Sustainability and Food SecurityCanadian Centre for Climate Change and AdaptationDepartment of Electrical Engineering and Computer Science, Alabama A&M UniversityAbstract Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-trained EfficientNet-B0 base, enhanced through architectural modifications such as the integration of a global max pooling (GMP) layer, dropout, regularization, and full-model fine-tuning. To address class imbalance and improve generalization, the study adopts a holistic training strategy that integrates data augmentation, stratified data splitting, and class weighting, alongside transfer learning. The model is evaluated on the PlantVillage (PV) dataset and a curated Apple PV (APV) dataset and compared against EfficientNet-B0, EfficientNet-B3, Inception-v3, ResNet50, and VGG16 models. The fine-tuned model demonstrates outstanding test accuracies of 99.69% and 99.78% for classifying plant diseases using the APV and PV datasets, respectively. The fine-tuned model outperforms EfficientNet-B0, EfficientNet-B3, and VGG16 on both datasets and shows superior performance compared to Inception-v3 and ResNet-50 on the PV dataset. Both EfficientNet-B0 and the fine-tuned model demonstrate the lowest memory consumption and floating-point operations per second (FLOPs). Also, as compared to the EfficientNet-B0 model, the fine-tuned model achieves an 11% increase in accuracy on the APV dataset and a 49.5% accuracy improvement on the PV dataset, with approximately a 7-8% increase in both memory usage and FLOPs. The fine-tuned model thus emerges as an effective solution for plant leaf disease classification, delivering outstanding accuracy with optimized memory consumption and FLOPs, making it suitable for resource-constrained environments. This study demonstrates that fine-tuned CNN approaches, when combined with transfer learning, advanced data pre-processing, and architectural optimizations, can significantly enhance the accuracy of diseased leaf classification in crops with efficient implementation in limited-resource settings.https://doi.org/10.1038/s41598-025-04479-2Apple leaf diseasesEfficientNet-B0Deep learningCNNTransfer learningFine-tuning |
| spellingShingle | Hassan Ali Noora Shifa Rachid Benlamri Aitazaz A. Farooque Raziq Yaqub A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases Scientific Reports Apple leaf diseases EfficientNet-B0 Deep learning CNN Transfer learning Fine-tuning |
| title | A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases |
| title_full | A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases |
| title_fullStr | A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases |
| title_full_unstemmed | A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases |
| title_short | A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases |
| title_sort | fine tuned efficientnet b0 convolutional neural network for accurate and efficient classification of apple leaf diseases |
| topic | Apple leaf diseases EfficientNet-B0 Deep learning CNN Transfer learning Fine-tuning |
| url | https://doi.org/10.1038/s41598-025-04479-2 |
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