Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model Article Swipe
Adel Sulaiman
,
Vatsala Anand
,
Sheifali Gupta
,
Hani Alshahrani
,
Mana Saleh Al Reshan
,
Adel Rajab
,
Asadullah Shaikh
,
Ahmad Taher Azar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su151713228
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su151713228
Apple foliar diseases are a group of diseases that affect the leaves of apple trees. These diseases can significantly impact apple tree health and fruit yield. Ordinary apple foliar diseases include frog_eye_leaf_spots, powdery mildew, rust, apple scabs, etc. Early detection of these diseases is important for effective apple crop management to increase the yield of apples. Therefore, this research proposes a fine-tuned EfficientNetB3 model for the quick and precise assessment of these apple foliar diseases. A dataset containing 23,187 RGB images of eleven different apple foliar diseases is used for experimentation. The proposed model is compared with four transfer learning models, i.e., InceptionResNetV2, ResNet50, AlexNet, and VGG16. All models are fine-tuned by adding different layers like the global average pooling layer, flatten layer, dropout layer, and dense layer. The performance of these five models is compared in terms of the precision, recall, accuracy, and F1-score. The EfficientNetB3 outperformed the other models in terms of all performance parameters. The best model is further optimized with the help of three optimizers, i.e., Adam, SGD, and Adagrad. The proposed model achieved the precision, recall, and F1-score values of 86%, 88%, and 86%, respectively, at 32 batch sizes and 10 epochs. This research formulated a model for an apple foliar disease diagnosis within sustainable agriculture.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su151713228
- https://www.mdpi.com/2071-1050/15/17/13228/pdf?version=1693807788
- OA Status
- gold
- Cited By
- 14
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386409574
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386409574Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su151713228Digital Object Identifier
- Title
-
Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-04Full publication date if available
- Authors
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Adel Sulaiman, Vatsala Anand, Sheifali Gupta, Hani Alshahrani, Mana Saleh Al Reshan, Adel Rajab, Asadullah Shaikh, Ahmad Taher AzarList of authors in order
- Landing page
-
https://doi.org/10.3390/su151713228Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/15/17/13228/pdf?version=1693807788Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2071-1050/15/17/13228/pdf?version=1693807788Direct OA link when available
- Concepts
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Powdery mildew, Computer science, Artificial intelligence, Horticulture, Yield (engineering), Mathematics, Machine learning, Statistics, Agricultural engineering, Biology, Engineering, Materials science, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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