A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment Article Swipe
Vinay Gautam
,
Naresh Kumar Trivedi
,
Aman Singh
,
Heba G. Mohamed
,
Irene Delgado Noya
,
Preet Kaur
,
Nitin Goyal
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142013610
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142013610
The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su142013610
- https://www.mdpi.com/2071-1050/14/20/13610/pdf?version=1666687310
- OA Status
- gold
- Cited By
- 66
- References
- 97
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306969352
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- OpenAlex ID
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https://openalex.org/W4306969352Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su142013610Digital Object Identifier
- Title
-
A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease AssessmentWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-20Full publication date if available
- Authors
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Vinay Gautam, Naresh Kumar Trivedi, Aman Singh, Heba G. Mohamed, Irene Delgado Noya, Preet Kaur, Nitin GoyalList of authors in order
- Landing page
-
https://doi.org/10.3390/su142013610Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/14/20/13610/pdf?version=1666687310Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/14/20/13610/pdf?version=1666687310Direct OA link when available
- Concepts
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Deep learning, Artificial intelligence, Convolutional neural network, Transfer of learning, Computer science, Machine learning, Segmentation, Preprocessor, Artificial neural network, Agricultural engineering, Crop, Agriculture, Pattern recognition (psychology), Agronomy, Biology, Engineering, EcologyTop concepts (fields/topics) attached by OpenAlex
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66Total citation count in OpenAlex
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2025: 16, 2024: 24, 2023: 24, 2022: 2Per-year citation counts (last 5 years)
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97Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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