Detection and Classification of Temporal Changes for Citrus Canker Growth Rate Using Deep Learning Article Swipe
Naureen Zainab
,
Hammad Afzal
,
Taher Al‐Shehari
,
Muna Al‐Razgan
,
Naima Iltaf
,
Muhammad Zakria
,
Muhammad Javed Hyder
,
Raheel Nawaz
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3331735
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3331735
Citrus canker is among the major plant diseases caused by Xanthomonas citri which affects the quality and quantity of citrus fruit. This results in the reduction of citrus production which causes a huge financial loss and livelihood of the farming community. Thus, it is critically important to build a robust, accurate, and time-efficient detection method for real-time identification of the disease. Due to their powerful learning capabilities and improved feature extraction, deep learning approaches have made it feasible to carry out a number of tasks related to the identification of citrus canker in citrus leaves. Previous research has primarily focused on detecting citrus canker on fruits, early detection on leaves can facilitate the adoption of preventive measures before the disease reaches a critical stage. This paper proposes a novel deep learning-based approach for determining the growth rate of citrus canker by classifying it into six distinct stages: water soaking, yellow chlorosis/initiation, chlorosis, blister formation, canker development start, canker infection (50% of the inoculated area), and canker infection (100% of the inoculated area). The proposed approach involves image conversion, size reduction, image augmentation, and the utilization of DenseNet-121. Experimental results demonstrate a classification accuracy of 98.97% using the suggested approach. The Accuracy was 98.97% with macro precision 97%, weighted precision 99%, Macro recall 98%, weighted recall 98%, macro F1_Score 97% and weighted F1_Score 98%. This study presents a unique technique for detecting and classifying the growth rate of citrus canker based on six different stages, while also calculating the temporal change in the affected area of the disease in inoculated citrus leaves. Furthermore, a mathematical model is proposed to predict the disease’s growth rate at any given time, offering valuable insights for disease management and prevention.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3331735
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10314481.pdf
- OA Status
- gold
- Cited By
- 13
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388544026
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388544026Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2023.3331735Digital Object Identifier
- Title
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Detection and Classification of Temporal Changes for Citrus Canker Growth Rate Using Deep LearningWork 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
-
2023-01-01Full publication date if available
- Authors
-
Naureen Zainab, Hammad Afzal, Taher Al‐Shehari, Muna Al‐Razgan, Naima Iltaf, Muhammad Zakria, Muhammad Javed Hyder, Raheel NawazList of authors in order
- Landing page
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https://doi.org/10.1109/access.2023.3331735Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10314481.pdfDirect 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
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10314481.pdfDirect OA link when available
- Concepts
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Citrus canker, Canker, Chlorosis, Artificial intelligence, Computer science, F1 score, Horticulture, Pattern recognition (psychology), Biology, Genetics, BacteriaTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 5Per-year citation counts (last 5 years)
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66Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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