The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits
Open accessAbstract
Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.
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Selective Search for Object Recognition
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Acquisition of Localization Confidence for Accurate Object Detection
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Using deep transfer learning for image-based plant disease identification
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A Proof of Local Convergence for the Adam Optimizer
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Exploit Bounding Box Annotations for Multi-Label Object Recognition
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Metadata
- Authors
- Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M. M., Dhaou, I. B., & Iwendi, C.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2022-02-08
- Volume
- 11
- Issue
- 3
- Pages
- 495
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4211112186
- Cited by
- 308
- References
- 20
- FWCI
- 34.72
- Type
- journal article
- Language
- en
- Open access
- gold
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- Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M. M., Dhaou, I. B., & Iwendi, C. (2022). A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits. Electronics. https://doi.org/10.3390/electronics11030495
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