Deep Gaussian Convolutional Neural Network Model in Classification of Cassava Diseases using Spectral Data Article Swipe
Emmanuel Ahishakiye
,
Waweru Mwangi
,
Petronilla Muthoni
,
Fredrick Kanobe
,
Godliver Owomugisha
,
Danison Taremwa
,
Leonard Nkalubo
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1750871/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1750871/v1
Early disease identification in crops is critical for food security, especially in Sub-Saharan Africa. To identify cassava diseases, professionals visually score the plants by looking for disease indicators on the leaves which is notoriously subjective. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. However, traditional machine learning algorithms are not calibrated even though they have high accuracy. The ability to provide well-calibrated posterior distributions is one of the most appealing properties of Gaussian processes. Motivated by the current developments in the field of Gaussian Processes, this study proposed a deep Gaussian convolutional neural network model (DGCNN) for the detection and classification of cassava diseases using spectral data. The proposed model uses a hybrid kernel function that is the product of a rational quadratic kernel and a squared exponential kernel. Experimental results revealed that our proposed hybrid kernel function performed better in terms of accuracy of 90.10% when compared to both the squared exponential kernel with an accuracy of 88.01% and the rational quadratic kernel with an accuracy of 88.52%. In our future work, we propose to integrate the Optimised model proposed in this study with the transfer learning approach, a move that may help to improve the model performance.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1750871/v1
- https://www.researchsquare.com/article/rs-1750871/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283016122
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- OpenAlex ID
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https://openalex.org/W4283016122Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1750871/v1Digital Object Identifier
- Title
-
Deep Gaussian Convolutional Neural Network Model in Classification of Cassava Diseases using Spectral DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-16Full publication date if available
- Authors
-
Emmanuel Ahishakiye, Waweru Mwangi, Petronilla Muthoni, Fredrick Kanobe, Godliver Owomugisha, Danison Taremwa, Leonard NkaluboList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1750871/v1Publisher landing page
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https://www.researchsquare.com/article/rs-1750871/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-1750871/latest.pdfDirect OA link when available
- Concepts
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Gaussian function, Kernel (algebra), Computer science, Machine learning, Artificial intelligence, Gaussian, Convolutional neural network, Pattern recognition (psychology), Quadratic equation, Gaussian network model, Gaussian process, Mathematics, Combinatorics, Physics, Quantum mechanics, GeometryTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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