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Application of CNN-gcForestCS to cassava leaf image classification
Cassava is one of the most consumed carbohydrates in the world, providing a reliable source of income and nutrition to inhabitants of Latin America, Africa and Asia. However, its production is greatly affected by pathogenic infection with cassava mosaic disease (CMD) posing the greatest threat to ca...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2024
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| _version_ | 1867613174135521280 |
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| access_status_str | Open Access |
| author | Carew, Liam |
| author2 | Britz, Stefan |
| author_browse | Britz, Stefan Carew, Liam |
| author_facet | Britz, Stefan Carew, Liam |
| author_sort | Carew, Liam |
| collection | Thesis |
| description | Cassava is one of the most consumed carbohydrates in the world, providing a reliable source of income and nutrition to inhabitants of Latin America, Africa and Asia. However, its production is greatly affected by pathogenic infection with cassava mosaic disease (CMD) posing the greatest threat to cassava farmers in Africa and Asia. Given that developing nations are estimated to be hit hardest by climate change and projected to have the largest population increases in coming decades, optimisation of cassava yield in these areas is imperative to ensure food security. Traditionally, crop health is determined by manual inspection which can be laborious, error-prone and require technical expertise. This produces a costly barrier of entry for smallholding farmers who make up majority of global cassava production. Development of automated disease detection systems using convolutional neural networks (CNNs) deployable on mobile phones have shown to be a cost-efficient and effective method for cassava monitoring, mainly owing to their advanced feature extraction capabilities. However, CNNs require complex hyperparameter tuning and can be computationally intensive to train. GcForestCS (multi-grained cascade forest with confidence screening) presents an alternative statistical learning method that can be trained using CPU, and requires less complex hyperparameter tuning than deep learning while producing competitive performance for lower-dimensionality datasets. Taking advantage of the feature extraction capabilities of CNNs and the competitive performance of gcForestCS for lower-dimensionality datasets, the central aim of this dissertation was to investigate CNN-gcForestCS as an alternative to deep learning for cassava leaf disease detection. The performance of CNN-gcForestCS was compared to gcForestCS and deep learning where the effect of class balance, CNN feature extraction, CNN feature extractor fine-tuning, pooling after multi-grained scanning, and training set curation were assessed. The results showed that the best DenseNet201-gcForestCS model (86.79%) produced marginally worse performance than the best DenseNet201 model (87.43%), while the best MobileNetV2-gcForestCS model (83.66%) produced marginally better performance than the best MobileNetV2 model (82.87%). Overall, the results indicate that it is inconclusive whether CNN-gcForestCS is a viable alternative to deep learning for cassava leaf disease detection, especially when considering the high computational cost associated with the CNN-gcForestCS methodology. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39293 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:56.645Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39293 Application of CNN-gcForestCS to cassava leaf image classification Carew, Liam Britz, Stefan Statistical Science Cassava is one of the most consumed carbohydrates in the world, providing a reliable source of income and nutrition to inhabitants of Latin America, Africa and Asia. However, its production is greatly affected by pathogenic infection with cassava mosaic disease (CMD) posing the greatest threat to cassava farmers in Africa and Asia. Given that developing nations are estimated to be hit hardest by climate change and projected to have the largest population increases in coming decades, optimisation of cassava yield in these areas is imperative to ensure food security. Traditionally, crop health is determined by manual inspection which can be laborious, error-prone and require technical expertise. This produces a costly barrier of entry for smallholding farmers who make up majority of global cassava production. Development of automated disease detection systems using convolutional neural networks (CNNs) deployable on mobile phones have shown to be a cost-efficient and effective method for cassava monitoring, mainly owing to their advanced feature extraction capabilities. However, CNNs require complex hyperparameter tuning and can be computationally intensive to train. GcForestCS (multi-grained cascade forest with confidence screening) presents an alternative statistical learning method that can be trained using CPU, and requires less complex hyperparameter tuning than deep learning while producing competitive performance for lower-dimensionality datasets. Taking advantage of the feature extraction capabilities of CNNs and the competitive performance of gcForestCS for lower-dimensionality datasets, the central aim of this dissertation was to investigate CNN-gcForestCS as an alternative to deep learning for cassava leaf disease detection. The performance of CNN-gcForestCS was compared to gcForestCS and deep learning where the effect of class balance, CNN feature extraction, CNN feature extractor fine-tuning, pooling after multi-grained scanning, and training set curation were assessed. The results showed that the best DenseNet201-gcForestCS model (86.79%) produced marginally worse performance than the best DenseNet201 model (87.43%), while the best MobileNetV2-gcForestCS model (83.66%) produced marginally better performance than the best MobileNetV2 model (82.87%). Overall, the results indicate that it is inconclusive whether CNN-gcForestCS is a viable alternative to deep learning for cassava leaf disease detection, especially when considering the high computational cost associated with the CNN-gcForestCS methodology. 2024-04-04T08:11:46Z 2024-04-04T08:11:46Z 2023 2024-04-04T07:24:32Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/39293 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Statistical Science Carew, Liam Application of CNN-gcForestCS to cassava leaf image classification |
| thesis_degree_str | Master's |
| title | Application of CNN-gcForestCS to cassava leaf image classification |
| title_full | Application of CNN-gcForestCS to cassava leaf image classification |
| title_fullStr | Application of CNN-gcForestCS to cassava leaf image classification |
| title_full_unstemmed | Application of CNN-gcForestCS to cassava leaf image classification |
| title_short | Application of CNN-gcForestCS to cassava leaf image classification |
| title_sort | application of cnn gcforestcs to cassava leaf image classification |
| topic | Statistical Science |
| url | http://hdl.handle.net/11427/39293 |
| work_keys_str_mv | AT carewliam applicationofcnngcforestcstocassavaleafimageclassification |