A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases
Abstract Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Traditional methods to diagnose these diseases...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-90646-4 |
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| author | G. Sambasivam G. Prabu kanna Munesh Singh Chauhan Prem Raja Yogesh Kumar |
| author_facet | G. Sambasivam G. Prabu kanna Munesh Singh Chauhan Prem Raja Yogesh Kumar |
| author_sort | G. Sambasivam |
| collection | DOAJ |
| description | Abstract Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Traditional methods to diagnose these diseases are time-consuming, prone to error, and require expert knowledge, making automated solutions highly preferred. This paper explores the application of advanced deep learning techniques to detect as well as classify cassava leaf diseases which includes EfficientNet models, DenseNet169, Xception, MobileNetV2, ResNet models, Vgg19, InceptionV3, and InceptionResNetV2. A dataset consisting of around 36,000 labelled images of cassava leaves, afflicted by diseases such as Cassava Brown Streak Disease, Cassava Mosaic Disease, Cassava Green Mottle, Cassava Bacterial Blight, and healthy leaves, was used to train these models. Further the images were pre-processed by converting them into grayscale, reducing noise using Gaussian filter, obtaining the region of interest using Otsu binarization, Distance transformation, as well as Watershed technique followed by employing contour-based feature selection to enhance model performance. Models, after fine-tuned with ADAM optimizer computed that among the tested models, the hybrid model (DenseNet169 + EfficientNetB0) had superior performance with classification accuracy of 89.94% while as EfficientNetB0 had the highest values of precision, recall, and F1score with 0.78 each. The novelty of the hybrid model lies in its ability to combine DenseNet169’s feature reuse capability with EfficientNetB0’s computational efficiency, resulting in improved accuracy and scalability. These results highlight the potential of deep learning for accurate and scalable cassava leaf disease diagnosis, laying the foundation for automated plant disease monitoring systems. |
| format | Article |
| id | doaj-art-ad9edd4b793d4e1499df9f3ebaae6c1b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ad9edd4b793d4e1499df9f3ebaae6c1b2025-08-20T03:03:57ZengNature PortfolioScientific Reports2045-23222025-02-0115112410.1038/s41598-025-90646-4A hybrid deep learning model approach for automated detection and classification of cassava leaf diseasesG. Sambasivam0G. Prabu kanna1Munesh Singh Chauhan2Prem Raja3Yogesh Kumar4School of Computing and Data Science, XIAMEN UNIVERSITY MALAYSIASchool of Computing Science Engineering and Artificial Intelligence (SCAI), VIT Bhopal UniversityEuro University of BahrainDepartment of Information Technology, Kalasalingam Academy of Research and EducationDepartment of Computer Science and Engineering, School of Technology, PDEU GandhinagarAbstract Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Traditional methods to diagnose these diseases are time-consuming, prone to error, and require expert knowledge, making automated solutions highly preferred. This paper explores the application of advanced deep learning techniques to detect as well as classify cassava leaf diseases which includes EfficientNet models, DenseNet169, Xception, MobileNetV2, ResNet models, Vgg19, InceptionV3, and InceptionResNetV2. A dataset consisting of around 36,000 labelled images of cassava leaves, afflicted by diseases such as Cassava Brown Streak Disease, Cassava Mosaic Disease, Cassava Green Mottle, Cassava Bacterial Blight, and healthy leaves, was used to train these models. Further the images were pre-processed by converting them into grayscale, reducing noise using Gaussian filter, obtaining the region of interest using Otsu binarization, Distance transformation, as well as Watershed technique followed by employing contour-based feature selection to enhance model performance. Models, after fine-tuned with ADAM optimizer computed that among the tested models, the hybrid model (DenseNet169 + EfficientNetB0) had superior performance with classification accuracy of 89.94% while as EfficientNetB0 had the highest values of precision, recall, and F1score with 0.78 each. The novelty of the hybrid model lies in its ability to combine DenseNet169’s feature reuse capability with EfficientNetB0’s computational efficiency, resulting in improved accuracy and scalability. These results highlight the potential of deep learning for accurate and scalable cassava leaf disease diagnosis, laying the foundation for automated plant disease monitoring systems.https://doi.org/10.1038/s41598-025-90646-4Cassava leaf diseaseHybrid deep learningCassava Mosaic DiseaseWatershed transformation, DenseNet169Agricultural disease classification |
| spellingShingle | G. Sambasivam G. Prabu kanna Munesh Singh Chauhan Prem Raja Yogesh Kumar A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases Scientific Reports Cassava leaf disease Hybrid deep learning Cassava Mosaic Disease Watershed transformation, DenseNet169 Agricultural disease classification |
| title | A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases |
| title_full | A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases |
| title_fullStr | A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases |
| title_full_unstemmed | A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases |
| title_short | A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases |
| title_sort | hybrid deep learning model approach for automated detection and classification of cassava leaf diseases |
| topic | Cassava leaf disease Hybrid deep learning Cassava Mosaic Disease Watershed transformation, DenseNet169 Agricultural disease classification |
| url | https://doi.org/10.1038/s41598-025-90646-4 |
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