POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea
Chickpeas (Cicer arietinum L.) are a nutritious legume crop farmed on 17.8 million hectares in 56 countries throughout the world, with an estimated yearly yield of 14.78 million tones. Ethiopia is the leader in chickpea production on the African continent and the sixth-largest producer globally. How...
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Food & Agriculture |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2024.2336002 |
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| author | Melaku Bitew Haile Abebech Jenber Belay |
| author_facet | Melaku Bitew Haile Abebech Jenber Belay |
| author_sort | Melaku Bitew Haile |
| collection | DOAJ |
| description | Chickpeas (Cicer arietinum L.) are a nutritious legume crop farmed on 17.8 million hectares in 56 countries throughout the world, with an estimated yearly yield of 14.78 million tones. Ethiopia is the leader in chickpea production on the African continent and the sixth-largest producer globally. However, Ascochyta rabiei remains a serious disease of chickpeas. If Ascochyta rabies is not managed, its effects on chickpea output could be either partial or complete under favourable environmental conditions. Knowing the severity level of this disease in farmlands where chickpeas are grown has an impact on the rates of yield and quality losses. Currently, Ethiopian farmers and pathologists in the field use traditional procedures to figure out the severity of Ascochyta blight, lead to invalid fungicide treatment. In this work, we created customized version of VGGNet model to identify the Ascochyta blight’s severity level. For noise reduction, we combined the Gaussian and Adaptive Median Filters; for optimization, we employed the Pelican Optimization Algorithm (POA). The model categorizes the chickpea images into five groups according to the severity of the disease: Asymptomatic, Resistant, Moderately Resistant, Susceptible, and Highly Susceptible. The study’s findings indicate that the customized VGGNet outperformed the other models, achieving an accuracy of 96%. |
| format | Article |
| id | doaj-art-17b1bdf00b0e44d1bc904d669f2850f2 |
| institution | Kabale University |
| issn | 2331-1932 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Food & Agriculture |
| spelling | doaj-art-17b1bdf00b0e44d1bc904d669f2850f22024-12-13T09:52:52ZengTaylor & Francis GroupCogent Food & Agriculture2331-19322024-12-0110110.1080/23311932.2024.2336002POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpeaMelaku Bitew Haile0Abebech Jenber Belay1Department of Information Technology, College of Informatics, University of Gondar, Gondar, EthiopiaDepartment of Information Technology, College of Informatics, University of Gondar, Gondar, EthiopiaChickpeas (Cicer arietinum L.) are a nutritious legume crop farmed on 17.8 million hectares in 56 countries throughout the world, with an estimated yearly yield of 14.78 million tones. Ethiopia is the leader in chickpea production on the African continent and the sixth-largest producer globally. However, Ascochyta rabiei remains a serious disease of chickpeas. If Ascochyta rabies is not managed, its effects on chickpea output could be either partial or complete under favourable environmental conditions. Knowing the severity level of this disease in farmlands where chickpeas are grown has an impact on the rates of yield and quality losses. Currently, Ethiopian farmers and pathologists in the field use traditional procedures to figure out the severity of Ascochyta blight, lead to invalid fungicide treatment. In this work, we created customized version of VGGNet model to identify the Ascochyta blight’s severity level. For noise reduction, we combined the Gaussian and Adaptive Median Filters; for optimization, we employed the Pelican Optimization Algorithm (POA). The model categorizes the chickpea images into five groups according to the severity of the disease: Asymptomatic, Resistant, Moderately Resistant, Susceptible, and Highly Susceptible. The study’s findings indicate that the customized VGGNet outperformed the other models, achieving an accuracy of 96%.https://www.tandfonline.com/doi/10.1080/23311932.2024.2336002Chickpeaascochyta blight of chickpeacomputer visionimage classification VGGNet modelsManuel Tejada, Universidad de Sevilla, SpainAgriculture and Food |
| spellingShingle | Melaku Bitew Haile Abebech Jenber Belay POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea Cogent Food & Agriculture Chickpea ascochyta blight of chickpea computer vision image classification VGGNet models Manuel Tejada, Universidad de Sevilla, Spain Agriculture and Food |
| title | POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea |
| title_full | POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea |
| title_fullStr | POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea |
| title_full_unstemmed | POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea |
| title_short | POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea |
| title_sort | poa optimized vgg16 svm architecture for severity level classification of ascochyta blight of chickpea |
| topic | Chickpea ascochyta blight of chickpea computer vision image classification VGGNet models Manuel Tejada, Universidad de Sevilla, Spain Agriculture and Food |
| url | https://www.tandfonline.com/doi/10.1080/23311932.2024.2336002 |
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