Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops
Abstract Timely identification and management of plant diseases are critical for sustaining crop yields and ensuring food security. This study proposes an innovative approach to detect Rhizoctonia aerial blight (RAB) caused by Rhizoctonia solani, a prevalent disease-causing substantial loss in soybe...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01191-w |
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| author | Mukta Nainwal Anurag Satpathi Saqib Shamsi Ali Salem Ajeet Singh Nain Dinesh Kumar Vishwakarma Ahmed Elbeltagi Salah El-Hendawy Mohamed A. Mattar |
| author_facet | Mukta Nainwal Anurag Satpathi Saqib Shamsi Ali Salem Ajeet Singh Nain Dinesh Kumar Vishwakarma Ahmed Elbeltagi Salah El-Hendawy Mohamed A. Mattar |
| author_sort | Mukta Nainwal |
| collection | DOAJ |
| description | Abstract Timely identification and management of plant diseases are critical for sustaining crop yields and ensuring food security. This study proposes an innovative approach to detect Rhizoctonia aerial blight (RAB) caused by Rhizoctonia solani, a prevalent disease-causing substantial loss in soybean crops in Uttarakhand, India. By integrating smartphone imageries into sophisticated algorithms, our automated system offers a scalable solution for disease detection. We evaluated nine machine learning algorithms, including logistic regression, Support Vector Machine (SVM), VGG-16 (with and without augmentation), ResNet-18 (with, without augmentation and larger image sizes) and ResNet-34 (with and without augmentation), for disease classification. Results demonstrate the effectiveness of our approach, with classification accuracies ranging from 66.77% (logistic regression) to 95.64% (ResNet-34). Particularly, the ResNet-34 model with data augmentation achieved the highest accuracy, showcasing its potential for accurate disease detection. Leveraging advanced computer technologies and smartphone imaging, this study presents a practical solution for enhancing crop management practices and minimizing yield losses due to diseases. The source code for our implementation is available in supportive file. |
| format | Article |
| id | doaj-art-6fd96f98d18646faab8a1bf9f748debf |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-6fd96f98d18646faab8a1bf9f748debf2025-08-20T02:05:13ZengSpringerOpenJournal of Big Data2196-11152025-06-0112113810.1186/s40537-025-01191-wPhotograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean cropsMukta Nainwal0Anurag Satpathi1Saqib Shamsi2Ali Salem3Ajeet Singh Nain4Dinesh Kumar Vishwakarma5Ahmed Elbeltagi6Salah El-Hendawy7Mohamed A. Mattar8Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and TechnologyDepartment of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and TechnologyDepartment of Computer Science, G.B. Pant University of Agriculture and TechnologyStructural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of PécsDepartment of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and TechnologyDepartment of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and TechnologyAgricultural Engineering Department, Faculty of Agriculture, Mansoura UniversityDepartment of Plant Production, College of Food and Agricultural Sciences, King Saud UniversityDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud UniversityAbstract Timely identification and management of plant diseases are critical for sustaining crop yields and ensuring food security. This study proposes an innovative approach to detect Rhizoctonia aerial blight (RAB) caused by Rhizoctonia solani, a prevalent disease-causing substantial loss in soybean crops in Uttarakhand, India. By integrating smartphone imageries into sophisticated algorithms, our automated system offers a scalable solution for disease detection. We evaluated nine machine learning algorithms, including logistic regression, Support Vector Machine (SVM), VGG-16 (with and without augmentation), ResNet-18 (with, without augmentation and larger image sizes) and ResNet-34 (with and without augmentation), for disease classification. Results demonstrate the effectiveness of our approach, with classification accuracies ranging from 66.77% (logistic regression) to 95.64% (ResNet-34). Particularly, the ResNet-34 model with data augmentation achieved the highest accuracy, showcasing its potential for accurate disease detection. Leveraging advanced computer technologies and smartphone imaging, this study presents a practical solution for enhancing crop management practices and minimizing yield losses due to diseases. The source code for our implementation is available in supportive file.https://doi.org/10.1186/s40537-025-01191-wSoybean crop diseasesMachine learningDisease detectionSmartphone imaging |
| spellingShingle | Mukta Nainwal Anurag Satpathi Saqib Shamsi Ali Salem Ajeet Singh Nain Dinesh Kumar Vishwakarma Ahmed Elbeltagi Salah El-Hendawy Mohamed A. Mattar Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops Journal of Big Data Soybean crop diseases Machine learning Disease detection Smartphone imaging |
| title | Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops |
| title_full | Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops |
| title_fullStr | Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops |
| title_full_unstemmed | Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops |
| title_short | Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops |
| title_sort | photograph based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops |
| topic | Soybean crop diseases Machine learning Disease detection Smartphone imaging |
| url | https://doi.org/10.1186/s40537-025-01191-w |
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