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This study proposes an innovative approach to detect Rhizoctonia aerial blight (RAB) caused by <jats:italic>Rhizoctonia solani<\/jats:italic>, 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.<\/jats:p>","DOI":"10.1186\/s40537-025-01191-w","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T16:52:32Z","timestamp":1749142352000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops"],"prefix":"10.1186","volume":"12","author":[{"given":"Mukta","family":"Nainwal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5884-4113","authenticated-orcid":false,"given":"Anurag","family":"Satpathi","sequence":"additional","affiliation":[]},{"given":"Saqib","family":"Shamsi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6176-8345","authenticated-orcid":false,"given":"Ali","family":"Salem","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4210-9779","authenticated-orcid":false,"given":"Ajeet Singh","family":"Nain","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-6995","authenticated-orcid":false,"given":"Dinesh Kumar","family":"Vishwakarma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-9502","authenticated-orcid":false,"given":"Ahmed","family":"Elbeltagi","sequence":"additional","affiliation":[]},{"given":"Salah","family":"El-Hendawy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7506-3036","authenticated-orcid":false,"given":"Mohamed A.","family":"Mattar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"key":"1191_CR1","unstructured":"SOPA. 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