{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T08:51:58Z","timestamp":1781772718341,"version":"3.54.5"},"reference-count":52,"publisher":"China Science Publishing & Media Ltd.","issue":"2","content-domain":{"domain":["engine.scichina.com"],"crossmark-restriction":false},"short-container-title":["DI"],"published-print":{"date-parts":[[2026,6,1]]},"DOI":"10.3724\/2096-7004.di.2025.0097","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T03:18:58Z","timestamp":1781061538000},"page":"20250097","update-policy":"https:\/\/doi.org\/10.1360\/scp-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Maize Plant Disease Classification using Federated Machine Learning in Smart Agriculture"],"prefix":"10.3724","volume":"8","author":[{"given":"Ghousia","family":"Usman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usman","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samreen","family":"Safdar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Komal","family":"Bashir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayesha","family":"Adnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asra","family":"Nasir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saba","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2026","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"null","unstructured":"Ahila Priyadharshini R., Arivazhagan S., Arun M., and Mirnalini A., \u201cMaize leaf disease classification using deep convolutional neural networks,\u201d Neural Computing and Applications, vol. 31, pp. 8887\u20138895, 2019."},{"key":"null","unstructured":"Amin H., Darwish A., Hassanien A. E., and Soliman M., \u201cEnd-to-end deep learning model for corn leaf disease classification,\u201d IEEE Access, vol. 10, pp. 31103\u201331115, 2022."},{"key":"null","unstructured":"Shaikh T. A., Rasool T., and Lone F. R., \u201cTowards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming,\u201d Computers and Electronics in Agriculture, vol. 198, Art. no. 107119, 2022."},{"key":"null","unstructured":"Dayan I. et al., \u201cFederated learning for predicting clinical outcomes in patients with COVID-19,\u201d Nature Medicine, vol. 27, no. 10, pp. 1735\u20131743, 2021."},{"key":"null","unstructured":"da Rocha E. L., Rodrigues L., and Mari J. F., \u201cMaize leaf disease classification using convolutional neural networks and hyperparameter optimization,\u201d in Proc. XVI Workshop de Vis\u00e3o Computacional, 2020, pp. 104\u2013110."},{"key":"null","unstructured":"Bochie K., Sammarco M., Detyniecki M., and Campista M. E., \u201cAn\u00e1lise do aprendizado federado em redes m\u00f3veis,\u201d in Proc. XXXIX Simp\u00f3sio Brasileiro de Redes de Computadores e Sistemas Distribu\u00eddos, 2021, pp. 71\u201384."},{"key":"null","unstructured":"El Houby E. M., \u201cA survey on applying machine learning techniques for management of diseases,\u201d Journal of Applied Biomedicine, vol. 16, no. 3, pp. 165\u2013174, 2018."},{"key":"null","unstructured":"Zhang C., Xie Y., Bai H., Yu B., Li W., and Gao Y., \u201cA survey on federated learning,\u201d Knowledge-Based Systems, vol. 216, Art. no. 106775, 2021."},{"key":"null","unstructured":"Alehegn E., \u201cEthiopian maize diseases recognition and classification using support vector machine,\u201d International Journal of Computational Vision and Robotics, vol. 9, no. 1, pp. 90\u2013109, 2019."},{"key":"null","unstructured":"Saeed F. et al., \u201cDeep neural network features fusion and selection based on PLS regression with an application for crops diseases classification,\u201d Applied Soft Computing, vol. 103, Art. no. 107164, 2021."},{"key":"null","unstructured":"Panigrahi K. P., Das H., Sahoo A. K., and Moharana S. C., \u201cMaize leaf disease detection and classification using machine learning algorithms,\u201d in Progress in Computing, Analytics and Networking. Singapore: Springer, 2020, pp. 659\u2013669."},{"key":"null","unstructured":"Krizhevsky A., Sutskever I., and Hinton G. E., \u201cImageNet classification with deep convolutional neural networks,\u201d in Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA, Dec.2012, vol. 25, pp.1097-1105."},{"key":"null","unstructured":"Ghane S. et al., \u201cFederated proximal policy optimization with action masking: Application in collective heating systems,\u201d Energy and AI, vol. 20, Art. no. 100506, May 2025, doi: 10.1016\/j.egyai.2025.100506."},{"key":"null","unstructured":"Li T., Sahu A. K., Talwalkar A., and Smith V., \u201cFederated learning: Challenges, methods, and future directions,\u201d IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50\u201360, 2020."},{"key":"null","unstructured":"Zhang Y.-D. et al., \u201cImage-based fruit category classification by 13-layer deep CNN and data augmentation,\u201d Multimedia Tools and Applications, vol. 78, pp. 3613\u20133632, 2019."},{"key":"null","unstructured":"Zhao Y. et al., \u201cSEV-Net: Residual network embedded with attention mechanism for plant disease severity detection,\u201d Concurrency and Computation: Practice and Experience, vol. 33, no. 10, Art. no. e6161, 2021."},{"key":"null","unstructured":"Abbas A., Jain S., Gour M., and Vankudothu S., \u201cTomato plant disease detection using transfer learning with C-GAN synthetic images,\u201d Computers and Electronics in Agriculture, vol. 187, Art. no. 106279, 2021."},{"key":"null","unstructured":"Militante S. V., Gerardo B. D., and Dionisio N. V., \u201cPlant leaf detection and disease recognition using deep learning,\u201d in Proc. IEEE Int. Conf. Electron. Comput. Eng., 2019, pp. 579\u2013582."},{"key":"null","unstructured":"Saleem M. H., Potgieter J., and Arif K. M., \u201cPlant disease detection and classification by deep learning,\u201d Plants, vol. 8, no. 11, Art. no. 468, 2019."},{"key":"null","unstructured":"Bagga M. and Goyal S., \u201cImage-based detection and classification of plant diseases using deep learning: State-of-the-art review,\u201d Urban Agriculture & Regional Food Systems, vol. 9, no. 1, Art. no. e20053, 2024."},{"key":"null","unstructured":"Ahmed I. and Yadav P. K., \u201cPlant disease detection using machine learning approaches,\u201d Expert Systems, vol. 40, no. 5, Art. no. e13136, 2023."},{"key":"null","unstructured":"DeChant C. et al., \u201cAutomated identification of northern leaf blight-infected maize plants from field imagery using deep learning,\u201d Phytopathology, vol. 107, no. 11, pp. 1426\u20131432, 2017."},{"key":"null","unstructured":"Lin Z. et al., \u201cMultichannel CNN for maize leaf disease identification,\u201d Transactions of the ASABE, vol. 61, no. 5, pp. 1461\u20131474, 2018."},{"key":"null","unstructured":"Zhang X. et al., \u201cIdentification of maize leaf diseases using improved deep CNNs,\u201d IEEE Access, vol. 6, pp. 30370\u201330377, 2018."},{"key":"null","unstructured":"Waheed A. et al., \u201cOptimized DenseNet for disease recognition in corn leaf,\u201d Computers and Electronics in Agriculture, vol. 175, Art. no. 105456, 2020."},{"key":"null","unstructured":"Haque M. A. et al., \u201cDeep learning-based approach for identification of diseases of maize crop,\u201d Scientific Reports, vol. 12, Art. no. 6334, 2022."},{"key":"null","unstructured":"Moreira R. et al., \u201cAgroLens: Smart farm architecture for real-time leaf disease diagnostics,\u201d Internet of Things, vol. 19, Art. no. 100570, 2022."},{"key":"null","unstructured":"Subramanian M., Lv N. P., and S V. E., \u201cBayesian optimization for VGG16 transfer learning on corn leaves,\u201d Big Data, vol. 10, no. 3, pp. 215\u2013229, 2022."},{"key":"null","unstructured":"Subramanian M., Shanmugavadivel K., and Nandhini P. S., \u201cFine-tuning deep learning models for maize leaves disease identification,\u201d Neural Computing and Applications, vol. 34, no. 16, pp. 13951\u201313968, 2022."},{"key":"null","unstructured":"Antico T. M., Moreira L. F., and Moreira R., \u201cEvaluating federated learning for maize leaf disease prediction,\u201d in Proc. ENIAC, 2022."},{"key":"null","unstructured":"Mamba Kabala D. et al., \u201cImage-based crop disease detection with federated learning,\u201d Scientific Reports, vol. 13, Art. no. 19220, 2023."},{"key":"null","unstructured":"Setiawan W. et al., \u201cAlexNet and SqueezeNet for maize leaf disease classification,\u201d Kinetik, 2021."},{"key":"null","unstructured":"Ojo M. O. and Zahid A., \u201cImproving deep learning classifiers via preprocessing and class imbalance approaches,\u201d Agronomy, vol. 13, no. 3, Art. no. 887, 2023."},{"key":"null","unstructured":"Alomar K., Aysel H. I., and Cai X., \u201cData augmentation in classification and segmentation: A survey,\u201d Journal of Imaging, vol. 9, no. 2, Art. no. 46, 2023."},{"key":"null","unstructured":"Islam M. et al., \u201cFederated learning and CNN ensembles for brain tumor MRI,\u201d Neural Processing Letters, vol. 55, no. 4, pp. 3779\u20133809, 2023."},{"key":"null","unstructured":"Sladojevic S. et al., \u201cDeep learning-based plant disease recognition,\u201d Computational Intelligence and Neuroscience, Art. no. 3289801, 2016."},{"key":"null","unstructured":"Kebaili A., Lapuyade-Lahorgue J., and Ruan S., \u201cDeep learning approaches for data augmentation in medical imaging: A review,\u201d Journal of Imaging, vol. 9, no. 4, Art. no. 81, 2023."},{"key":"null","unstructured":"Gulli A. and Pal S., Deep Learning with Keras. Birmingham, U.K.: Packt, 2017."},{"key":"null","unstructured":"Suryavanshi A. et al., \u201cFederated CNNs in banana leaf disease detection,\u201d in Proc. INOCON, 2024."},{"key":"null","unstructured":"He K., Zhang X., Ren S., and Sun J., \u201cDeep residual learning for image recognition,\u201d in Proc. CVPR, 2016, pp. 770\u2013778."},{"key":"null","unstructured":"Wen J. et al., \u201cA survey on federated learning: Challenges and applications,\u201d International Journal of Machine Learning and Computing, vol. 14, no. 2, pp. 513\u2013535, 2023."},{"key":"null","unstructured":"McMahan B. et al., \u201cCommunication-efficient learning of deep networks from decentralized data,\u201d in Proc. AISTATS, 2017, pp. 1273\u20131282."},{"key":"null","unstructured":"Ye Y. et al., \u201cEdgeFed: Optimized federated learning based on edge computing,\u201d IEEE Access, vol. 8, pp. 209191\u2013209198, 2020."},{"key":"null","unstructured":"Li Q. et al., \u201cA survey on federated learning systems,\u201d IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3347\u20133366, 2021."},{"key":"null","unstructured":"Shu X. and Ye Y., \u201cKnowledge discovery: Methods from data mining and machine learning,\u201d Social Science Research, vol. 110, Art. no. 102817, 2023."},{"key":"null","unstructured":"Tan Y. N. et al., \u201cTransfer learning for breast cancer in a federated learning framework,\u201d IEEE Access, vol. 11, pp. 27462\u201327476, 2023."},{"key":"null","unstructured":"Hogan J. and Adams N. M., \u201cOn averaging ROC curves,\u201d Transactions on Machine Learning Research, 2023."},{"key":"null","unstructured":"Dhaka V. S. et al., \u201cSurvey of deep learning techniques for plant disease classification,\u201d Multimedia Tools and Applications, vol. 80, no. 1, pp. 1\u201334, 2021."},{"key":"null","unstructured":"Picon A. et al., \u201cEvaluation of computer vision techniques for plant disease classification,\u201d Computers and Electronics in Agriculture, vol. 178, Art. no. 105766, 2020."},{"key":"null","unstructured":"McMahan H. B. et al., \u201cCommunication-efficient learning of deep networks from decentralized data,\u201d in Proc. AISTATS, vol. 54, 2017, pp. 1273\u20131282."},{"key":"null","unstructured":"Kone\u010dn\u00fd J. et al., \u201cFederated learning: Strategies for improving communication efficiency,\u201d arXiv preprint arXiv:1610.05492, 2017."},{"key":"null","unstructured":"Yang Q., Liu Y., Chen T., and Tong Y., \u201cFederated machine learning: Concept and applications,\u201d ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1\u201319, 2019."}],"container-title":["Data Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.sciengine.com\/sci-open\/api\/v1\/open\/file\/pdf\/76590CC85F2441EF89F3EBF80547578B","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciengine.com\/doi\/10.3724\/2096-7004.di.2025.0097","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciengine.com\/sci-open\/api\/v1\/open\/file\/pdf\/76590CC85F2441EF89F3EBF80547578B","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T08:00:36Z","timestamp":1781769636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciengine.com\/doi\/10.3724\/2096-7004.di.2025.0097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,6,10]]},"published-print":{"date-parts":[[2026,6,1]]}},"URL":"https:\/\/doi.org\/10.3724\/2096-7004.di.2025.0097","relation":{},"ISSN":["2096-7004"],"issn-type":[{"value":"2096-7004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6,1]]}}}