{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:47:30Z","timestamp":1778604450861,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Manipal"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The maize leaf diseases create severe yield reductions and critical problems. The maize leaf disease should be discovered early, perfectly identified, and precisely diagnosed to make greater yield. This work studies three main leaf diseases: common rust, blight, and grey leaf spot. This approach involves pre-processing, including sampling and labelling, while ensuring class balance and preventing overfitting via the SMOTE algorithm. The maize leaf dataset with augmentation was used to classify these diseases using several deep-learning pre-trained networks, including VGG16, Resnet34, Resnet50, and SqueezeNet. The model was evaluated using a maize leaf dataset that included various leaf classes, mini-batch sizes, and input sizes. Performance measures, recall, precision, accuracy, F1-score, and confusion matrix were computed for each network. The SqueezeNet learning model produces an accuracy of 97% in classifying four different classes of plant leaf datasets. Comparatively, the SqueezeNet learning model has improved accuracy by 2\u20135% and reduced the mean square error by 4\u201311% over VGG16, Resnet34, and Resnet50 deep learning models.<\/jats:p>","DOI":"10.1186\/s40537-024-00972-z","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T14:02:18Z","timestamp":1723298538000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases"],"prefix":"10.1186","volume":"11","author":[{"given":"Prasannavenkatesan","family":"Theerthagiri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. Usha","family":"Ruby","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. George Chellin","family":"Chandran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanvir Habib","family":"Sardar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahamed","family":"Shafeeq B. M.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"key":"972_CR1","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salath\u00e9 M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1419.","journal-title":"Front Plant Sci"},{"issue":"1","key":"972_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2193-1801-2-660","volume":"2","author":"JG Arnal Barbedo","year":"2013","unstructured":"Arnal Barbedo JG. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus. 2013;2(1):1\u201312.","journal-title":"SpringerPlus"},{"key":"972_CR3","doi-asserted-by":"publisher","first-page":"106124","DOI":"10.1016\/j.compag.2021.106124","volume":"184","author":"FS Ishengoma","year":"2021","unstructured":"Ishengoma FS, Rai IA, Said RN. Identification of maize leaves infected by fall armyworms using UAV-based imagery and convolutional neural networks. Comput Electron Agric. 2021;184:106124.","journal-title":"Comput Electron Agric"},{"issue":"19","key":"972_CR4","doi-asserted-by":"publisher","first-page":"4161","DOI":"10.3390\/s19194161","volume":"19","author":"J Hang","year":"2019","unstructured":"Hang J, Zhang D, Chen P, Zhang J, Wang B. Classification of plant leaf diseases based on improved convolutional neural network. Sensors. 2019;19(19):4161.","journal-title":"Sensors"},{"issue":"1","key":"972_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-38966-0","volume":"9","author":"WJ Liang","year":"2019","unstructured":"Liang WJ, Zhang H, Zhang GF, Cao HX. Rice blast disease recognition using a deep convolutional neural network. Sci Rep. 2019;9(1):1\u201310. https:\/\/doi.org\/10.1038\/s41598-019-38966-0.","journal-title":"Sci Rep"},{"key":"972_CR6","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.compag.2016.01.008","volume":"121","author":"CL Chung","year":"2016","unstructured":"Chung CL, Huang KJ, Chen SY, Lai MH, Chen YC, Kuo YF. Detecting Bakanae disease in rice seedlings by machine vision. Comput Electron Agric. 2016;121:404\u201311. https:\/\/doi.org\/10.1016\/j.compag.2016.01.008.","journal-title":"Comput Electron Agric"},{"key":"972_CR7","doi-asserted-by":"publisher","unstructured":"Khirade SD, Patil AB. Plant disease detection using image processing. Proc Int Conf Comput Commun Control Autom. 2015;768\u2013771. https:\/\/doi.org\/10.1109\/ICCUBEA.2015.153.","DOI":"10.1109\/ICCUBEA.2015.153"},{"key":"972_CR8","doi-asserted-by":"crossref","unstructured":"Zhang LN, Yang B. (2014). Research on recognition of maize disease based on mobile internet and support vector machine technique. In Advanced Materials Research (Vol. 905, pp. 659\u2013662). Trans Tech Publications Ltd.","DOI":"10.4028\/www.scientific.net\/AMR.905.659"},{"issue":"1","key":"972_CR9","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3390\/agriengineering1010009","volume":"1","author":"M Sibiya","year":"2019","unstructured":"Sibiya M, Sumbwanyambe M. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering. 2019;1(1):119\u201331.","journal-title":"AgriEngineering"},{"key":"972_CR10","first-page":"100695","volume":"35","author":"W Zeng","year":"2022","unstructured":"Zeng W, Li H, Hu G, Liang D. Identification of maize leaf diseases by using the SKPSNet-50 convolutional neural network model. Sustainable Computing: Inf Syst. 2022;35:100695.","journal-title":"Sustainable Computing: Inf Syst"},{"key":"972_CR11","doi-asserted-by":"publisher","first-page":"30370","DOI":"10.1109\/ACCESS.2018.2844405","volume":"6","author":"X Zhang","year":"2018","unstructured":"Zhang X, Qiao Y, Meng F, Fan C, Zhang M. Identification of maize leaf diseases using improved deep convolutional neural networks. Ieee Access. 2018;6:30370\u20137.","journal-title":"Ieee Access"},{"key":"972_CR12","doi-asserted-by":"publisher","first-page":"57952","DOI":"10.1109\/ACCESS.2020.2982443","volume":"8","author":"M Lv","year":"2020","unstructured":"Lv M, Zhou G, He M, Chen A, Zhang W, Hu Y. Maize leaf disease identification based on feature enhancement and DMS-robust alexnet. IEEE Access. 2020;8:57952\u201366.","journal-title":"IEEE Access"},{"key":"972_CR13","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/978-981-15-2414-1_66","volume-title":"Progress in Computing, Analytics and networking","author":"KP Panigrahi","year":"2020","unstructured":"Panigrahi KP, Das H, Sahoo AK, Moharana SC. Maize leaf disease detection and classification using machine learning algorithms. Progress in Computing, Analytics and networking. Singapore: Springer; 2020. pp. 659\u201369."},{"key":"972_CR14","doi-asserted-by":"publisher","first-page":"33679","DOI":"10.1109\/ACCESS.2020.2973658","volume":"8","author":"J Sun","year":"2020","unstructured":"Sun J, Yang Y, He X, Wu X. Northern maize leaf blight detection under complex field environment based on deep learning. IEEE Access. 2020;8:33679\u201388.","journal-title":"IEEE Access"},{"key":"972_CR15","doi-asserted-by":"crossref","unstructured":"Aravind KR, Raja P, Mukesh KV, Aniirudh R, Ashiwin R, Szczepanski C. (2018, January). Disease classification in maize crop using bag of features and multiclass support vector machine. In 2018 2nd International Conference on Inventive Systems and Control (ICISC) (pp. 1191\u20131196). IEEE.","DOI":"10.1109\/ICISC.2018.8398993"},{"issue":"21","key":"972_CR16","doi-asserted-by":"publisher","first-page":"4218","DOI":"10.3390\/rs13214218","volume":"13","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wa S, Liu Y, Zhou X, Sun P, Ma Q. High-accuracy detection of Maize Leaf diseases CNN based on Multi-pathway activation function Module. Remote Sens. 2021;13(21):4218.","journal-title":"Remote Sens"},{"key":"972_CR17","doi-asserted-by":"crossref","unstructured":"Subramanian M, Shanmugavadivel K, Nandhini PS. (2022). On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Comput Appl, 1\u201318.","DOI":"10.1007\/s00521-022-07246-w"},{"issue":"5","key":"972_CR18","doi-asserted-by":"publisher","first-page":"6051","DOI":"10.1007\/s11042-021-11763-6","volume":"81","author":"RK Singh","year":"2022","unstructured":"Singh RK, Tiwari A, Gupta RK. Deep transfer modeling for classification of Maize Plant Leaf Disease. Multimedia Tools Appl. 2022;81(5):6051\u201367.","journal-title":"Multimedia Tools Appl"},{"key":"972_CR19","doi-asserted-by":"crossref","unstructured":"Cui S, Su YL, Duan K, Liu Y. (2022). Maize leaf disease classification using CBAM and lightweight Autoencoder network. J Ambient Intell Humaniz Comput, 1\u201311.","DOI":"10.1007\/s12652-022-04438-z"},{"issue":"12","key":"972_CR20","doi-asserted-by":"publisher","first-page":"8887","DOI":"10.1007\/s00521-019-04228-3","volume":"31","author":"R Ahila Priyadharshini","year":"2019","unstructured":"Ahila Priyadharshini R, Arivazhagan S, Arun M, Mirnalini A. Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl. 2019;31(12):8887\u201395.","journal-title":"Neural Comput Appl"},{"key":"972_CR21","doi-asserted-by":"crossref","unstructured":"Panigrahi KP, Sahoo AK, Das H. (2020, June). A cnn approach for corn leaves disease detection to support digital agricultural system. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 678\u2013683). IEEE.","DOI":"10.1109\/ICOEI48184.2020.9142871"},{"key":"972_CR22","doi-asserted-by":"crossref","unstructured":"Concepcion R, Dadios E, Alejandrino J, Mendigoria CH, Aquino H, Alajas OJ. (2021, April). Diseased surface assessment of maize cercospora leaf spot using hybrid gaussian quantum-behaved particle swarm and recurrent neural network. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1\u20136). IEEE.","DOI":"10.1109\/IEMTRONICS52119.2021.9422515"},{"issue":"15","key":"972_CR23","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.3390\/plants11151942","volume":"11","author":"HA Craze","year":"2022","unstructured":"Craze HA, Pillay N, Joubert F, Berger DK. Deep Learning Diagnostics of Gray Leaf Spot in Maize under mixed Disease Field conditions. Plants. 2022;11(15):1942.","journal-title":"Plants"},{"key":"972_CR24","doi-asserted-by":"crossref","unstructured":"Haque, M., Marwaha, S., Deb, C. K., Nigam, S., Arora, A., Hooda, K. S., ... & Agrawal, R. C. (2022). Deep learning-based approach for identification of diseases of maize crop. Scientific reports, 12(1), 1\u201314.","DOI":"10.1038\/s41598-022-10140-z"},{"key":"972_CR25","doi-asserted-by":"publisher","first-page":"101182","DOI":"10.1016\/j.ecoinf.2020.101182","volume":"61","author":"\u00dc Atila","year":"2021","unstructured":"Atila \u00dc, U\u00e7ar M, Akyol K, U\u00e7ar E. Plant leaf disease classification using EfficientNet deep learning model. Ecol Inf. 2021;61:101182.","journal-title":"Ecol Inf"},{"key":"972_CR26","unstructured":"Sodha N, Chanda K, Rawate S, Chaudhary S, Suryawanshi R, Vazalwar N. (2022). Plant Leaf Disease Prediction Using Deep Learning (No. 7569). EasyChair."},{"issue":"2","key":"972_CR27","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3390\/pathogens10020131","volume":"10","author":"M Sibiya","year":"2021","unstructured":"Sibiya M, Sumbwanyambe M. Automatic fuzzy logic-based maize common rust disease severity predictions with thresholding and deep learning. Pathogens. 2021;10(2):131.","journal-title":"Pathogens"},{"issue":"1","key":"972_CR28","first-page":"14","volume":"2","author":"J Arora","year":"2020","unstructured":"Arora J, Agrawal U. Classification of Maize leaf diseases from healthy leaves using Deep Forest. J Artif Intell Syst. 2020;2(1):14\u201326.","journal-title":"J Artif Intell Syst"},{"key":"972_CR29","unstructured":"Phytopathology, 107(11), 1426\u20131432."},{"key":"972_CR30","doi-asserted-by":"publisher","first-page":"100108","DOI":"10.1016\/j.atech.2022.100108","volume":"3","author":"LG Divyanth","year":"2023","unstructured":"Divyanth LG, Ahmad A, Saraswat D. A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery. Smart Agricultural Technol. 2023;3:100108.","journal-title":"Smart Agricultural Technol"},{"key":"972_CR31","unstructured":"Quality Enhancement Techniques. for Processing Fruits and Vegetables."},{"issue":"19","key":"972_CR32","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.3390\/rs11192209","volume":"11","author":"EL Stewart","year":"2019","unstructured":"Stewart EL, Wiesner-Hanks T, Kaczmar N, DeChant C, Wu H, Lipson H, Gore MA. Quantitative phenotyping of Northern Leaf Blight in UAV images using deep learning. Remote Sens. 2019;11(19):2209.","journal-title":"Remote Sens"},{"key":"972_CR33","doi-asserted-by":"crossref","unstructured":"Ahmad A, Saraswat D, Gamal E. A. (2022). A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agricultural Technol, 100083.","DOI":"10.1016\/j.atech.2022.100083"},{"key":"972_CR34","doi-asserted-by":"publisher","first-page":"106597","DOI":"10.1016\/j.asoc.2020.106597","volume":"96","author":"S Hern\u00e1ndez","year":"2020","unstructured":"Hern\u00e1ndez S, L\u00f3pez JL. Uncertainty quantification for plant disease detection using bayesian deep learning. Appl Soft Comput. 2020;96:106597.","journal-title":"Appl Soft Comput"},{"issue":"4","key":"972_CR35","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/S1537-5110(03)00098-9","volume":"85","author":"DG Sena Jr","year":"2003","unstructured":"Sena DG Jr, Pinto FAC, Queiroz DM, Viana PA. Fall armyworm damaged maize plant identification using digital images. Biosyst Eng. 2003;85(4):449\u201354.","journal-title":"Biosyst Eng"},{"key":"972_CR36","doi-asserted-by":"publisher","unstructured":"Kilaru R, Raju KM. (2022, February). Prediction of maize leaf disease detection to improve crop yield using machine learning-based models. In 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST) (pp. 212\u2013217). IEEE, https:\/\/doi.org\/10.1109\/ICRTCST54752.2022.9782023.","DOI":"10.1109\/ICRTCST54752.2022.9782023"},{"issue":"2","key":"972_CR37","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1007\/s13369-022-06851-0","volume":"48","author":"J He","year":"2023","unstructured":"He J, Liu T, Li L, Hu Y, Zhou G. MFaster r-CNN for maize leaf diseases detection based on machine vision. Arab J Sci Eng. 2023;48(2):1437\u201349.","journal-title":"Arab J Sci Eng"},{"issue":"7","key":"972_CR38","doi-asserted-by":"publisher","first-page":"19415","DOI":"10.1007\/s11042-023-16398-3","volume":"83","author":"CK Rai","year":"2024","unstructured":"Rai CK, Pahuja R. Northern maize leaf blight disease detection and segmentation using deep convolution neural networks. Multimedia Tools Appl. 2024;83(7):19415\u201332.","journal-title":"Multimedia Tools Appl"},{"key":"972_CR39","unstructured":"Kaggle Repository. https:\/\/www.kaggle.com\/datasets\/smaranjitghose\/corn-or-maize-leaf-disease-dataset."},{"key":"972_CR40","doi-asserted-by":"crossref","unstructured":"Tabassum H, Theerthagiri P. (2022, December). Performance Analysis of AI-based Learning Models on Leaf Disease Prediction. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 85\u201388). IEEE.","DOI":"10.1109\/I4C57141.2022.10057794"},{"key":"972_CR41","unstructured":"Prasannavenkatesan T, Vidya J. Cardiovascular Disease prediction using recursive feature elimination and gradient boosting classification techniques. Expert Syst, 2022."},{"key":"972_CR42","doi-asserted-by":"crossref","unstructured":"Theerthagiri P, Usha Ruby A, Vidya J. Diagnosis and classification of the diabetes using machine learning algorithms. SN Comput Sci, 4, 72, 2023.","DOI":"10.1007\/s42979-022-01485-3"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-00972-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-024-00972-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-00972-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T14:04:53Z","timestamp":1723298693000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-024-00972-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,10]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["972"],"URL":"https:\/\/doi.org\/10.1186\/s40537-024-00972-z","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,10]]},"assertion":[{"value":"13 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Nil.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data"}},{"value":"The authors declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"112"}}