{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:08:10Z","timestamp":1772910490420,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16467-7","type":"journal-article","created":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T07:01:42Z","timestamp":1692428502000},"page":"26971-26999","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Feature analysis and classification of maize crop diseases employing AlexNet-inception network"],"prefix":"10.1007","volume":"83","author":[{"given":"Gayathri Devi","family":"K","sequence":"first","affiliation":[]},{"given":"Kishore","family":"Balasubramanian","sequence":"additional","affiliation":[]},{"given":"Senthilkumar","family":"C","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"16467_CR1","unstructured":"Amara, J, Bouaziz, B, Algergawy, A (2017) A deep learning-based approach for banana leaf diseases classification. In Gesellschaft f\u00fcr Informatik"},{"issue":"1","key":"16467_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.33969\/ais.2020.21002","volume":"2","author":"J Arora","year":"2020","unstructured":"Arora J, Agrawal U, Sharma P (2020) Classification of Maize leaf diseases from healthy leaves using Deep Forest. J Artif Intell Syst 2(1):14\u201326. https:\/\/doi.org\/10.33969\/ais.2020.21002","journal-title":"J Artif Intell Syst"},{"key":"16467_CR3","doi-asserted-by":"crossref","unstructured":"Arroyo, JA, Gomez-Castaneda, C, Ruiz, E, Munoz de Cote, E, Gavi, F, Sucar, LE (2017) UAV technology and machine learning techniques applied to the yield improvement in precision agriculture. 2017 IEEE Mexican Humanitarian Technology Conference (MHTC)","DOI":"10.1109\/MHTC.2017.8006410"},{"issue":"4","key":"16467_CR4","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1504\/ijcistudies.2021.10044487","volume":"10","author":"K Balasubramanian","year":"2021","unstructured":"Balasubramanian K (2021) Performance of convolutional neural networks optimizers: an extensive evaluation on glaucoma prediction. Int J Comput Intell Stud 10(4):217\u2013231. https:\/\/doi.org\/10.1504\/ijcistudies.2021.10044487","journal-title":"Int J Comput Intell Stud"},{"issue":"12","key":"16467_CR5","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.3390\/agriculture12122047","volume":"12","author":"Y Chen","year":"2022","unstructured":"Chen Y, Chen X, Lin J, Pan R, Cao T, Cai J, Yu D, Cernava T, Zhang X (2022) DFCANet: A novel lightweight convolutional neural network model for corn disease identification. Agriculture 12(12):2047. https:\/\/doi.org\/10.3390\/agriculture12122047","journal-title":"Agriculture"},{"key":"16467_CR6","doi-asserted-by":"crossref","unstructured":"Da Rocha, Erik L, Rodrigues L, Mari JF (2020) Maize leaf disease classification using convolutional neural networks and hyperparameter optimization. In: Anais do XVI Workshop de Vis\u00e3o Computacional, pp 104\u2013110","DOI":"10.5753\/wvc.2020.13489"},{"key":"16467_CR7","doi-asserted-by":"crossref","unstructured":"Daneshwari, AN, Basavaraju, DR (2022) Corn leaf image classification based on machine learning techniques for accurate leaf disease detection. Int J Electric Comput Eng, 12(3)","DOI":"10.11591\/ijece.v12i3.pp2509-2516"},{"key":"16467_CR8","doi-asserted-by":"crossref","unstructured":"Devi KG, Rath M, Linh NTD (2020) Artificial intelligence trends for data analytics using machine learning and deep learning approaches. CRC Press","DOI":"10.1201\/9780367854737"},{"issue":"04","key":"16467_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.jrs.11.042621","volume":"11","author":"JG Ha","year":"2017","unstructured":"Ha JG, Moon H, Kwak JT, Hassan SI, Dang M, New Lee O, Park HY (2017) Deep convolutional neural network for classifying fusarium wilt of radish from unmanned aerial vehicles. J Appl Remote Sens 11(04):1. https:\/\/doi.org\/10.1117\/1.jrs.11.042621","journal-title":"J Appl Remote Sens"},{"key":"16467_CR10","unstructured":"Hanson, AMGJ, Joel, MG, Joy, A, Francis, J (2017) Plant leaf disease detection using deep learning and convolutional neural network. Int J Eng Sci"},{"issue":"1","key":"16467_CR11","doi-asserted-by":"publisher","first-page":"51","DOI":"10.21609\/jiki.v12i1.695","volume":"12","author":"A Hidayat","year":"2019","unstructured":"Hidayat A, Darusalam U, Irmawati I (2019) Detection of disease on corn plants using convolutional neural network methods. Jurnal Ilmu Komputer Dan Informasi 12(1):51\u201356. https:\/\/doi.org\/10.21609\/jiki.v12i1.695","journal-title":"Jurnal Ilmu Komputer Dan Informasi"},{"issue":"5","key":"16467_CR12","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/lgrs.2017.2681128","volume":"14","author":"N Kussul","year":"2017","unstructured":"Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett A Publ IEEE Geosci Remote Sens Soc 14(5):778\u2013782. https:\/\/doi.org\/10.1109\/lgrs.2017.2681128","journal-title":"IEEE Geosci Remote Sens Lett A Publ IEEE Geosci Remote Sens Soc"},{"issue":"1","key":"16467_CR13","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/sym10010011","volume":"10","author":"B Liu","year":"2017","unstructured":"Liu B, Zhang Y, He D, Li Y (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1):11. https:\/\/doi.org\/10.3390\/sym10010011","journal-title":"Symmetry"},{"key":"16467_CR14","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neucom.2017.06.023","volume":"267","author":"Y Lu","year":"2017","unstructured":"Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378\u2013384. https:\/\/doi.org\/10.1016\/j.neucom.2017.06.023","journal-title":"Neurocomputing"},{"key":"16467_CR15","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 (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci"},{"issue":"5","key":"16467_CR16","first-page":"1015","volume":"16","author":"C Ren","year":"2020","unstructured":"Ren C, Kim DK, Jeong D (2020) A survey of deep learning in agriculture: techniques and their applications. J Inf Process Syst 16(5):1015\u20131033","journal-title":"J Inf Process Syst"},{"key":"16467_CR17","doi-asserted-by":"crossref","unstructured":"Ristorto, G, Mazzetto, F, Guglieri, G, Quagliotti, F (2015) Monitoring performances and cost estimation of multirotor unmanned aerial systems in precision farming. 2015 international conference on unmanned aircraft systems (ICUAS)","DOI":"10.1109\/ICUAS.2015.7152329"},{"key":"16467_CR18","doi-asserted-by":"crossref","unstructured":"Rothe, PR, Kshirsagar, RV (2015) Cotton leaf disease identification using pattern recognition techniques. 2015 international conference on pervasive computing (ICPC)","DOI":"10.1109\/PERVASIVE.2015.7086983"},{"key":"16467_CR19","doi-asserted-by":"crossref","unstructured":"Sheikh MH, Mim TT, Reza MS, Rabby ASA, Hossain SA (2019) Detection of maize and peach leaf diseases using image processing. In: 2019 10th international conference on computing, Communication and Networking Technologies (ICCCNT), pp 1\u20137","DOI":"10.1109\/ICCCNT45670.2019.8944530"},{"issue":"1","key":"16467_CR20","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3390\/agriengineering1010009","volume":"1","author":"M Sibiya","year":"2019","unstructured":"Sibiya M, Sumbwanyambe M (2019) A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering 1(1):119\u2013131. https:\/\/doi.org\/10.3390\/agriengineering1010009","journal-title":"AgriEngineering"},{"issue":"3","key":"16467_CR21","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1089\/big.2021.0218","volume":"10","author":"M Subramanian","year":"2022","unstructured":"Subramanian M, Narasimha Prasad LV, Ve S (2022) Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data 10(3):215\u2013229. https:\/\/doi.org\/10.1089\/big.2021.0218","journal-title":"Big Data"},{"key":"16467_CR22","doi-asserted-by":"crossref","unstructured":"Yallappa, D, Veerangouda, M, Maski, D, Palled, V, Bheemanna, M (2017) Development and evaluation of drone mounted sprayer for pesticide applications to crops. 2017 IEEE global humanitarian technology conference (GHTC)","DOI":"10.1109\/GHTC.2017.8239330"},{"key":"16467_CR23","doi-asserted-by":"publisher","first-page":"143824","DOI":"10.1109\/access.2021.3120379","volume":"9","author":"H Yu","year":"2021","unstructured":"Yu H, Liu J, Chen C, Heidari AA, Zhang Q, Chen H, Mafarja M, Turabieh H (2021) Corn leaf diseases diagnosis based on K-means clustering and deep learning. IEEE Access: Pract Innov Open Solutions 9:143824\u2013143835. https:\/\/doi.org\/10.1109\/access.2021.3120379","journal-title":"IEEE Access: Pract Innov Open Solutions"},{"issue":"106943","key":"16467_CR24","doi-asserted-by":"publisher","first-page":"106943","DOI":"10.1016\/j.compag.2022.106943","volume":"197","author":"W Zeng","year":"2022","unstructured":"Zeng W, Li H, Hu G, Liang D (2022) Lightweight dense-scale network (LDSNet) for corn leaf disease identification. Comput Electron Agric 197(106943):106943. https:\/\/doi.org\/10.1016\/j.compag.2022.106943","journal-title":"Comput Electron Agric"},{"key":"16467_CR25","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 (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access: Pract Innov Open Solutions 6:30370\u201330377. https:\/\/doi.org\/10.1109\/access.2018.2844405","journal-title":"IEEE Access: Pract Innov Open Solutions"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16467-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16467-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16467-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T03:27:05Z","timestamp":1709522825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16467-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,19]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16467"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16467-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,19]]},"assertion":[{"value":"24 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest and no potential funding.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}