{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T17:55:35Z","timestamp":1767894935017,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"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":["Wireless Netw"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11276-022-03150-2","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:14:28Z","timestamp":1668114868000},"page":"919-939","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Taylor-student psychology based optimization integrated deep learning in IoT application for plant disease classification"],"prefix":"10.1007","volume":"29","author":[{"given":"S.","family":"Vimala","sequence":"first","affiliation":[]},{"given":"T. V.","family":"Madhusudhana Rao","sequence":"additional","affiliation":[]},{"given":"A.","family":"Balaji","sequence":"additional","affiliation":[]},{"given":"Balajee","family":"Maram","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"3150_CR1","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s12652-020-02051-6","volume":"12","author":"M Mishra","year":"2021","unstructured":"Mishra, M., Choudhury, P., & Pati, B. (2021). Modified ride-NN optimizer for the IoT based plant disease detection. Journal of Ambient Intelligence and Humanized Computing, 12, 691\u2013703.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"4","key":"3150_CR2","first-page":"23","volume":"2","author":"PK Reddy","year":"2019","unstructured":"Reddy, P. K., & Rajasekhara Babu, M. (2019). Cluster head selection in IoT using enhanced self adaptive bat algorithm. Journal of Networking and Communication Systems (JNACS), 2(4), 23\u201332.","journal-title":"Journal of Networking and Communication Systems (JNACS)"},{"issue":"1","key":"3150_CR3","first-page":"20","volume":"3","author":"AV Dhumane","year":"2020","unstructured":"Dhumane, A. V., Markande, S. D., & Midhunchakkaravarthy, D. (2020). Multipath transmission in IoT using hybrid salp swarm-differential evolution algorithm. Journal of Networking and Communication Systems, 3(1), 20\u201330.","journal-title":"Journal of Networking and Communication Systems"},{"issue":"2","key":"3150_CR4","first-page":"1","volume":"3","author":"M Anandkumar","year":"2020","unstructured":"Anandkumar, M. (2020). Multicast routing in WSN using bat algorithm with genetic operators for IoT applications. Journal of Networking and Communication Systems, 3(2), 1\u20138.","journal-title":"Journal of Networking and Communication Systems"},{"key":"3150_CR5","doi-asserted-by":"crossref","unstructured":"Wang, X. F., Wang, Z., Zhang, S. W., & Shi, Y. (2015). Monitoring and discrimination of plant disease and insect pests based on agricultural IOT. In 4th International Conference on Information Technology and Management Innovation (pp. 112\u2013115). Atlantis Press.","DOI":"10.2991\/icitmi-15.2015.21"},{"key":"3150_CR6","doi-asserted-by":"crossref","unstructured":"Sabrol, H., & Satish, K. (2016). Tomato plant disease classification in digital images using classification tree. In 2016 International Conference on Communication and Signal Processing (ICCSP) (pp. 1242\u20131246).","DOI":"10.1109\/ICCSP.2016.7754351"},{"key":"3150_CR7","doi-asserted-by":"publisher","first-page":"105162","DOI":"10.1016\/j.compag.2019.105162","volume":"169","author":"JG Esgario","year":"2020","unstructured":"Esgario, J. G., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.","journal-title":"Computers and Electronics in Agriculture"},{"key":"3150_CR8","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.compag.2017.03.016","volume":"137","author":"MA Ebrahimi","year":"2017","unstructured":"Ebrahimi, M. A., Khoshtaghaza, M. H., Minaei, S., & Jamshidi, B. (2017). Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 137, 52\u201358. https:\/\/doi.org\/10.1016\/j.compag.2017.03.016","journal-title":"Computers and Electronics in Agriculture"},{"key":"3150_CR9","unstructured":"Deepa, S., & Umarani, R. (2017). Steganalysis on Images using SVM with Selected Hybrid Features of Gini Index Feature Selection Algorithm. International Journal of Advanced Research in Computer Science, 8(5)."},{"key":"3150_CR10","doi-asserted-by":"crossref","unstructured":"Guettari, N., Capelle-Laiz\u00e9, A. S., & Carr\u00e9, P. (2016). Blind image steganalysis based on evidential k-nearest neighbors. In: IEEE (pp. 2742\u20132746). Phoenix.","DOI":"10.1109\/ICIP.2016.7532858"},{"key":"3150_CR11","doi-asserted-by":"publisher","first-page":"105393","DOI":"10.1016\/j.compag.2020.105393","volume":"173","author":"J Chen","year":"2020","unstructured":"Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393.","journal-title":"Computers and Electronics in Agriculture"},{"issue":"2","key":"3150_CR12","first-page":"1","volume":"5","author":"A Kadir","year":"2014","unstructured":"Kadir, A. (2014). A model of plant identification system using GLCM, lacunarity and shen features. Research Journal of Pharmaceutical, Biological, and Chemical Sciences, 5(2), 1\u201310.","journal-title":"Research Journal of Pharmaceutical, Biological, and Chemical Sciences"},{"key":"3150_CR13","unstructured":"Naik, M. R., & Sivappagari, C. (2016). Plant Leaf and Disease Detection by using HSV features and SVM. IJESC, 6(12)."},{"key":"3150_CR14","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.inpa.2018.05.002","volume":"5","author":"K Golhani","year":"2018","unstructured":"Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2018). A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, 5, 354\u2013371.","journal-title":"Information Processing in Agriculture"},{"key":"3150_CR15","doi-asserted-by":"publisher","first-page":"106643","DOI":"10.1016\/j.compeleceng.2020.106643","volume":"85","author":"AR Hameed","year":"2020","unstructured":"Hameed, A. R., Ul Islam, S., Raza, M., & Khattak, H. A. (2020). Towards energy and performance-aware geographic routing for IoT-enabled sensor networks. Computers & Electrical Engineering, 85, 106643.","journal-title":"Computers & Electrical Engineering"},{"key":"3150_CR16","doi-asserted-by":"crossref","unstructured":"Deeba, K., & Amutha, B. (2020). ResNet-deep neural network architecture for leaf disease classification. Microprocessors and Microsystems, 103364.","DOI":"10.1016\/j.micpro.2020.103364"},{"key":"3150_CR17","doi-asserted-by":"publisher","first-page":"122537","DOI":"10.1016\/j.physa.2019.122537","volume":"535","author":"MM Ozguven","year":"2019","unstructured":"Ozguven, M. M., & Adem, K. (2019). Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 535, 122537.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"1","key":"3150_CR18","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s42161-020-00683-3","volume":"103","author":"VK Shrivastava","year":"2021","unstructured":"Shrivastava, V. K., & Pradhan, M. K. (2021). Rice plant disease classification using color features: A machine learning paradigm. Journal of Plant Pathology, 103(1), 17\u201326.","journal-title":"Journal of Plant Pathology"},{"key":"3150_CR19","doi-asserted-by":"publisher","first-page":"105456","DOI":"10.1016\/j.compag.2020.105456","volume":"175","author":"A Waheed","year":"2020","unstructured":"Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., & Pandey, H. M. (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175, 105456.","journal-title":"Computers and Electronics in Agriculture"},{"key":"3150_CR20","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, S., & Lopez, J. L. (2020). Uncertainty quantification for plant disease detection using Bayesian deep learning. Applied Soft Computing, 96.","DOI":"10.1016\/j.asoc.2020.106597"},{"key":"3150_CR21","doi-asserted-by":"crossref","unstructured":"Arg\u00fceso, D., Picon, A., Irusta, U., Medela, A., San-Emeterio, M. G., Bereciartua, A., & Alvarez-Gila, A. (2020). Few-shot learning approach for plant disease classification using images taken in the field. Computers and Electronics in Agriculture, 175.","DOI":"10.1016\/j.compag.2020.105542"},{"issue":"3","key":"3150_CR22","first-page":"460","volume":"6","author":"TD Nguyen","year":"2017","unstructured":"Nguyen, T. D., Khan, J. Y., & Ngo, D. T. (2017). An effective energy-harvesting-aware routing algorithm for WSN-based IoT applications. Journal of Emerging Technologies and Innovative Research, 6(3), 460\u2013469.","journal-title":"Journal of Emerging Technologies and Innovative Research"},{"key":"3150_CR23","doi-asserted-by":"crossref","unstructured":"Chen, Z., He, M., Liang, W., & Chen, K. (2015). Trust-aware and low energy consumption security topology protocol of wireless sensor network. Journal of Sensors.","DOI":"10.1155\/2015\/716468"},{"key":"3150_CR24","doi-asserted-by":"crossref","unstructured":"Mangai, S. A., Sankar, B. R., & Alagarsamy, K. (2014). Taylor series prediction of time series data with error propagated by artificial neural network. International Journal of Computer Applications, 89(1).","DOI":"10.5120\/15470-4112"},{"key":"3150_CR25","doi-asserted-by":"publisher","first-page":"102804","DOI":"10.1016\/j.advengsoft.2020.102804","volume":"146","author":"B Das","year":"2020","unstructured":"Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering Software, 146, 102804.","journal-title":"Advances in Engineering Software"},{"issue":"2","key":"3150_CR26","doi-asserted-by":"publisher","first-page":"e0212110","DOI":"10.1371\/journal.pone.0212110","volume":"14","author":"T L\u00f6fstedt","year":"2019","unstructured":"L\u00f6fstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., & Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLoS One, 14(2), e0212110.","journal-title":"PLoS One"},{"key":"3150_CR27","doi-asserted-by":"crossref","unstructured":"Freitas, P. G., Akamine, W. Y., & Farias, M. C. (2016). No-reference image quality assessment based on statistics of local ternary pattern. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX) (pp. 1\u20136).","DOI":"10.1109\/QoMEX.2016.7498959"},{"issue":"6","key":"3150_CR28","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1109\/TIP.2010.2042645","volume":"19","author":"X Tan","year":"2010","unstructured":"Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635\u20131650.","journal-title":"IEEE Transactions on Image Processing"},{"key":"3150_CR29","doi-asserted-by":"crossref","unstructured":"Fausto, F., Cuevas, E., & P\u00e9rez-Cisneros, M. (2016, November). An optimization based approach for maximizing the information content of keypoints detected on a digital image. In: Proceedings of the sixteenth mexican international conference on computer science (pp. 1\u20135).","DOI":"10.1145\/3149235.3149236"},{"key":"3150_CR30","doi-asserted-by":"publisher","first-page":"39974","DOI":"10.1109\/ACCESS.2019.2902846","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Liu, H., Zheng, W., Xia, Y., Li, Y., Chen, P., Guo, K., & Xie, H. (2019). Multi-objective workflow scheduling with deep-Q-network-based multi-agent reinforcement learning. IEEE Access, 7, 39974\u201339982.","journal-title":"IEEE Access"},{"key":"3150_CR31","unstructured":"Rice Leaf Diseases Data Set, https:\/\/archive.ics.uci.edu\/ml\/datasets\/Rice+Leaf+Diseases, Accessed on July 2021."},{"key":"3150_CR32","unstructured":"Image set for deep learning, https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6030791\/, Accessed on July 2021."},{"issue":"1","key":"3150_CR33","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s10586-019-02937-x","volume":"23","author":"A Serhani","year":"2020","unstructured":"Serhani, A., Naja, N., & Jamali, A. (2020). AQ-Routing: Mobility-, stability-aware adaptive routing protocol for data routing in MANET\u2013IoT systems. Cluster Computing, 23(1), 13\u201327.","journal-title":"Cluster Computing"},{"issue":"4","key":"3150_CR34","doi-asserted-by":"publisher","first-page":"28","DOI":"10.14569\/IJACSA.2020.0110405","volume":"11","author":"A Gedminas","year":"2020","unstructured":"Gedminas, A., Duoba, L., & Navakauskas, D. (2020). P system framework for Ant Colony algorithm in IoT data routing. International Journal of Advanced Computer Science and Applications, 11(4), 28\u201334.","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"3150_CR35","doi-asserted-by":"publisher","first-page":"102938","DOI":"10.1016\/j.micpro.2019.102938","volume":"72","author":"B Farahani","year":"2020","unstructured":"Farahani, B., Barzegari, M., Aliee, F. S., & Shaik, K. A. (2020). Towards collaborative intelligent IoT eHealth: From device to fog, and cloud. Microprocessors and Microsystems, 72, 102938.","journal-title":"Microprocessors and Microsystems"},{"issue":"11","key":"3150_CR36","doi-asserted-by":"publisher","first-page":"9059","DOI":"10.1109\/JIOT.2021.3056325","volume":"8","author":"D Ciuonzo","year":"2021","unstructured":"Ciuonzo, D., Rossi, P. S., & Varshney, P. K. (2021). Distributed detection in wireless sensor networks under multiplicative fading via generalized score tests. IEEE Internet of Things Journal, 8(11), 9059\u20139071.","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"3150_CR37","doi-asserted-by":"publisher","first-page":"4827","DOI":"10.1109\/JSEN.2020.3029459","volume":"21","author":"H Darvishi","year":"2021","unstructured":"Darvishi, H., Ciuonzo, D., Eide, E. R., & Rossi, P. S. (2021). Sensor-fault detection, isolation and accommodation for digital twins via modular data-driven architecture. IEEE Sensors Journal, 21(4), 4827\u20134838.","journal-title":"IEEE Sensors Journal"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-022-03150-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-022-03150-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-022-03150-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T14:37:43Z","timestamp":1675348663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-022-03150-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3150"],"URL":"https:\/\/doi.org\/10.1007\/s11276-022-03150-2","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,10]]},"assertion":[{"value":"29 August 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}