{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:55:33Z","timestamp":1784044533315,"version":"3.55.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-024-09481-2","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T11:00:54Z","timestamp":1731322854000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Optimized recurrent neural network-based early diagnosis of crop pest and diseases in agriculture"],"prefix":"10.1007","volume":"27","author":[{"given":"Vijesh Kumar","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumar","family":"Abhishek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shitharth","family":"Selvarajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"9481_CR1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-981-15-2021-1_8","volume":"2019","author":"S Bhattacharya","year":"2020","unstructured":"Bhattacharya S, Mukherjee A, Phadikar S. A deep learning approach for the classification of rice leaf diseases. Intell Enabled Res DoSIER. 2020;2019:61\u20139.","journal-title":"Intell Enabled Res DoSIER"},{"key":"9481_CR2","volume-title":"Deep learning-based automated feature engineering for rice leaf disease prediction Computational Intelligence in Pattern Recognition proceedings: of CIPR 2020","author":"A Das","year":"2020","unstructured":"Das A, Mallick C, Dutta S. Deep learning-based automated feature engineering for rice leaf disease prediction Computational Intelligence in Pattern Recognition proceedings: of CIPR 2020. Singapore: Springer; 2020."},{"key":"9481_CR3","volume-title":"MCNN: an approach for plant disease detection using modified convolutional neural network intelligent systems and human machine collaboration: select of ICISHMC 2022","author":"S Brinthakumari","year":"2023","unstructured":"Brinthakumari S, Sivaraja PM. MCNN: an approach for plant disease detection using modified convolutional neural network intelligent systems and human machine collaboration: select of ICISHMC 2022. Singapore: Springer Nature Singapore; 2023."},{"key":"9481_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2021.104321","author":"H Jiang","year":"2021","unstructured":"Jiang H, Li X, Safara F. IoT-based agriculture: deep learning in detecting apple fruit diseases. Microprocess Microsyst. 2021. https:\/\/doi.org\/10.1016\/j.micpro.2021.104321.","journal-title":"Microprocess Microsyst"},{"key":"9481_CR5","first-page":"12","volume":"4","author":"BS Anami","year":"2020","unstructured":"Anami BS, Malvade NN, Palaiah S. Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. Artif intell Agric. 2020;4:12\u201320.","journal-title":"Artif intell Agric"},{"key":"9481_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105985","volume":"121","author":"B Prasath","year":"2023","unstructured":"Prasath B, Akila M. IoT-based pest detection and classification using deep features with enhanced deep learning strategies. Eng Appl Artif Intell. 2023;121: 105985.","journal-title":"Eng Appl Artif Intell"},{"key":"9481_CR7","volume":"3","author":"APAS Rani","year":"2022","unstructured":"Rani APAS, Singh NS. Protecting the environment from pollution through early detection of infections on crops using the deep belief network in paddy. Total Environ Res Themes. 2022;3: 100020.","journal-title":"Total Environ Res Themes"},{"key":"9481_CR8","volume-title":"Pest detection using improvised YOLO architecture. In computer vision and machine intelligence paradigms for SDGs: select proceedings of ICRTAC-CVMIP","author":"M Sujaritha","year":"2023","unstructured":"Sujaritha M, Kavitha M, Roobini S. Pest detection using improvised YOLO architecture. In computer vision and machine intelligence paradigms for SDGs: select proceedings of ICRTAC-CVMIP. Singapore: Springer Nature; 2023."},{"key":"9481_CR9","doi-asserted-by":"publisher","first-page":"100024","DOI":"10.1016\/j.fraope.2023.100024","volume":"3","author":"AB Kathole","year":"2023","unstructured":"Kathole AB, Katti J, Lonare S, Dharmale G. Identify and classify pests in the agricultural sector using metaheuristics deep learning approach. Franklin Open. 2023;3:100024.","journal-title":"Franklin Open"},{"key":"9481_CR10","first-page":"22","volume":"9","author":"MT Ahad","year":"2023","unstructured":"Ahad MT, Li Y, Song B, Bhuiyan T. Comparison of CNN-based deep learning architectures for rice diseases classification. Artif Intell Agric. 2023;9:22\u201335.","journal-title":"Artif Intell Agric"},{"issue":"1","key":"9481_CR11","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1007\/s11831-021-09588-5","volume":"29","author":"JA Wani","year":"2022","unstructured":"Wani JA, Sharma S, Muzamil M, Ahmed S, Sharma S, Singh S. Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: methodologies, applications, and challenges. Archiv Comput Methods Eng. 2022;29(1):641\u201377.","journal-title":"Archiv Comput Methods Eng"},{"key":"9481_CR12","doi-asserted-by":"publisher","first-page":"180750","DOI":"10.1109\/ACCESS.2020.3024891","volume":"8","author":"CJ Chen","year":"2020","unstructured":"Chen CJ, Huang YY, Li YS, Chang CY, Huang YM. An AIoT based smart agricultural system for pests\u2019 detection. IEEE Access. 2020;8:180750\u201361.","journal-title":"IEEE Access"},{"key":"9481_CR13","doi-asserted-by":"publisher","first-page":"100643","DOI":"10.1016\/j.measen.2022.100643","volume":"25","author":"YM Abd Algani","year":"2023","unstructured":"Abd Algani YM, Caro OJM, Bravo LMR, Kaur C, Al Ansari MS, Bala BK. Leaf disease identification and classification using optimized deep learning. Measure Sens. 2023;25:100643.","journal-title":"Measure Sens"},{"issue":"9","key":"9481_CR14","doi-asserted-by":"publisher","first-page":"12407","DOI":"10.1007\/s12652-022-04331-9","volume":"14","author":"P Kaur","year":"2023","unstructured":"Kaur P, Harnal S, Gautam V, Singh MP, Singh SP. A novel transfer deep learning method for detection and classification of plant leaf disease. J Ambient Intell Humaniz Comput. 2023;14(9):12407\u201324.","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"9481_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2023.100713","volume":"26","author":"MG Nayagam","year":"2023","unstructured":"Nayagam MG, Vijayalakshmi B, Somasundaram K, Mukunthan MA, Yogaraja CA, Partheeban P. Control of pests and diseases in plants using IOT Technology. Measurement Sensors. 2023;26: 100713.","journal-title":"Measurement Sensors"},{"key":"9481_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114770","volume":"178","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang H, Peng Z. Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Syst Appl. 2021;178: 114770.","journal-title":"Expert Syst Appl"},{"key":"9481_CR17","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.biosystemseng.2020.03.020","volume":"194","author":"CR Rahman","year":"2020","unstructured":"Rahman CR, Arko PS, Ali ME, Khan MAI, Apon SH, Nowrin F, Wasif A. Identification and recognition of rice diseases and pests using convolutional neural networks. Biosys Eng. 2020;194:112\u201320.","journal-title":"Biosys Eng"},{"key":"9481_CR18","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.susoc.2023.03.001","volume":"4","author":"I Ahmed","year":"2023","unstructured":"Ahmed I, Yadav PK. A systematic analysis of machine learning and deep learning-based approaches for identifying and diagnosing plant diseases. Sustain Operat Comput. 2023;4:96\u2013104.","journal-title":"Sustain Operat Comput"},{"key":"9481_CR19","first-page":"200278","volume":"20","author":"MM Islam","year":"2023","unstructured":"Islam MM, Talukder MA, Sarker MRA, Uddin MA, Akhter A, Sharmin S, Debnath SK. A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intell Syst Appl. 2023;20:200278.","journal-title":"Intell Syst Appl"},{"key":"9481_CR20","doi-asserted-by":"publisher","first-page":"100966","DOI":"10.1016\/j.measen.2023.100966","volume":"31","author":"RS Jesie","year":"2024","unstructured":"Jesie RS, Premi MG, Jarin T. Comparative analysis of paddy leaf diseases sensing with a hybrid convolutional neural network model. Measurement Sens. 2024;31:100966.","journal-title":"Measurement Sens"},{"issue":"3","key":"9481_CR21","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1007\/s11063-022-10978-4","volume":"55","author":"MSU Sourav","year":"2023","unstructured":"Sourav MSU, Wang H. Intelligent identification of jute pests based on transfer learning and deep convolutional neural networks. Neural Process Lett. 2023;55(3):2193\u2013210.","journal-title":"Neural Process Lett"},{"key":"9481_CR22","first-page":"100764","volume":"14","author":"MM Islam","year":"2023","unstructured":"Islam MM, Adil MAA, Talukder MA, Ahamed MKU, Uddin MA, Hasan MK, Debnath SK. DeepCrop: deep learning-based crop disease prediction with web application. J Agric Food Res. 2023;14:100764.","journal-title":"J Agric Food Res"},{"key":"9481_CR23","doi-asserted-by":"publisher","first-page":"44934","DOI":"10.1109\/ACCESS.2022.3169147","volume":"10","author":"Z Liu","year":"2022","unstructured":"Liu Z, Bashir RN, Iqbal S, Shahid MMA, Tausif M, Umer Q. Internet of Things (IoT) and machine learning model of plant disease prediction\u2013blister blight for tea plant. IEEE Access. 2022;10:44934\u201344.","journal-title":"IEEE Access"},{"key":"9481_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2023.109306","author":"PK Mensah","year":"2023","unstructured":"Mensah PK, Akoto-Adjepong V, Adu K, Ayidzoe MA, Bediako EA, Nyarko-Boateng O, Boateng S, Donkor EF, Bawah FU, Awarayi NS, Nimbe P. CCMT: dataset for crop pest and disease detection. Data Brief. 2023. https:\/\/doi.org\/10.1016\/j.dib.2023.109306.","journal-title":"Data Brief"},{"issue":"1","key":"9481_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. Journal of big data. 2019;6(1):1\u201348.","journal-title":"Journal of big data"},{"issue":"12","key":"9481_CR26","doi-asserted-by":"publisher","first-page":"7409","DOI":"10.15680\/IJIRCCE.2014.0212024","volume":"2","author":"P Parsania","year":"2014","unstructured":"Parsania P, Virparia PV. A review: Image interpolation techniques for image scaling. Int J Innov Res Computer Commun Eng. 2014;2(12):7409\u201314.","journal-title":"Int J Innov Res Computer Commun Eng"},{"key":"9481_CR27","doi-asserted-by":"crossref","unstructured":"Jayakumar, D., Elakkiya, A., Rajmohan, R., & Ramkumar, M. O. (2020, July). Automatic prediction and classification of diseases in melons using stacked RNN based deep learning model. In\u00a02020 international conference on system, computation, automation and networking (ICSCAN)\u00a0(pp. 1\u20135). IEEE.","DOI":"10.1109\/ICSCAN49426.2020.9262414"},{"issue":"3","key":"9481_CR28","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/JETCAS.2021.3101740","volume":"11","author":"A Albanese","year":"2021","unstructured":"Albanese A, Nardello M, Brunelli D. Automated pest detection with DNN on the edge for precision agriculture. IEEE J Select Topics Circuits Syst. 2021;11(3):458\u201367.","journal-title":"IEEE J Select Topics Circuits Syst"},{"key":"9481_CR29","doi-asserted-by":"crossref","unstructured":"Waleed, M., Abdullah, A. S., & Ahmed, S. R. (2020, September). Classification of Vegetative pests for cucumber plants using artificial neural networks. In\u00a02020 3rd International Conference on Engineering Technology and its Applications (IICETA) (pp. 47\u201351). IEEE.","DOI":"10.1109\/IICETA50496.2020.9318890"},{"issue":"1","key":"9481_CR30","doi-asserted-by":"publisher","first-page":"29","DOI":"10.26554\/sti.2022.7.1.29-35","volume":"7","author":"Y Resti","year":"2022","unstructured":"Resti Y, Irsan C, Putri MT, Yani I, Ansyori A, Suprihatin B. Identification of corn plant diseases and pests based on digital images using multinomial na\u00efve bayes and k-nearest neighbor. Sci Technol Indonesia. 2022;7(1):29\u201335.","journal-title":"Sci Technol Indonesia"},{"issue":"3","key":"9481_CR31","doi-asserted-by":"publisher","first-page":"210","DOI":"10.3844\/jcssp.2020.280.294","volume":"16","author":"T Haryanto","year":"2020","unstructured":"Haryanto T, Pratama A, Suhartanto H, Murni A, Kusmardi K, Pidani\u010d J. Multipatch-GLCM for texture feature extraction on classification of the colon histopathology images using deep neural network with GPU acceleration. J Computer Sci. 2020;16(3):210.","journal-title":"J Computer Sci"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-024-09481-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-024-09481-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-024-09481-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T11:03:42Z","timestamp":1731323022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-024-09481-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,11]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["9481"],"URL":"https:\/\/doi.org\/10.1007\/s10791-024-09481-2","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,11]]},"assertion":[{"value":"26 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"43"}}