{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:08:31Z","timestamp":1773248911182,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100011754","name":"M.P. Council of Science and Technology","doi-asserted-by":"publisher","award":["File No: A\/RD\/RP-2\/339\/31.03.2023"],"award-info":[{"award-number":["File No: A\/RD\/RP-2\/339\/31.03.2023"]}],"id":[{"id":"10.13039\/501100011754","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011754","name":"M.P. Council of Science and Technology","doi-asserted-by":"publisher","award":["File No: A\/RD\/RP-2\/339\/31.03.2023"],"award-info":[{"award-number":["File No: A\/RD\/RP-2\/339\/31.03.2023"]}],"id":[{"id":"10.13039\/501100011754","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s13198-025-02975-2","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T15:11:55Z","timestamp":1758121915000},"page":"256-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards automated crop protection: fusion of densenet121-resnet50 model for disease detection and pest recognition"],"prefix":"10.1007","volume":"17","author":[{"given":"Vedansh","family":"Sood","sequence":"first","affiliation":[]},{"given":"Shiv Shankar Prasad","family":"Shukla","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4027-8229","authenticated-orcid":false,"given":"Anil Kumar","family":"Yadav","sequence":"additional","affiliation":[]},{"given":"Sparsh","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Yashashvi","family":"Srivastava","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"2975_CR1","doi-asserted-by":"crossref","unstructured":"Anilkumar S, Kalyani G, Teja V, Sadhrusya D (2024) Insect management in crops using deep learning. In: International Conference on Communication and Computational Technologies, pp. 363\u2013375. Springer","DOI":"10.1007\/978-981-97-7423-4_28"},{"issue":"3","key":"2975_CR2","doi-asserted-by":"publisher","first-page":"478","DOI":"10.3390\/agriengineering3030032","volume":"3","author":"AA Ahmed","year":"2021","unstructured":"Ahmed AA, Reddy GH (2021) A mobile-based system for detecting plant leaf diseases using deep learning. Agri Engineering 3(3):478\u2013493","journal-title":"AgriEngineering"},{"issue":"9","key":"2975_CR3","doi-asserted-by":"publisher","first-page":"513","DOI":"10.3390\/info14090513","volume":"14","author":"A Bottrighi","year":"2023","unstructured":"Bottrighi A, Pennisi M (2023) Exploring the state of machine learning and deep learning in medicine: a survey of the italian research community. Information 14(9):513","journal-title":"Information"},{"issue":"2","key":"2975_CR4","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3390\/agronomy14020327","volume":"14","author":"U Barman","year":"2024","unstructured":"Barman U, Sarma P, Rahman M, Deka V, Lahkar S, Sharma V, Saikia MJ (2024) Vit-smartagri: vision transformer and smartphone-based plant disease detection for smart agriculture. Agronomy 14(2):327","journal-title":"Agronomy"},{"key":"2975_CR5","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"2975_CR6","unstructured":"Chen X, Fan H, Girshick R, He K (2020) Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297"},{"key":"2975_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107060","volume":"126","author":"Q Guo","year":"2023","unstructured":"Guo Q, Wang C, Xiao D, Huang Q (2023) A novel multi-label pest image classifier using the modified swin transformer and soft binary cross entropy loss. Eng Appl Artif Intell 126:107060","journal-title":"Eng Appl Artif Intell"},{"issue":"9","key":"2975_CR8","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales T, Antoniou A, Micaelli P, Storkey A (2021) Meta-learning in neural networks: a survey. IEEE Trans Pattern Anal Mach Intell 44(9):5149\u20135169","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2975_CR9","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s42853-020-00043-0","volume":"45","author":"JY Kim","year":"2020","unstructured":"Kim JY (2020) Roadmap to high throughput phenotyping for plant breeding. J Biosyst Eng 45:43\u201355","journal-title":"Journal of Biosystems Engineering"},{"key":"2975_CR10","unstructured":"Koner R, Shit S, Tresp V (2020) Relation transformer network. arXiv preprint arXiv:2004.06193"},{"issue":"7553","key":"2975_CR11","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"4","key":"2975_CR12","doi-asserted-by":"publisher","first-page":"864","DOI":"10.3390\/agronomy14040864","volume":"14","author":"C Li","year":"2024","unstructured":"Li C, Tian Y, Tian X, Zhai Y, Cui H, Song M (2024) An advancing gct-inception-resnet-v3 model for arboreal pest identification. Agronomy 14(4):864","journal-title":"Agronomy"},{"issue":"16","key":"2975_CR13","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.3390\/electronics10161979","volume":"10","author":"W Muhammad","year":"2021","unstructured":"Muhammad W, Bhutto Z, Ansari A, Memon ML, Kumar R, Hussain A, Shah SAR, Thaheem I, Ali S (2021) Multi-path deep cnn with residual inception network for single image super-resolution. Electronics 10(16):1979","journal-title":"Electronics"},{"issue":"7540","key":"2975_CR14","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"2975_CR15","doi-asserted-by":"crossref","unstructured":"Mitra A, Mohanty SP, Kougianos E (2022) agrodet: a novel framework for plant disease detection and leaf damage estimation. In: IFIP International Internet of Things Conference, pp. 3\u201322. Springer","DOI":"10.1007\/978-3-031-18872-5_1"},{"key":"2975_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105985","volume":"121","author":"B Prasath","year":"2023","unstructured":"Prasath B, Akila M (2023) Iot-based pest detection and classification using deep features with enhanced deep learning strategies. Eng Appl Artif Intell 121:105985","journal-title":"Eng Appl Artif Intell"},{"key":"2975_CR17","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"2975_CR18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"issue":"2","key":"2975_CR19","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.tplants.2015.10.015","volume":"21","author":"A Singh","year":"2016","unstructured":"Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21(2):110\u2013124","journal-title":"Trends Plant Sci"},{"key":"2975_CR20","doi-asserted-by":"crossref","unstructured":"Thakur PS, Khanna P, Sheorey T, Ojha A (2021) Vision transformer for plant disease detection: Plantvit. In: International Conference on Computer Vision and Image Processing, pp. 501\u2013511. Springer","DOI":"10.1007\/978-3-031-11346-8_43"},{"key":"2975_CR21","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR"},{"key":"2975_CR22","doi-asserted-by":"publisher","first-page":"73019","DOI":"10.1109\/ACCESS.2022.3189676","volume":"10","author":"N Ullah","year":"2022","unstructured":"Ullah N, Khan JA, Alharbi LA, Raza A, Khan W, Ahmad I (2022) An efficient approach for crops pests recognition and classification based on novel deeppestnet deep learning model. IEEE Access 10:73019\u201373032","journal-title":"IEEE Access"},{"key":"2975_CR23","unstructured":"Vinyals O, Ewalds T, Bartunov S, Georgiev P, Vezhnevets AS, Yeo M, Makhzani A, K\u00fcttler H, Agapiou J, Schrittwieser J, et al (2017) Starcraft ii: a new challenge for reinforcement learning. arXiv preprint arXiv:1708.04782"},{"issue":"9","key":"2975_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29912","volume":"10","author":"S Vallabhajosyula","year":"2024","unstructured":"Vallabhajosyula S, Sistla V, Kolli VKK (2024) A novel hierarchical framework for plant leaf disease detection using residual vision transformer. Heliyon 10(9):e29912","journal-title":"Heliyon"},{"issue":"1","key":"2975_CR25","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s43621-024-00285-4","volume":"5","author":"Y Wang","year":"2024","unstructured":"Wang Y, Rajkumar Dhamodharan US, Sarwar N, Almalki FA, Naith QH (2024) A hybrid approach for rice crop disease detection in agricultural iot system. Discover Sustainability 5(1):99","journal-title":"Discover Sustainability"},{"issue":"3","key":"2975_CR26","doi-asserted-by":"publisher","first-page":"500","DOI":"10.3390\/agronomy14030500","volume":"14","author":"Y Wang","year":"2024","unstructured":"Wang Y, Yin Y, Li Y, Qu T, Guo Z, Peng M, Jia S, Wang Q, Zhang W, Li F (2024) Classification of plant leaf disease recognition based on self-supervised learning. Agronomy 14(3):500","journal-title":"Agronomy"},{"key":"2975_CR27","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"2975_CR28","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"key":"2975_CR29","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-02975-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-025-02975-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-02975-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T14:45:14Z","timestamp":1769957114000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-025-02975-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2975"],"URL":"https:\/\/doi.org\/10.1007\/s13198-025-02975-2","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,17]]},"assertion":[{"value":"15 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2025","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}