{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:07:53Z","timestamp":1783973273408,"version":"3.55.0"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["6040-32000-012-000D"],"award-info":[{"award-number":["6040-32000-012-000D"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["7008291"],"award-info":[{"award-number":["7008291"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006033","name":"US Poultry and Egg Association","doi-asserted-by":"publisher","award":["F-114"],"award-info":[{"award-number":["F-114"]}],"id":[{"id":"10.13039\/100006033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007917","name":"Agricultural Research Service","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007917","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.compag.2026.112116","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T09:45:40Z","timestamp":1782467140000},"page":"112116","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Salmonella risk prediction in poultry farms via deep learning image classification Models, Cross-Region Validation, and edge computing"],"prefix":"10.1016","volume":"252","author":[{"given":"Venkat Umesh","family":"Chandra Bodempudi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7624-8051","authenticated-orcid":false,"given":"Guoming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adelumola","family":"Oladeinde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Rothrock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel E","family":"Aggrey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongshuai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai Akshitha","family":"Reddy Kota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sravan Sai","family":"Rahul Nalla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.112116_b0005","unstructured":"Agarap, A.F., 2018. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375."},{"key":"10.1016\/j.compag.2026.112116_b0010","doi-asserted-by":"crossref","first-page":"13396","DOI":"10.3390\/su132313396","article-title":"An approach towards IoT-based predictive service for early detection of diseases in poultry chickens","volume":"13","author":"Ahmed","year":"2021","journal-title":"Sustainability"},{"key":"10.1016\/j.compag.2026.112116_b0015","doi-asserted-by":"crossref","DOI":"10.1515\/nleng-2024-0081","article-title":"DeepFowl: Disease prediction from chicken excreta images using deep learning","volume":"14","author":"Anwarul","year":"2025","journal-title":"Nonlinear Engineering"},{"key":"10.1016\/j.compag.2026.112116_b0020","first-page":"357","article-title":"Application of the logistic function to bio-assay","volume":"39","author":"Berkson","year":"1944","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.compag.2026.112116_b0025","series-title":"Pattern recognition and machine learning","author":"Bishop","year":"2006"},{"key":"10.1016\/j.compag.2026.112116_b0030","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.watres.2016.05.014","article-title":"Characterizing relationships among fecal indicator bacteria, microbial source tracking markers, and associated waterborne pathogen occurrence in stream water and sediments in a mixed land use watershed","volume":"101","author":"Bradshaw","year":"2016","journal-title":"Water Res."},{"key":"10.1016\/j.compag.2026.112116_b0035","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"Breiman","year":"2001","journal-title":"Random Forests. Machine Learning"},{"key":"10.1016\/j.compag.2026.112116_b0040","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/vetsci5010014","article-title":"A review of eight high-priority, economically important viral pathogens of poultry within the Caribbean region","volume":"5","author":"Brown Jordan","year":"2018","journal-title":"Veterinary Sciences"},{"key":"10.1016\/j.compag.2026.112116_b0045","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.compag.2019.05.013","article-title":"Development of sound-based poultry health monitoring tool for automated sneeze detection","volume":"162","author":"Carpentier","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112116_b0050","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1017\/S0950268816002375","article-title":"Poultry: the most common food in outbreaks with known pathogens, United States, 1998\u20132012","volume":"145","author":"Chai","year":"2017","journal-title":"Epidemiology & Infection"},{"key":"10.1016\/j.compag.2026.112116_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109765","article-title":"Deep learning methods for poultry disease prediction using images","volume":"230","author":"Chidziwisano","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112116_b0060","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jviromet.2012.09.003","article-title":"Evaluation of five rapid diagnostic kits for influenza A\/B virus","volume":"187","author":"Cho","year":"2013","journal-title":"J. Virol. Methods"},{"key":"10.1016\/j.compag.2026.112116_b0065","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1251","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"10.1016\/j.compag.2026.112116_b0070","doi-asserted-by":"crossref","unstructured":"Dar, M.A., Ahmad, S.M., Bhat, B.A., Dar, T.A., ul Haq, Z., Wani, B.A., Shabir, N., Kashoo, Z.A., Shah, R.A., Ganai, N.A., 2022. Comparative RNA-Seq analysis reveals insights in Salmonella disease resistance of chicken; and database development as resource for gene expression in poultry. Genomics 114, 110475.","DOI":"10.1016\/j.ygeno.2022.110475"},{"key":"10.1016\/j.compag.2026.112116_b0075","article-title":"Smartphone based detection and classification of poultry diseases from chicken fecal images using deep learning techniques","volume":"4","author":"Degu","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.112116_b0080","series-title":"2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE","first-page":"1","article-title":"Non-intrusive detection of poultry diseases through faecal analysis using deep learning","author":"Deo","year":"2024"},{"key":"10.1016\/j.compag.2026.112116_b0085","doi-asserted-by":"crossref","unstructured":"Dinesh, K., Sankhyan, V., Thakur, D., Suman, M., Sharma, A., Bhardwaj, N., 2021. Mortality Pattern among Poultry Stock Reared under Intensive Management in Sub temperate Condition of Himachal Pradesh.","DOI":"10.5455\/ijlr.20210217103704"},{"key":"10.1016\/j.compag.2026.112116_b0090","doi-asserted-by":"crossref","DOI":"10.1111\/jfpp.13455","article-title":"In\u2010house validation of real\u2010time PCR methods for detecting the INV a and TTR genes of Salmonella spp. in food","volume":"42","author":"Dmitric","year":"2018","journal-title":"J. Food Process. Preserv."},{"key":"10.1016\/j.compag.2026.112116_b0095","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2022.06.111","article-title":"Activation functions in deep learning: a comprehensive survey and benchmark","volume":"503","author":"Dubey","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compag.2026.112116_b0100","doi-asserted-by":"crossref","DOI":"10.1002\/ece3.71291","article-title":"Exploring Bird Gut Microbiota through Opportunistic Fecal Sampling: Ecological and Evolutionary Perspectives","volume":"15","author":"Fablet","year":"2025","journal-title":"Ecol. Evol."},{"key":"10.1016\/j.compag.2026.112116_b0105","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"Journal-Japanese Society for Artificial Intelligence"},{"key":"10.1016\/j.compag.2026.112116_b0110","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"10.1016\/j.compag.2026.112116_b0115","article-title":"Impacts of tea tree or lemongrass essential oils supplementation on growth, immunity, carcass traits, and blood biochemical parameters of broilers reared under different stocking densities","volume":"100","author":"Ghanima","year":"2021","journal-title":"Poult. Sci."},{"key":"10.1016\/j.compag.2026.112116_b0120","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.compag.2026.112116_b0125","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intelligent Systems and Their Applications"},{"key":"10.1016\/j.compag.2026.112116_b0130","series-title":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE","first-page":"345","article-title":"Smartpoultry: Early detection of poultry disease from smartphone captured fecal image","author":"Hossain","year":"2023"},{"key":"10.1016\/j.compag.2026.112116_b0135","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., 2019. Searching for mobilenetv3, Proceedings of the IEEE\/CVF international conference on computer vision, pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10.1016\/j.compag.2026.112116_b0140","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.compag.2026.112116_b0145","series-title":"Proceedings of the 3rd International Conference on Computing Advancements","first-page":"830","article-title":"Enhancing Poultry Disease Classification using Fecal image: a Fusion Approach","author":"Huda","year":"2024"},{"key":"10.1016\/j.compag.2026.112116_b0150","series-title":"2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE","first-page":"1","article-title":"Respoultry: an enhanced resnet50 model for multiclass classification of poultry diseases","author":"Kaur","year":"2024"},{"key":"10.1016\/j.compag.2026.112116_b0155","series-title":"2022 International Conference on Electrical and Information Technology (IEIT). IEEE","first-page":"362","article-title":"Classification of infectious diseases in chickens based on feces images using deep learning","author":"Kholil","year":"2022"},{"key":"10.1016\/j.compag.2026.112116_b0160","unstructured":"Kingma, D.P., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"10.1016\/j.compag.2026.112116_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2021.107973","article-title":"Establishment and validation of a loop-mediated isothermal amplification (LAMP) assay targeting the ttrRSBCA locus for rapid detection of Salmonella spp. in food","volume":"126","author":"Kreitlow","year":"2021","journal-title":"Food Control"},{"key":"10.1016\/j.compag.2026.112116_b0170","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1111\/lam.13409","article-title":"Evaluation of different target genes for the detection of Salmonella sp. by loop\u2010mediated isothermal amplification","volume":"72","author":"Kreitlow","year":"2021","journal-title":"Lett. Appl. Microbiol."},{"key":"10.1016\/j.compag.2026.112116_b0175","doi-asserted-by":"crossref","first-page":"417","DOI":"10.13031\/aea.15607","article-title":"An on-site feces image classifier system for chicken health assessment: a proof of concept","volume":"39","author":"Li","year":"2023","journal-title":"Appl. Eng. Agric."},{"key":"10.1016\/j.compag.2026.112116_b0180","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1089\/fpd.2005.2.90","article-title":"Vertical and horizontal transmission of Salmonella within integrated broiler production system","volume":"2","author":"Liljebjelke","year":"2005","journal-title":"Foodbourne Pathogens & Disease"},{"key":"10.1016\/j.compag.2026.112116_b0185","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.clinmicnews.2011.02.004","volume":"33","author":"Linscott","year":"2011","journal-title":"Food-Borne Illnesses. Clinical Microbiology Newsletter"},{"key":"10.1016\/j.compag.2026.112116_b0190","article-title":"Optimizing poultry disease classification: a feature-based transfer learning approach","volume":"10","author":"Luo","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.112116_b0195","doi-asserted-by":"crossref","unstructured":"Machuve, D., Nwankwo, E., Mduma, N., Mbelwa, J., 2022a. Poultry diseases diagnostics models using deep learning. Frontiers in Artificial Intelligence, 168.","DOI":"10.3389\/frai.2022.733345"},{"key":"10.1016\/j.compag.2026.112116_b0200","doi-asserted-by":"crossref","unstructured":"Machuve, D., Nwankwo, E., Mduma, N., Mbelwa, J., 2022b. Poultry diseases diagnostics models using deep learning. Front Artif Intell 5.","DOI":"10.3389\/frai.2022.733345"},{"key":"10.1016\/j.compag.2026.112116_b0205","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1128\/AEM.70.12.7046-7052.2004","article-title":"Diagnostic real-time PCR for detection of Salmonella in food","volume":"70","author":"Malorny","year":"2004","journal-title":"Appl. Environ. Microbiol."},{"key":"10.1016\/j.compag.2026.112116_b0210","doi-asserted-by":"crossref","unstructured":"Mbelwa, H., Machuve, D., Mbelwa, J., 2021. Deep convolutional neural network for chicken diseases detection.","DOI":"10.14569\/IJACSA.2021.0120295"},{"key":"10.1016\/j.compag.2026.112116_b0215","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0220926","article-title":"Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird","volume":"15","author":"Murray","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.compag.2026.112116_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2021.108539","article-title":"Public health impact of Salmonella spp. on raw poultry: current concepts and future prospects in the United States","volume":"132","author":"O'Bryan","year":"2022","journal-title":"Food Control"},{"key":"10.1016\/j.compag.2026.112116_b0225","first-page":"1374","article-title":"Wireless sensor system for detection of avian influenza outbreak farms at an early stage, SENSORS, 2009 IEEE","author":"Okada","year":"2009","journal-title":"IEEE"},{"key":"10.1016\/j.compag.2026.112116_b0230","doi-asserted-by":"crossref","DOI":"10.1128\/aem.01388-24","article-title":"Broiler litter moisture and trace metals contribute to the persistence of Salmonella strains that harbor large plasmids carrying siderophores","volume":"91","author":"Oladeinde","year":"2025","journal-title":"Appl. Environ. Microbiol."},{"key":"10.1016\/j.compag.2026.112116_b0235","unstructured":"Paszke, A., 2019. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703."},{"key":"10.1016\/j.compag.2026.112116_b0240","first-page":"81","volume":"1","author":"Quinlan","year":"1986","journal-title":"Induction of Decision Trees. Machine Learning"},{"key":"10.1016\/j.compag.2026.112116_b0245","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"Mobilenetv2: Inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"10.1016\/j.compag.2026.112116_b0250","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"618","article-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.compag.2026.112116_b0255","unstructured":"Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"10.1016\/j.compag.2026.112116_b0260","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.5875\/ausmt.v13i1.2439","article-title":"Deep learning based classification of poultry disease","volume":"13","author":"Srivastava","year":"2023","journal-title":"International Journal of Automation and Smart Technology"},{"key":"10.1016\/j.compag.2026.112116_b0265","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"10.1016\/j.compag.2026.112116_b0270","doi-asserted-by":"crossref","unstructured":"Suthagar, S., Mageshkumar, G., Ayyadurai, M., Snegha, C., Sureka, M., Velmurugan, S., 2023. Faecal image-based chicken disease classification using deep learning techniques, Inventive Computation and Information Technologies: Proceedings of ICICIT 2022. Springer, pp. 903\u2013917.","DOI":"10.1007\/978-981-19-7402-1_64"},{"key":"10.1016\/j.compag.2026.112116_b0275","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.compag.2026.112116_b0280","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019","journal-title":"International Conference on Machine Learning. PMLR"},{"key":"10.1016\/j.compag.2026.112116_b0285","doi-asserted-by":"crossref","first-page":"691","DOI":"10.3390\/microbiolres13040050","article-title":"Salmonella spp. in Chicken: Prevalence, Antimicrobial Resistance, and Detection Methods","volume":"13","author":"Tan","year":"2022","journal-title":"Microbiol. Res."},{"key":"10.1016\/j.compag.2026.112116_b0290","doi-asserted-by":"crossref","unstructured":"Tiwari, R.G., Maheshwari, H., Agarwal, A.K., 2023. Fecal-based Health Status Prediction in Poultry Birds: A Blended Approach using Handcrafted and Deep Features, 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, pp. 939\u2013945.","DOI":"10.1109\/ICACRS58579.2023.10405067"},{"key":"10.1016\/j.compag.2026.112116_b0295","unstructured":"USDA, 2022. Salmonella By the Numbers."},{"key":"10.1016\/j.compag.2026.112116_b0300","unstructured":"USDA, 2024. USDA Proposes New Policy to Reduce Salmonella in Raw Poultry Products."},{"key":"10.1016\/j.compag.2026.112116_b0305","unstructured":"USDA, 2025. Poultry - Production and Value 2024 Summary."},{"key":"10.1016\/j.compag.2026.112116_b0310","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1017\/S0043933909000270","article-title":"Strategies to control Salmonella in the broiler production chain","volume":"65","author":"Van Immerseel","year":"2009","journal-title":"Worlds Poult. Sci. J."},{"key":"10.1016\/j.compag.2026.112116_b0315","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13567-017-0418-5","article-title":"Advanced biosensors for detection of pathogens related to livestock and poultry","volume":"48","author":"Vidic","year":"2017","journal-title":"Vet. Res."},{"key":"10.1016\/j.compag.2026.112116_b0320","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12985-017-0849-7","article-title":"Bacteriophage therapy to combat bacterial infections in poultry","volume":"14","author":"Wernicki","year":"2017","journal-title":"Virol. J."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926007118?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926007118?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:50:50Z","timestamp":1783972250000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926007118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":64,"alternative-id":["S0168169926007118"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.112116","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Salmonella risk prediction in poultry farms via deep learning image classification Models, Cross-Region Validation, and edge computing","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.112116","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112116"}}