{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T01:47:58Z","timestamp":1777859278762,"version":"3.51.4"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009880","name":"Lazio Region","doi-asserted-by":"publisher","award":["A0613-2023-078152"],"award-info":[{"award-number":["A0613-2023-078152"]}],"id":[{"id":"10.13039\/501100009880","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":[[2026,5]]},"DOI":"10.1016\/j.compag.2026.111560","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T11:20:44Z","timestamp":1772277644000},"page":"111560","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Enabling early identification of nutritional deficiencies in hazelnut orchards through a data-driven robotic framework"],"prefix":"10.1016","volume":"246","author":[{"given":"Fabio","family":"Fuoti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martina","family":"Lippi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Rabbai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Miele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niccol\u00f2","family":"Bonucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerio","family":"Cristofori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Gasparri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.compag.2026.111560_b1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3390\/plants10010028","article-title":"Convolutional neural network for automatic identification of plant diseases with limited data","volume":"10","author":"Afifi","year":"2020","journal-title":"Plants"},{"key":"10.1016\/j.compag.2026.111560_b2","doi-asserted-by":"crossref","first-page":"31103","DOI":"10.1109\/ACCESS.2022.3159678","article-title":"End-to-end deep learning model for corn leaf disease classification","volume":"10","author":"Amin","year":"2022","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.compag.2026.111560_b3","doi-asserted-by":"crossref","first-page":"15537","DOI":"10.1038\/s41598-024-66543-7","article-title":"PND-Net: plant nutrition deficiency and disease classification using graph convolutional network","volume":"14","author":"Bera","year":"2024","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.compag.2026.111560_b4","doi-asserted-by":"crossref","DOI":"10.3390\/info11020125","article-title":"Albumentations: Fast and flexible image augmentations","volume":"11","author":"Buslaev","year":"2020","journal-title":"Information"},{"key":"10.1016\/j.compag.2026.111560_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.111026","article-title":"A lightweight rotating target detection method for rice leaf blast based on improved YOLOv8n","volume":"239","author":"Cao","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.111114","article-title":"ABC+CNN-SH: Detection of peruvian coffea leaf diseases with a new hybrid classification algorithm based on ABC optimization and CNN","volume":"239","author":"\u00c7etiner","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b7","series-title":"IX International Congress on Hazelnut 1226","first-page":"273","article-title":"Total foliar nutrition applied on European hazelnut","author":"Cristofori","year":"2017"},{"key":"10.1016\/j.compag.2026.111560_b8","series-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.compag.2026.111560_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107340","article-title":"Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa)","volume":"202","author":"Dey","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105162","article-title":"Deep learning for classification and severity estimation of coffee leaf biotic stress","volume":"169","author":"Esgario","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b11","series-title":"IEEE Int. Conf. Autom.\/Congress Chilean Association Autom. Control","first-page":"1","article-title":"Detection of nutrient deficiencies in banana plants using deep learning","author":"Guerrero","year":"2021"},{"key":"10.1016\/j.compag.2026.111560_b12","series-title":"Int. Joint Conf. Comput. Scie. Software Eng.","first-page":"277","article-title":"Classification of nutrient deficiency in black gram using deep convolutional neural networks","author":"Han","year":"2019"},{"key":"10.1016\/j.compag.2026.111560_b13","series-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.compag.2026.111560_b14","series-title":"Int. Conf. Comput. Vis.","first-page":"1314","article-title":"Searching for Mobilenetv3","author":"Howard","year":"2019"},{"key":"10.1016\/j.compag.2026.111560_b15","series-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"issue":"1","key":"10.1016\/j.compag.2026.111560_b16","doi-asserted-by":"crossref","first-page":"7331","DOI":"10.1038\/s41598-023-34549-2","article-title":"Construction of deep learning-based disease detection model in plants","volume":"13","author":"Jung","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111560_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105342","article-title":"SoyNet: Soybean leaf diseases classification","volume":"172","author":"Karlekar","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.compag.2026.111560_b18","doi-asserted-by":"crossref","first-page":"510","DOI":"10.3390\/agriculture13020510","article-title":"Real-time plant health detection using deep convolutional neural networks","volume":"13","author":"Khalid","year":"2023","journal-title":"Agriculture"},{"issue":"12","key":"10.1016\/j.compag.2026.111560_b19","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.compag.2026.111560_b20","series-title":"Int. Conf. Comput. Vis.","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.compag.2026.111560_b21","series-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"11976","article-title":"A convnet for the 2020s","author":"Liu","year":"2022"},{"key":"10.1016\/j.compag.2026.111560_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110775","article-title":"Solutions and challenges in AI-based pest and disease recognition","volume":"238","author":"Liu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b23","series-title":"Intell. Syst. Conf.","article-title":"Smooth Grad-CAM++: An enhanced inference level visualization technique for deep convolutional neural network models","author":"Omeiza","year":"2019"},{"key":"10.1016\/j.compag.2026.111560_b24","series-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","first-page":"8026","article-title":"Pytorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"issue":"9","key":"10.1016\/j.compag.2026.111560_b25","doi-asserted-by":"crossref","first-page":"822","DOI":"10.3390\/horticulturae8090822","article-title":"Cultivar-specific assessments of almond nutritional status through foliar analysis","volume":"8","author":"Pica","year":"2022","journal-title":"Horticulturae"},{"key":"10.1016\/j.compag.2026.111560_b26","series-title":"Machine Learning","first-page":"101","article-title":"Chapter 6 - support vector machine","author":"Pisner","year":"2020"},{"key":"10.1016\/j.compag.2026.111560_b27","series-title":"Robotics: Modelling, Planning and Control","author":"Siciliano","year":"2010"},{"issue":"1","key":"10.1016\/j.compag.2026.111560_b28","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1002\/jsfa.10557","article-title":"Advances in cultivar choice, hazelnut orchard management, and nut storage to enhance product quality and safety: An overview","volume":"101","author":"Silvestri","year":"2021","journal-title":"J. Sci. Food Agric."},{"key":"10.1016\/j.compag.2026.111560_b29","series-title":"ACM IKDD CoDS and COMAD","first-page":"249","article-title":"PlantDoc: A dataset for visual plant disease detection","author":"Singh","year":"2020"},{"key":"10.1016\/j.compag.2026.111560_b30","series-title":"Int. Conf. Mach. Learn.","first-page":"10096","article-title":"EfficientNetV2: Smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.compag.2026.111560_b31","series-title":"International Conference on Advances in Data Engineering and Intelligent Computing Systems","first-page":"1","article-title":"YOLOv8: A novel object detection algorithm with enhanced performance and robustness","author":"Varghese","year":"2024"},{"issue":"2","key":"10.1016\/j.compag.2026.111560_b32","first-page":"34","article-title":"Applications of image processing in agriculture: A survey","volume":"52","author":"Vibhute","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"10.1016\/j.compag.2026.111560_b33","series-title":"X International Congress on Hazelnut 1379","first-page":"229","article-title":"Nutrient deficiency symptoms and uptake relations in juvenile hazelnut (Corylus avellana) in response to macronutrient supply","author":"Voogt","year":"2022"},{"issue":"1\u20132","key":"10.1016\/j.compag.2026.111560_b34","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/01904168009362767","article-title":"Some problems in the study of simultaneous multiple nutrient deficiencies in plants","volume":"2","author":"Wallace","year":"1980","journal-title":"J. Plant Nutr."},{"key":"10.1016\/j.compag.2026.111560_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106373","article-title":"A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net","volume":"189","author":"Wang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110823","article-title":"CSPNet: A feature interaction network for tomato leaf disease detection in complex scenarios","volume":"238","author":"Yan","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111560_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110801","article-title":"ESM-YOLOv11: A lightweight deep learning framework for real-time peanut leaf spot disease detection and precision severity quantification in field conditions","volume":"238","author":"Zhang","year":"2025","journal-title":"Comput. Electron. Agric."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926001559?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926001559?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:28:22Z","timestamp":1777508902000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926001559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":37,"alternative-id":["S0168169926001559"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111560","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enabling early identification of nutritional deficiencies in hazelnut orchards through a data-driven robotic framework","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111560","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":"111560"}}