[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127022-en":3,"doc-seo-127022-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127022,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness","THz time-domain spectroscopy can mature into practical technologies only when signal processing advances to enable robust, generalizable performance. This work demonstrates physics-assisted machine learning for THz measurements grounded in light–matter interactions to quantify leaf wetness, i.e., free water on plant leaves. Using THz time-domain spectroscopy, transmission responses from 12,000 distinct water-droplet patterns on a plastized leaf are acquired, and decision trees and convolutional neural networks are evaluated. Model testing under increasing deviations from the training set assesses generalizability for agriculture-focused disease-related monitoring.","Physics-assisted machine learning for THz time-domain spectroscopy  \nCitation for published version (APA):  \nKoumans, M. , Meulendijks, D. , Middeljans, H. , Peeters, D. , Douma, J. C. , & van Mechelen, D. (2024) . Physicsassisted machine learning for THz time-domain spectroscopy: sensing leaf wetness. Scientific Reports, 14(1), Article 7034. [https://doi.org/10.1038/s41598-024-57161-4](https://doi.org/10.1038/s41598-024-57161-4)  \nDocument license:  \nCC BY  \nDOI:  \n10.1038/s41598-024-57161-4  \nDocument status and date:  \nPublished: 25/03/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 23. Oct. 2024  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPhysics‑assisted machine learning forTHz time‑domain spectroscopy: sensing leaf wetness  \nMilan Koumans1, Daan Meulendijks1, Haiko Middeljans1, Djero Peeters1, Jacob C. Douma2 & Dook van Mechelen1*  \nSignal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques forTHz time‑domain spectroscopy assisted by domain knowledge based on light–matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so‑called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of 12,000 distinct water droplet patterns on a plastized leaf was experimentally acquired using THz time‑domain spectroscopy. Wereport on key insights of applying decision trees and convolutional neural networks to the data using physics‑motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set.  \nThe quest to implement societally and industrially relevant applications ofTHz technology is impeded by aspects such as costs and performance as compared to alternatives. Despite the wide range of exploratory studies of THz technology since the late 1980s, and the numerous suggested applications1, very few produc","cbCaitTDIXspXBOY","https://ap.wps.com/l/cbCaitTDIXspXBOY","pdf",5519217,1,12,"English","en",105,"# Overview and motivation\n# Physics-assisted machine learning approach\n# Data acquisition and experimental setup\n# Model comparison and physics-motivated decisions\n# Generalizability assessment for out-of-distribution cases","[{\"question\":\"What problem does the work address for THz time-domain spectroscopy?\",\"answer\":\"It addresses the need for advanced signal processing so THz spectroscopy can reach maturity for practical applications, overcoming limitations in generality, robustness, and speed.\"},{\"question\":\"What agricultural target quantity is predicted in this study?\",\"answer\":\"The study targets leaf wetness, defined as the amount of free water on plant leaves, which is important for disease development.\"},{\"question\":\"How is the learning data generated for the models?\",\"answer\":\"Transmission data are experimentally acquired using THz time-domain spectroscopy for 12,000 distinct water droplet patterns on a plastized leaf.\"},{\"question\":\"How is the generalizability of the models evaluated?\",\"answer\":\"Models are tested on cases with increasing deviations from the training set to check whether they can determine leaf wetness beyond the learned conditions.\"}]","Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness | 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problem does the work address for THz time-domain spectroscopy?","Question",{"text":75,"@type":76},"It addresses the need for advanced signal processing so THz spectroscopy can reach maturity for practical applications, overcoming limitations in generality, robustness, and speed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What agricultural target quantity is predicted in this study?",{"text":80,"@type":76},"The study targets leaf wetness, defined as the amount of free water on plant leaves, which is important for disease development.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the learning data generated for the models?",{"text":84,"@type":76},"Transmission data are experimentally acquired using THz time-domain spectroscopy for 12,000 distinct water droplet patterns on a plastized leaf.",{"name":86,"@type":73,"acceptedAnswer":87},"How is the generalizability of the models evaluated?",{"text":88,"@type":76},"Models are tested on cases with increasing deviations from 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