[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-442246-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-442246-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","optimizing-chlorophyll-content-prediction-in-tea-leaves-via-spectral-transformations-and-deep-learning","Optimizing chlorophyll content prediction in tea leaves via spectral transformations and deep learning","","Accurate estimation of chlorophyll content from spectral reflectance supports monitoring plant physiological status and precision agriculture. The study evaluates four machine-learning models—1D-CNN, SSL, ViT, and Conformer—and quantifies how four preprocessing techniques (Original Reflectance, Continuum Removal, De-trending, SNV) affect prediction quality. Ten-fold cross validation is performed on tea-leaf spectra. SNV and De-trending improve sensitivity near key absorption regions, while Continuum Removal highlights visible-spectrum negative correlations. The SSL model with SNV achieves the highest accuracy (R²=0.82, RPD=2.37).",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/optimizing-chlorophyll-content-prediction-in-tea-leaves-via-spectral-transformations-and-deep-learning/442246/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/optimizing-chlorophyll-content-prediction-in-tea-leaves-via-spectral-transformations-and-deep-learning/442246.png","ImageObject",300,407,{"name":42,"@type":43},"Elsa","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-30","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":22},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Why is estimating chlorophyll content from spectral reflectance important for tea cultivation?","Question",{"text":62,"@type":63},"Chlorophyll content helps reflect photosynthetic capacity and metabolic and quality traits of tea leaves, supporting cultivar management and precision agriculture decisions.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which preprocessing methods most improved chlorophyll prediction sensitivity and where?",{"text":67,"@type":63},"SNV and De-trending enhanced spectral sensitivity around chlorophyll absorption regions (450–500 nm and 650–700 nm), while Continuum Removal emphasized negative correlation in the visible range.",{"name":69,"@type":60,"acceptedAnswer":70},"What model-preprocessing pairing produced the best prediction results?",{"text":71,"@type":63},"The SSL model combined with SNV achieved the highest accuracy, outperforming other model-preprocessing combinations (R²=0.82, RPD=2.37).","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},442246,1790811683,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":22,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":112},137455077381,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Tsuchiya et al. BMC Plant Biology (2026) 26:26 [https://doi.org/10.1186/s12870-025-07863-2](https://doi.org/10.1186/s12870-025-07863-2)  \nBMC Plant Biology  \nRESEARCH Open Access  \nOptimizing chlorophyll content prediction in tea leaves via spectral transformationsand deep learning  \nYutaTsuchiya 1, Keita Yoshida2, Yoshiki Ishiguro3, Jumpei Kawaki4, Hiroto Yamashita2, Takashi Ikka2 and Rei Sonobe 1,2*  \nAbstract  \nAccurate estimation of chlorophyll contents from spectral reflectance is necessary for monitoring plant physiological status and for supporting precision agriculture. This study, which uses four machine learning models (1D Convolutional Neural Network (1D-CNN), Self-Supervised Learning (SSL), Vision Transformer (ViT), and Conformer), elucidates the effects of four preprocessing techniques on the performance of chlorophyll content prediction: Original Reflectance (OR), Continuum Removal (CR), De-trending (DT), and Standard Normal Variate (SNV) . Reflectance data were collected from tea leaves (Camellia sinensis) and were analysed using ten-fold crossvalidation. Correlation analysis revealed that SNV and DT enhanced the spectral sensitivity to chlorophyll content, particularly around the chlorophyll absorption regions (450–500 nm and 650–700 nm), whereas CR emphasized negative correlation in the visible spectrum. Prediction results demonstrated that the SSL model combined with SNV preprocessing achieved the highest accuracy (R² = 0. 82, RPD = 2 . 37), outperforming other model-preprocessing combinations. The 1D-CNN model performed best with DT, leveraging local spectral features, whereas ViT and Conformer models benefited most from CR, which emphasizes absorption depth and spectral shape. These results highlight that the optimal preprocessing method depends on the model architecture, and that proper pairing between preprocessing and modelling approaches is crucially important for maximizing prediction performance. The study results underscore the importance of customized preprocessing strategies for hyperspectral analysis and provide practical insights for improving biochemical trait estimation in plant phenotyping.  \nKeywords Chlorophyll estimation, Hyperspectral reflectance, Machine learning models, Spectral preprocessing, Tea leaves (Camellia sinensis)  \n*Correspondence:  \nRei Sonobe  \n[sonobe. rei@shizuoka.ac.jp](sonobe. rei@shizuoka.ac.jp)  \n1Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan  \n2Faculty of Agriculture, Shizuoka University, Shizuoka, Japan  \n3United Graduate School of Agricultural Science, Gifu University, Yanagido, Gifu, Japan  \n4Cha Open Innovation Practical and Applied Research Center, Kikugawa, Japan  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.  \nTsuchiya et al. BMC Plant Biology (2026) 26:26  \nIntroduction  \nUnderstanding the chlorophyll contents of tea leaves (Camellia sinensis) is a valuable indicator for managing different cultivars effectively","cbCaitJ7WKqMbm9m","https://ap.wps.com/l/cbCaitJ7WKqMbm9m","pdf",2880716,12,"English","# Abstract\n# Introduction","[{\"question\":\"Why is estimating chlorophyll content from spectral reflectance important for tea cultivation?\",\"answer\":\"Chlorophyll content helps reflect photosynthetic capacity and metabolic and quality traits of tea leaves, supporting cultivar management and precision agriculture decisions.\"},{\"question\":\"Which preprocessing methods most improved chlorophyll prediction sensitivity and where?\",\"answer\":\"SNV and De-trending enhanced spectral sensitivity around chlorophyll absorption regions (450–500 nm and 650–700 nm), while Continuum Removal emphasized negative correlation in the visible range.\"},{\"question\":\"What model-preprocessing pairing produced the best prediction results?\",\"answer\":\"The SSL model combined with SNV achieved the highest accuracy, outperforming other model-preprocessing combinations (R²=0.82, RPD=2.37).\"}]","Optimizing chlorophyll content prediction in tea leaves via spectral transformations and deep learning | PDF",1790699572]