[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127521-en":3,"doc-seo-127521-105":30,"detail-sidebar-cat-0-en-105":91},{"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},127521,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Estimation of Tomato Water Status with Photochemical Reflectance Index and Machine Learning - Assessment from Proximal Sensors and UAV Imagery","Tracking plant water status enables adaptive precision irrigation for processing tomatoes, a key specialty crop in California. Photochemical reflectance index (PRI) from proximal sensors and high-resolution UAV imagery offer efficient monitoring pathways. Using an experimental tomato field with intensive aerial and plant measurements, the study builds random-forest regression models to estimate tomato stem water potential from PRI, multispectral UAV vegetation indices, and weather variables. Results show strong agreement with plant measurements and clear differentiation across irrigation treatments, supporting data-driven irrigation management.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nEstimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery  \nPermalink  \n[https://escholarship.org/uc/item/2z70b1t1](https://escholarship.org/uc/item/2z70b1t1)  \nAuthors  \nTang, Zhehan  \nJin, Yufang Brown, Patrick Het al.  \nPublication Date  \n2023  \nDOI  \n10.3389/fpls.2023.1057733  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nTYPE Original Research PUBLISHED 06 April 2023  \nDOI 10.3389/fpls.2023.1057733  \nOPEN ACCESS  \nEDITED BY  \nGregorio Egea, University of Seville, Spain  \nREVIEWED BY  \nJohn Arthur Gamon, University of Nebraska-Lincoln, United States  \nShangpeng Sun,  \nMcGill University, Canada  \n*CORRESPONDENCE Zhehan Tang  \n [zhhtang@ucdavis.edu](zhhtang@ucdavis.edu)  \nSPECIALTY SECTION  \nThis article was submitted to Technical Advances in Plant Science, a section of the journal  \nFrontiers in Plant Science  \nRECEIVED 30 September 2022  \nACCEPTED 27 January 2023  \nPUBLISHED 06 April 2023  \nCITATION  \nTang Z, Jin Y, Brown PH and Park M (2023) Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery.  \nFront. Plant Sci. 14:1057733 .  \ndoi: 10.3389/fpls.2023.1057733  \nCOPYRIGHT  \n© 2023 Tang, Jin, Brown and Park. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEstimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and  \nUAV imagery  \nZhehan Tang 1*, Yufang Jin 1, Patrick H. Brown 2 and Meerae Park 2  \n1 Department of Land, Air and Water Resources, University of California, Davis, Davis, CA,  \nUnited States, 2 Department of Plant Sciences, University of California, Davis, Davis, CA, United States  \nTracking plant water status is a critical step towards the adaptive precision irrigation management of processing tomatoes, one of the most important specialty crops in California. The photochemical reflectance index (PRI) from proximal sensors and the high-resolution unmanned aerial vehicle (UAV) imagery provide an opportunity to monitor the crop water status efficiently. Based on data from an experimental tomato field with intensive aerial and plant-based measurements, we developed random forest machine learning regression models to estimate tomato stem water potential (ystem), (using observations from proximal sensors and 12-band UAV imagery, respectively, along with weather data. The proximal sensor-based model estimation agreed well with the plant ystem with R2 of 0 .74 and mean absolute error (MAE) of 0 . 63 bars. The model included PRI, normalized difference vegetation index, vapor pressure deficit, and air temperature and tracked well with the seasonal dynamics of ystem across different plots. A separate model, built with multiple vegetation indices (VIs) from UAV imagery and weather variables, had an R2 of 0 . 81 and MAE of 0 . 67 bars. The plant-level ystem maps generated from UAV imagery closely represented the water status differences of plots under different irrigation treatments and also tracked well the temporal change among flights. PRI was found to be the most important VI in both the proximal sensor-and the UAVbased models, providing critical information on tomato plant water status. This study demonstrated that machine learning models can accurately estimate the water status by integrating PRI, other VIs, and weather data, and thus facilitate data-driven ","cbCaigUnGGni6gmY","https://ap.wps.com/l/cbCaigUnGGni6gmY","pdf",23860703,1,18,"English","en",105,"# Abstract\n# Introduction\n## Water scarcity and precision irrigation needs\n## Measuring plant water status and limitations\n# Methods\n## Study field and data collection\n## Proximal sensor PRI modeling\n## UAV imagery vegetation index modeling\n## Weather variables and machine learning approach\n# Results\n## Model performance for stem water potential\n## Feature importance and temporal/plot dynamics\n# Discussion\n## Interpreting PRI and VI contributions\n## Implications for data-driven irrigation management\n# Conclusion","[{\"question\":\"What do PRI and UAV imagery contribute to estimating tomato water status?\",\"answer\":\"PRI from proximal sensors and high-resolution UAV imagery provide complementary spectral information to monitor tomato water status efficiently. The study integrates these signals with weather data for estimation.\"},{\"question\":\"How are the stem water potential estimates generated in this work?\",\"answer\":\"Random forest machine learning regression models are trained to estimate tomato stem water potential using either proximal sensor observations and PRI-related variables or vegetation indices from UAV imagery combined with weather variables.\"},{\"question\":\"How did the models perform compared with plant measurements and across irrigation treatments?\",\"answer\":\"The proximal-sensor model showed good agreement with plant stem water potential measurements, and the UAV-based model also tracked temporal changes. Plant-level stem water potential maps reflected water-status differences among plots under different irrigation treatments.\"}]","Estimation of Tomato Water Status with Photochemical Reflectance Index and Machine Learning - Assessment from Proximal Sensors and UAV Imagery | PDF",1785939717,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"estimation-of-tomato-water-status-with-photochemical-reflectance-index-and-machine-learning-assessment-from-proximal-sensors-and-uav-imagery","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/estimation-of-tomato-water-status-with-photochemical-reflectance-index-and-machine-learning-assessment-from-proximal-sensors-and-uav-imagery/127521/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What do PRI and UAV imagery contribute to estimating tomato water status?","Question",{"text":75,"@type":76},"PRI from proximal sensors and high-resolution UAV imagery provide complementary spectral information to monitor tomato water status efficiently. The study integrates these signals with weather data for estimation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the stem water potential estimates generated in this work?",{"text":80,"@type":76},"Random forest machine learning regression models are trained to estimate tomato stem water potential using either proximal sensor observations and PRI-related variables or vegetation indices from UAV imagery combined with weather variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the models perform compared with plant measurements and across irrigation treatments?",{"text":84,"@type":76},"The proximal-sensor model showed good agreement with plant stem water potential measurements, and the UAV-based model also tracked temporal changes. Plant-level stem water potential maps reflected water-status differences among plots under different irrigation treatments.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]