[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-118529-105":59,"doc-detail-118529-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","predicting-vineyard-mildew-with-uav-remote-sensing-and-machine-learning","Predicting Vineyard Mildew with UAV Remote Sensing and Machine Learning","","This study introduces a novel strategy for vineyard health and productivity management by foreseeing mildew outbreaks. Through the use of unmanned aerial vehicles (UAVs) outfitted with multispectral and hyperspectral sensors, real-time data was collected on vine health indicators essential for early mildew identification, such as leaf wetness and the normalized difference vegetation index (NDVI). This data, combined with environmental variables, was integrated into machine learning models, facilitating the prediction of mildew risk prior to the manifestation of visual symptoms. The deployment of UAV-based remote sensing technology in precision viticulture aids in optimizing fungicide application, thereby lessening environmental impact and operational expenses, while simultaneously confronting the difficulties posed by climate change and labor scarcity. Our machine learning algorithms analyzed the gathered information to guide prompt and focused interventions, substantially enhancing resource efficacy and crop resilience. This investigation highlights the feasibility of incorporating advanced sensing technologies and machine learning into agricultural practices to improve sustainability and operational effectiveness. By enabling early mildew detection and precise intervention strategies, our research advances precision agriculture methods, especially within vineyard management, offering a preemptive response to a critical challenge in viticulture.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-vineyard-mildew-with-uav-remote-sensing-and-machine-learning/118529/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-vineyard-mildew-with-uav-remote-sensing-and-machine-learning/118529.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-22","2026-08-02",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this study?","Question",{"text":112,"@type":113},"The main goal is to develop an innovative approach to manage vineyard health and productivity by predicting mildew outbreaks using UAV remote sensing and machine learning.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What types of data are collected by the UAVs?",{"text":117,"@type":113},"UAVs equipped with multispectral and hyperspectral sensors collect real-time data on vine health indicators, including leaf wetness and NDVI, along with environmental data.",{"name":119,"@type":110,"acceptedAnswer":120},"How does this technology help optimize fungicide use?",{"text":121,"@type":113},"By enabling early mildew detection and prediction, the technology allows for timely and targeted fungicide application, optimizing resource use and reducing environmental impact.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},118529,1785684005,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","March 2, 2024  \nProject Title: Predic.ng Vineyard Mildew with UAV Remote Sensing and Machine Learning  \nAuthor(s): Jonathan Pena  \nAbstract: This study presents an innovative approach to managing vineyard health and productivity by predicting mildew outbreaks. Utilizing unmanned aerial vehicles (UAVs) equipped with multispectral and hyperspectral sensors, we captured real time data on vine health indicators critical for early mildew detection, including leaf wetness and the normalized difference vegetation index (NDVI). These indicators, along with environmental data, fed into machine learning models, enabling the prediction of mildew risk before visual symptoms emerge. The application of UAV-based remote sensing technology for precision viticulture allows for the optimization of fungicide use, reducing environmental impact and costs while addressing the challenges of climate change and labor shortages. Our machine learning models processed the collected data to inform timely and targeted interventions, significantly improving resource efficiency and crop resilience. This research underscores the potential of integrating advanced sensing technologies and machine learning in agriculture to enhance sustainability and operational efficiency. By enabling early mildew detection and precision intervention, our study contributes to the advancement of precision agriculture practices, particularly in vineyard management, offering a proactive solution to one of the most pressing issues in viticulture.","cbCaipB9ErqSHcrp","https://ap.wps.com/l/cbCaipB9ErqSHcrp","pdf",73172,"English","# Project Title: Predicting Vineyard Mildew with UAV Remote Sensing and Machine Learning\n# Author(s): Jonathan Pena","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"The main goal is to develop an innovative approach to manage vineyard health and productivity by predicting mildew outbreaks using UAV remote sensing and machine learning.\"},{\"question\":\"What types of data are collected by the UAVs?\",\"answer\":\"UAVs equipped with multispectral and hyperspectral sensors collect real-time data on vine health indicators, including leaf wetness and NDVI, along with environmental data.\"},{\"question\":\"How does this technology help optimize fungicide use?\",\"answer\":\"By enabling early mildew detection and prediction, the technology allows for timely and targeted fungicide application, optimizing resource use and reducing environmental impact.\"}]","Predicting Vineyard Mildew with UAV Remote Sensing and Machine Learning | PDF"]