[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120866-en":3,"doc-seo-120866-105":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},120866,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Yield estimation using machine learning from satellite imagery","Accurate and early grape yield estimation from pea-size stage supports field-level decisions such as green harvesting and irrigation management, enables advance purchasing of grapes from suppliers, and improves forecasting of campaign wine volume before production begins. It also strengthens vintage quality assessment through monitoring of leaf-surface heterogeneity, photosynthetic activity, and soil moisture relative to historical patterns, while enabling vine-by-vine control by identifying non-productive vines for removal. Using a multi-year Sentinel-2 time series and satellite imagery enhanced with field information, the workflow combines machine-learning models to deliver results reaching 91% accuracy in 2020 and 95% in 2022.","Yield estimation using machine learning from satellite imagery  \nDavid de la Fuente 1, Elena Rivilla2, Ana Tena2, João Vitorino 1, Eva Navascués2, and Antonio Tabasco 1  \n1GMV, Remote Sensing and Geospatial Analytics Division, 28760 Tres Cantos, Madrid, Spain  \n2Pago de Carraovejas, R&D Department, 47300 Peñafiel, Valladolid, Spain  \nAbstract. Accurate and early yield estimation (from pea size) allows 1.-Make decisions at field level:  \ngreen harvesting, irrigation management. 2.-Advance or organise the purchase of grapes from suppliers.  \n3.-Forecast the volume of wine produced in the campaign that has not yet begun. 4.-Define the quality of the vintage: regular and detailed monitoring of whether, or not, the heterogeneity of the leaf surface, photosynthetic activity or soil moisture observed in the vineyards is as expected at this time, compared with historical values. 5.-Precise control of each vine in production, knowing which vines are no longer productive or should be grubbed up. The Sentinel-2 satellite has generated a time series of images spanning more than six years, which is a great help in analysing the state of permanent crops such as vineyards, where grapes are produced every year. The weekly comparison of what is happening in the current season with what has happened in the previous six seasons is information that is in line with agricultural practices: Winegrowers make the mental exercise of comparing how the vines are developing today with how they developed in previous seasons, with the aim of repeating the years of good yields. In addition, several commercial satellites can now capture images of 50 centimetres pixel resolution or even better, making it possible to check the health of each vine every year. Since 2020, GMV and Pago de Carraovejas have been working together to develop a yield estimation service based on field information and satellite images that feed machine learning algorithms. This paper describes the path followed from the beginning and the steps taken, summarising as follows: 1. - Machine learning algorithm trained with cluster counting and satellite data. 2. - Adjustment of the number of vines in production in each vineyard using very high-resolution imagery. 3. -Machine learning algorithm trained on real production from past campaigns and historical Sentinel-2 time series. The results obtained by comparing the actual grape intake in the winery with the yield estimation range from 91% accuracy in 2020 to 95% accuracy in 2022.  \n1 Introduction  \nKnowing in advance and accurately vineyard yield from the grape’s pea size stage is a piece of very valuable information for winegrowers and winemakers: they can control the quality of the vintage according to whether heterogeneity in leaf area, photosynthetic activity or humidity is as expected, knowing which areas are no longer productive, better watering and green pruning management, forecast the volume of wine to be produced and organise in advance the purchase of supplies if necessary.  \nTraditional methods or direct methods of yield estimation are based on theoretical equations with explanatory variables such as the number of grape bunches, the number of berries per bunch and the average berry weight [1] . They are static estimates based on a multi-stage process, highly dependent on adequate human resources, from counting bunches by visiting control parcels once to managing extensive historical databases of bunch weights and past yields. Bunches number and grape weights vary yearly according to the climatic  \nconditions, the general health status of the vineyards and the agronomic practices such as grubbing up of unproductive vines [2] .  \nIndirect methods are leading alternatives to traditional methods. The number of vines in the vineyard decreases with the age of the vineyard, as the vines can become sick and die. Aerial imagery can update variables such as the fault factor due to the dead vines' grubbing-up process [3] . Likewise, aerial i","cbCaip85U3EyLq5J","https://ap.wps.com/l/cbCaip85U3EyLq5J","pdf",729117,1,7,"English","en",105,"# Introduction\n## Motivation for early yield estimation\n## Traditional yield estimation methods\n## Indirect methods using aerial and satellite imagery\n## Machine learning and AI for yield estimation","[{\"question\":\"Why is early yield estimation from the pea-size stage important for winegrowers?\",\"answer\":\"It enables field-level actions such as green harvesting and irrigation management, supports advance procurement of grapes, and helps forecast the wine volume for the campaign before it starts.\"},{\"question\":\"How does the paper leverage satellite imagery for yield estimation?\",\"answer\":\"It uses Sentinel-2 imagery, including NDVI time series, and combines it with field information; the approach also updates the number of vines in production using very high-resolution imagery to account for grubbed or unproductive vines.\"},{\"question\":\"What accuracy range did the method achieve in real comparisons?\",\"answer\":\"Comparisons with actual grape intake in the winery show yield estimation accuracy ranging from 91% in 2020 to 95% in 2022.\"}]","Yield estimation using machine learning from satellite imagery | PDF",1785732412,18,{"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},"yield-estimation-using-machine-learning-from-satellite-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/yield-estimation-using-machine-learning-from-satellite-imagery/120866/",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","2026-08-03",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},"Why is early yield estimation from the pea-size stage important for winegrowers?","Question",{"text":75,"@type":76},"It enables field-level actions such as green harvesting and irrigation management, supports advance procurement of grapes, and helps forecast the wine volume for the campaign before it starts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper leverage satellite imagery for yield estimation?",{"text":80,"@type":76},"It uses Sentinel-2 imagery, including NDVI time series, and combines it with field information; the approach also updates the number of vines in production using very high-resolution imagery to account for grubbed or unproductive vines.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy range did the method achieve in real comparisons?",{"text":84,"@type":76},"Comparisons with actual grape intake in the winery show yield estimation accuracy ranging from 91% in 2020 to 95% in 2022.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]