[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125989-en":3,"doc-seo-125989-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125989,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Machine Learning for Dynamic Management Zone in Smart Farming","Digital agriculture accelerates data-driven practices while reducing reliance on costly traditional workflows. This study presents a dynamic management zone delineation method built on machine learning clustering, integrating crop yield data with elevation, soil texture maps, and satellite-derived NDVI. The approach supports spatial analysis of persistent yield variation and enables frequency maps to reflect seasonally changing incidental issues. It helps farmers and agronomists apply variable-rate nitrogen fertilisation by assessing yield potential and stability using NDVI monitoring.","Machine Learning for Dynamic Management Zone in Smart Farming  \nChamil Kulatungaa , Sahraoui Dhelimb , Tahar Kechadic  \na University College Dublin, Dublin, Ireland  \nb University College Dublin, Dublin, Ireland  \nc University College Dublin, Dublin, Ireland  \nAbstract  \nDigital agriculture is growing in popularity among professionals and brings together new opportunities along with pervasive use of modern data-driven technologies. Digital agriculture approaches can be used to replace all traditional agricultural system at very reasonable costs. It is very effective in optimising large-scale management of resources, while traditional techniques cannot even tackle the problem. In this paper, we proposed a dynamic management zone delineation approach based on Machine Learning clustering algorithms using crop yield data, elevation and soil texture maps and available NDVI data. Our proposed dynamic management zone delineation approach is useful for analysing the spatial variation of yield zones. Delineation of yield regions based on historical yield data augmented with topography and soil physical properties helps farmers to economically and sustainably deploy site-specific management practices identifying persistent issues in a field. The use of frequency maps is capable of capturing dynamically changing incidental issues within a growing season. The proposed zone management approach can help farmers/agronomists to apply variable-rate N fertilisation more effectively by analysing yield potential and stability zones with satellite-based NDVI monitoring.  \nKeywords: Data-driven Agriculture, In-field Variability, Management Zones, Yield Maps, NDVI, Geographically Weighted Regression.  \n1. Introduction  \nAgriculture 4.0 is using many modern research and technologies in different aspects of agriculture including genomics, nanotechnology, synthetic proteins, Internet of Things, automation and machine learning [1] . As an important pillar in this space, data-driven agriculture has gain a momentum in last twenty years as a retrofitting mechanism for the available technologies to feed 9 billion population in 2050 . It has become more realistic than ever due to wider use of sensors, cloud computing and their integration with cyber-physical-social farming systems to use big data for intuition, intelligence and insights. However, data-driven agriculture is challenging for small actors but important for global sustainability compared to others industries such as healthcare, fin-tech and manufacturing. Those challenges come with small profit margins, climate change activities, ever decreeing land and labour. But certainly data-driven systems in agriculture sheds some light on sustainable intensification in agriculture to reduce environmental footprint and to maximise economic returns [2] .  \nArable farming contributes considerably to the world food production as cereal a main staple food and also as a sustainable crop in different climatic regions in the world. Several new technologies for better data collections are becoming available in crop framing such as soil scans, remote and proximal crop growth sensing, yield quality and quantity monitoring, granular weather monitoring, etc. Arable fields naturally have contiguity of those data of within-field variations for regionalization based on homogeneous sub-fields [3] . Farmers and agronomists look  \ninto site-specific management practices of large fields by capturing time and spatial variability. Due to economic and logistic reasons, soil sampling are not frequent enough to understand its impact on annual yield. For example P, K, Mg are tested once for three years. However, altitude, soil texture data are not changed or changed slowly. Based on our data management experience in UK farms, yield maps are being collected by many farmers in the last two decades. Most of the analyses have been focused on spatial variability of individual maps. Due to lack of consecutive number of yield maps and cro","cbCaiakG6uQGgmcs","https://ap.wps.com/l/cbCaiakG6uQGgmcs","pdf",12283714,9,1,8,"English","en",105,"# Introduction\n## Agriculture 4.0 and data-driven agriculture\n## Need for yield maps and spatio-temporal analysis\n## Prior work on management zone delineation\n# Proposed approach and contributions","[{\"question\":\"What problem does the dynamic management zone approach address in smart farming?\",\"answer\":\"It targets spatial and temporal variability in crop yields to better identify yield zones and persistent field issues for more informed site-specific decisions.\"},{\"question\":\"Which data sources are combined to delineate management zones?\",\"answer\":\"The method integrates crop yield data with elevation, soil texture maps, and available NDVI data for satellite-based monitoring.\"},{\"question\":\"How does the approach support variable-rate nitrogen fertilisation?\",\"answer\":\"By analyzing yield potential and stability zones, it enables more effective variable-rate N fertilisation based on satellite NDVI monitoring and identified zone characteristics.\"}]","Machine Learning for Dynamic Management Zone in Smart Farming | 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problem does the dynamic management zone approach address in smart farming?","Question",{"text":77,"@type":78},"It targets spatial and temporal variability in crop yields to better identify yield zones and persistent field issues for more informed site-specific decisions.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which data sources are combined to delineate management zones?",{"text":82,"@type":78},"The method integrates crop yield data with elevation, soil texture maps, and available NDVI data for satellite-based monitoring.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the approach support variable-rate nitrogen fertilisation?",{"text":86,"@type":78},"By analyzing yield potential and stability zones, it enables more effective variable-rate N fertilisation based on satellite NDVI monitoring and identified zone 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