SharpmetriX: A Cost-Effective IoT Solution for Mapping Harvest Vineyards Yields and Labor Productivity Indicators

SharpmetriX: A Cost-Effective IoT Solution for Mapping Harvest Vineyards Yields and Labor Productivity Indicators | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SharpmetriX: A Cost-Effective IoT Solution for Mapping Harvest Vineyards Yields and Labor Productivity Indicators Ricardo Jorge Neves, Filipe Neves dos Santos, Germano Moreira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9418360/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Manual harvesting remains indispensable in terrain constrained for mechanization and in premium vineyards, underscoring the need for high-resolution mapping of yield, labor productivity, and quality to enable effective precision management. This paper introduces SharpmetriX, a cost-effective IoT-based framework designed to support precision in-harvest yield and labor productivity mapping through addition of digital capability to conventional manual harvesting tools. Lightweight sensing add-ons integrated into harvesting scissors and buckets are synchronized through a smartphone application, enabling the collection of time-stamped and georeferenced harvesting events without disrupting established harvesting workflows. In practice, smartphone-based GNSS positioning during manual harvesting is affected by unavoidable sources of uncertainty related to operator movement, posture, and local signal conditions. Rather than addressing these limitations solely through higher-precision sensors, the proposed approach adopts a geometry-aware spatial processing strategy. Harvesting events are projected onto pre-mapped vine-row centerlines and assigned using trajectory-based clustering with temporal continuity constraints and global cluster-to-row matching, improving spatial attribution under meter-level GNSS noise. Field experiments conducted in Portugal's Dão wine region illustrate the performance of the proposed system under realistic harvesting conditions. Standalone smartphone GNSS achieved horizontal accuracy on the order of 4~m, which is insufficient for reliable vine-row-level mapping. A benchmark against RTK-GNSS indicated an approximate 65% reduction in cross-track error relative to standalone smartphone positioning, highlighting the impact of positioning uncertainty. After spatial correction and attribution, cross-track dispersion was reduced and spatial coherence improved, enabling sub-meter effective positioning relative to vine rows. Controlled harvest tests showed a low discrepancy between ground-truth harvested bunches and SharpmetriX registered cutting events (2.98%) and an average relative error of 4.96% in vine-level weight estimation. Overall, the results indicate that high-resolution, vine-row-referenced yield and productivity mapping can be achieved using affordable, tool-integrated sensing combined with geometry-aware spatial processing, providing a practical pathway for precision viticulture in manually harvested and terrain-constrained vineyards. Precision viticulture yield map productivity assessment GNSS-RTK IoT sensing Smartphone-based monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9418360","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635677456,"identity":"712a90f6-0026-404f-b308-5df2a0f270bf","order_by":0,"name":"Ricardo Jorge 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This paper introduces SharpmetriX, a cost-effective IoT-based framework designed to support precision in-harvest yield and labor productivity mapping through addition of digital capability to conventional manual harvesting tools. Lightweight sensing add-ons integrated into harvesting scissors and buckets are synchronized through a smartphone application, enabling the collection of time-stamped and georeferenced harvesting events without disrupting established harvesting workflows. In practice, smartphone-based GNSS positioning during manual harvesting is affected by unavoidable sources of uncertainty related to operator movement, posture, and local signal conditions. Rather than addressing these limitations solely through higher-precision sensors, the proposed approach adopts a geometry-aware spatial processing strategy. Harvesting events are projected onto pre-mapped vine-row centerlines and assigned using trajectory-based clustering with temporal continuity constraints and global cluster-to-row matching, improving spatial attribution under meter-level GNSS noise. Field experiments conducted in Portugal's Dão wine region illustrate the performance of the proposed system under realistic harvesting conditions. Standalone smartphone GNSS achieved horizontal accuracy on the order of 4~m, which is insufficient for reliable vine-row-level mapping. A benchmark against RTK-GNSS indicated an approximate 65% reduction in cross-track error relative to standalone smartphone positioning, highlighting the impact of positioning uncertainty. After spatial correction and attribution, cross-track dispersion was reduced and spatial coherence improved, enabling sub-meter effective positioning relative to vine rows. Controlled harvest tests showed a low discrepancy between ground-truth harvested bunches and SharpmetriX registered cutting events (2.98%) and an average relative error of 4.96% in vine-level weight estimation. Overall, the results indicate that high-resolution, vine-row-referenced yield and productivity mapping can be achieved using affordable, tool-integrated sensing combined with geometry-aware spatial processing, providing a practical pathway for precision viticulture in manually harvested and terrain-constrained vineyards.\u003c/p\u003e","manuscriptTitle":"SharpmetriX: A Cost-Effective IoT Solution for Mapping Harvest Vineyards Yields and Labor Productivity Indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 06:43:01","doi":"10.21203/rs.3.rs-9418360/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fbb2e1b9-5d7c-450b-97c1-2624577d011b","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"5","date":"2026-05-06T15:12:27+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:43:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 06:43:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9418360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9418360","identity":"rs-9418360","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}