Evaluation of a Locally Developed Mobile Application in Malawi to Uniquely Identify Cattle Through Facial Recognition

Evaluation of a Locally Developed Mobile Application in Malawi to Uniquely Identify Cattle Through Facial Recognition | 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 Evaluation of a Locally Developed Mobile Application in Malawi to Uniquely Identify Cattle Through Facial Recognition Ivy Frazier Jere, Gregory Chingala, Francis Ganya, Daniel Chiumia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8253606/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 The current study evaluated a locally developed facial recognition mobile application as a tool for cattle identification in the Malawi’s smallholder systems. The study implemented a Deep Learning Convolutional Neural Network (DCNN) model using a Transfer Learning approach to leverage pre-existing models for feature extraction and improve training efficiency to train the application model. The foundational model selected was EfficientNet-B0. The application was tested on 175 cattle, including Malawi Zebu and dairy crosses, across controlled handling races and open-field environments to simulate real-world conditions. Model Performance was evaluated using standard classification metrics including precision, recall, accuracy, and F1-Score. The application demonstrated outstanding predictive and accuracy performance when applied to restrained Holstein-Friesian dairy cows and their crosses in the handling race with a precision of 0.95 and a recall of 0.91. Nevertheless, its best performance occurred in Malawi Zebu cattle registered and retrieved under open field conditions (precision = 1.00 and recall = 0.95). Specificity testing with unregistered cattle showed an 80% success rate. However, the system matched 20% of unknown animals with existing identities indicating need for further refinement to ascertain ownership to curb stock theft. Despite these limitations, the application shows significant potential as a digital livestock identification tool. Its high precision supports traceability and management, though further development is needed to improve robustness in diverse field conditions. Deep learning Malawi Zebu model evaluation tranfer learning Full Text 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8253606","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556960859,"identity":"eda0e25b-59c5-4a6e-bd9e-01f6d69640b1","order_by":0,"name":"Ivy Frazier Jere","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Ivy","middleName":"Frazier","lastName":"Jere","suffix":""},{"id":556960860,"identity":"9260a86d-a726-46a4-845d-2de76dc8a95c","order_by":1,"name":"Gregory Chingala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACHoYDQFKGgb25gTQtPAw8B0nQAiElEonUIu9+9uDhgl92PPwzH7ZJ/Nxhl8/AfvgB08023FoMz+QlHJ7Zl8wjcTuxTbL3TLJlA0+aAXMuPi0zeAwO8/Yw8zAAtUjwtjEbMDDkMDDnbiOopZ5H/ubBNsm/bfUGDPxv8GuRlwBq4flxmMfgBmObNG/bYQMGCQK2GPDkAG1pOM5jeCax2Vq27bgBm8Qzg8O5//DY0n7G+DPPn2o5ueOHD95821ZtwM+f/PBxzhk8thwAEozI4cMGxAdwawDa0gAi/+BTMgpGwSgYBSMeAAAkFk5lsiu6OQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4577-1429","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Chingala","suffix":""},{"id":556960861,"identity":"dd3254a1-fd33-499f-962c-4bbe358a1807","order_by":2,"name":"Francis Ganya","email":"","orcid":"","institution":"Malawi University of Business and Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Ganya","suffix":""},{"id":556960862,"identity":"c569d2ee-0c7d-44e2-b48f-76e73bf6f42a","order_by":3,"name":"Daniel Chiumia","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Chiumia","suffix":""}],"badges":[],"createdAt":"2025-12-01 19:52:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8253606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8253606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97929392,"identity":"0c3cb05d-9a82-40ad-ae91-5a4bede44ef6","added_by":"auto","created_at":"2025-12-10 23:17:02","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9218,"visible":true,"origin":"","legend":"","description":"","filename":"tropTROPD2502359.xml","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/aeaccdd06e6a190a2c39a26e.xml"},{"id":98421723,"identity":"20e2ec8e-5fdd-40a3-8a60-792d70595ef0","added_by":"auto","created_at":"2025-12-17 16:29:06","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":931,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD250235953060.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/edaebb0af02bd43d2eb161e4.xml"},{"id":97929390,"identity":"105d36ef-f439-4def-b9d1-d528d9d461d5","added_by":"auto","created_at":"2025-12-10 23:17:02","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":926,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD2502359Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/97bc4a12db6b3ffb16f269e0.xml"},{"id":98422837,"identity":"1e6dae52-bb29-4885-8efc-6633d0329793","added_by":"auto","created_at":"2025-12-17 16:31:33","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76884,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD25023590enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/77abedff6b44c24345b93b83.xml"},{"id":97929396,"identity":"cc698f8e-8ca5-412d-9bc5-2918b4ecc90c","added_by":"auto","created_at":"2025-12-10 23:17:02","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":397527,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/88e24c325af95cbaedadd8d9.jpeg"},{"id":97929393,"identity":"ea246890-6801-4646-a302-febe7d11a2f5","added_by":"auto","created_at":"2025-12-10 23:17:02","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131396,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/e8079baa38749d2dbb85e8fb.png"},{"id":98422559,"identity":"d99871dc-bed2-40f6-a3d2-6e967aaf0a5c","added_by":"auto","created_at":"2025-12-17 16:31:12","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74636,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD25023590structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/4da8c9b4af5ef1713967dff7.xml"},{"id":98423100,"identity":"41c1bf98-0ea4-4210-9e41-f83670b94ca8","added_by":"auto","created_at":"2025-12-17 16:31:51","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":81737,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1/fefd348747fd3d36400ed06e.html"},{"id":98443614,"identity":"10bd793c-6a9d-448c-ba84-7f52d75956eb","added_by":"auto","created_at":"2025-12-17 17:13:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":412308,"visible":true,"origin":"","legend":"","description":"","filename":"TAHPManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8253606/v1_covered_8786cf6a-ece6-409a-b0ed-b402f4714e15.pdf"}],"financialInterests":"","formattedTitle":"Evaluation of a Locally Developed Mobile Application in Malawi to Uniquely Identify Cattle Through Facial Recognition","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Deep learning, Malawi Zebu, model evaluation, tranfer learning","lastPublishedDoi":"10.21203/rs.3.rs-8253606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8253606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current study evaluated a locally developed facial recognition mobile application as a tool for cattle identification in the Malawi\u0026rsquo;s smallholder systems. The study implemented a Deep Learning Convolutional Neural Network (DCNN) model using a Transfer Learning approach to leverage pre-existing models for feature extraction and improve training efficiency to train the application model. The foundational model selected was EfficientNet-B0. The application was tested on 175 cattle, including Malawi Zebu and dairy crosses, across controlled handling races and open-field environments to simulate real-world conditions. Model Performance was evaluated using standard classification metrics including precision, recall, accuracy, and F1-Score. The application demonstrated outstanding predictive and accuracy performance when applied to restrained Holstein-Friesian dairy cows and their crosses in the handling race with a precision of 0.95 and a recall of 0.91. Nevertheless, its best performance occurred in Malawi Zebu cattle registered and retrieved under open field conditions (precision\u0026thinsp;=\u0026thinsp;1.00 and recall\u0026thinsp;=\u0026thinsp;0.95). Specificity testing with unregistered cattle showed an 80% success rate. However, the system matched 20% of unknown animals with existing identities indicating need for further refinement to ascertain ownership to curb stock theft. Despite these limitations, the application shows significant potential as a digital livestock identification tool. Its high precision supports traceability and management, though further development is needed to improve robustness in diverse field conditions.\u003c/p\u003e","manuscriptTitle":"Evaluation of a Locally Developed Mobile Application in Malawi to Uniquely Identify Cattle Through Facial Recognition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 23:16:57","doi":"10.21203/rs.3.rs-8253606/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":"ae825e43-d16c-4ded-837b-86fbc1bcd41f","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-13T10:12:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 23:16:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8253606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8253606","identity":"rs-8253606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}