Accelerating Cassava Genetic Improvement through NDVI-Based High-Throughput Phenotyping

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This preprint studied whether NDVI data from an affordable handheld sensor (Trimble GreenSeeker) can rapidly predict cassava yield and plant architecture traits, addressing the long breeding cycles and labor-intensive phenotyping of cassava improvement. A panel of 453 cassava accessions was evaluated in two contrasting Nigerian agroecological zones (Mokwa and Onne) during 2021/2022, with NDVI collected at 3, 6, and 9 months after planting and integrated with ground-truth measurements of 26 agronomic traits. The authors reported moderate to high broad-sense heritability for traits including fresh root yield, dry matter content, and harvest index, and found that NDVI—especially at 6 months after planting—showed strong predictive power for yield components (R² up to 0.9), with accuracy varying by location; they also observed significant negative correlations between lodging and yield-related traits. A major caveat explicitly stated is that the work is a preprint that has not been peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Cassava ( Manihot esculenta Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker). A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria; Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.Genetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R² up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.
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Accelerating Cassava Genetic Improvement through NDVI-Based High-Throughput Phenotyping | 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 Accelerating Cassava Genetic Improvement through NDVI-Based High-Throughput Phenotyping Abiodun Fatai Olayinka, Adesike Oladoyin Olayinka, Nkouaya Gaby Edwige Mbanjo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8118741/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Cassava ( Manihot esculenta Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker). A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria; Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits. Genetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R² up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions. NDVI High-Throughput Phenotyping Plant Architecture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx TableS3.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Nov, 2025 Editor assigned by journal 25 Nov, 2025 Submission checks completed at journal 25 Nov, 2025 First submitted to journal 14 Nov, 2025 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. 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yield, TYLD – top yield, HI – harvest index, LODG – number of lodged plants per plot, PLTHT6 – plant height at 6 months after planting, BRNHT6 – height at first branch at 6 months after planting, PLTHT9 – plant height at 9 months after planting, BRNHT9 – height at first branch at 9 months after planting, BRNLEV9 - level of branching \u0026nbsp;at 9 months after planting, BRNHB9 – branching habit at 9 months after planting, ANGBR9 – angle of branching, and STMDI9 – stem diameter.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1/44e16aed91cede60ed3fa11f.png"},{"id":96992856,"identity":"6d6b0ed6-b62e-4e3b-9c5a-ff428084d1e4","added_by":"auto","created_at":"2025-11-28 11:41:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath coefficient analysis plot of plant architecture and yield traits in Mokwa trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNOH - number of harvested plants per plot, RTN – number of harvested roots per plot, RTW – root weight, STA – starch content, FYL – fresh root yield, HI – harvest index, LOD – number of lodged plants per plot, PLTHT6 – plant height at 6 months after planting, PLTHT9 – plant height at 9 months after planting, ANG – angle of branching, and STM – stem diameter.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1/69f455bd5654b782372a25eb.png"},{"id":96992877,"identity":"07d21110-fa6c-4a43-b952-2b60fcf3436f","added_by":"auto","created_at":"2025-11-28 11:41:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":712599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenotypic correlation coefficient plot of plant architecture and yield traits in Onne trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNOHAV - number of harvested plants per plot, RTNO – number of harvested roots per plot, SHTWT – shoot weight, RTWT – root weight, DM – dry matter content, STARCH – starch content, FYLD – fresh root yield, DYLD – dry yield, TYLD – top yield, HI – harvest index, LODG – number of lodged plants per plot, PLTHT6 – plant height at 6 months after planting, BRNHT6 – height at first branch at 6 months after planting, PLTHT9 – plant height at 9 months after planting, BRNHT9 – height at first branch at 9 months after planting, BRNLEV9 - level of branching \u0026nbsp;at 9 months after planting, BRNHB9 – branching habit at 9 months after planting, ANGBR9 – angle of branching, and STMDI9 – stem diameter.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1/bace991261c76308601256ed.png"},{"id":96992873,"identity":"0f1f0169-f017-4ab6-9f68-80c76868f99e","added_by":"auto","created_at":"2025-11-28 11:41:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":281424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath coefficient analysis plot of plant architecture and yield traits in Onne trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNOH - number of harvested plants per plot, RTN – number of harvested roots per plot, RTW – root weight, STA – starch content, FYL – fresh root yield, HI – harvest index, LOD – number of lodged plants per plot, PLTHT6 – plant height at 6 months after planting, PLTHT9 – plant height at 9 months after planting, ANG – angle of branching, and STM – stem diameter.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1/10067bacdb9d0aae50500243.png"},{"id":97144961,"identity":"c74b6b62-1590-40f9-8d94-b2b78119baa3","added_by":"auto","created_at":"2025-12-01 10:12:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272302,"visible":true,"origin":"","legend":"","description":"","filename":"Olayinkaetal2015.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1_covered_b971cb56-d45e-4194-8efe-e3f09e152750.pdf"},{"id":96992851,"identity":"d95b95fa-3f62-4f5c-9927-290a30c514fd","added_by":"auto","created_at":"2025-11-28 11:41:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46208,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1/a5db24254422f745e9686bb5.docx"},{"id":97139171,"identity":"966f64d7-bee8-443f-83ad-75c2ade6d342","added_by":"auto","created_at":"2025-12-01 09:59:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17638,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8118741/v1/3121eb4ee6a68682a4da3f13.docx"},{"id":96992862,"identity":"3cfd1f58-f9f7-47fa-bf61-f05d4c422229","added_by":"auto","created_at":"2025-11-28 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Phenotyping","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Agriculture](https://bmcagriculture.biomedcentral.com/)","snPcode":"44399","submissionUrl":"https://submission.nature.com/new-submission/44399/3","title":"BMC Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NDVI, High-Throughput Phenotyping, Plant Architecture","lastPublishedDoi":"10.21203/rs.3.rs-8118741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8118741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCassava (\u003cem\u003eManihot esculenta\u003c/em\u003e Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker). A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria; Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.\u003c/p\u003e\u003cp\u003eGenetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R\u0026sup2; up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.\u003c/p\u003e","manuscriptTitle":"Accelerating Cassava Genetic Improvement through NDVI-Based High-Throughput Phenotyping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 11:41:17","doi":"10.21203/rs.3.rs-8118741/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-26T07:01:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-25T08:16:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-25T08:15:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Agriculture","date":"2025-11-15T01:36:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Agriculture](https://bmcagriculture.biomedcentral.com/)","snPcode":"44399","submissionUrl":"https://submission.nature.com/new-submission/44399/3","title":"BMC Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb0c8768-7c52-4f7c-8a78-e4cd1f906ccc","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T15:39:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 11:41:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8118741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8118741","identity":"rs-8118741","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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