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Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava-CMD and CBSD

dc.contributor.authorMrisho, L.
dc.contributor.authorMbilinyi, N.
dc.contributor.authorNdalahwa, M.
dc.contributor.authorRamcharan, A.
dc.contributor.authorKehs, A.K.
dc.contributor.authorMcCloskey, P.
dc.contributor.authorMurithi, H.
dc.contributor.authorHughes, D.P.
dc.contributor.authorLegg, J.
dc.date.accessioned2022-04-07T09:50:16Z
dc.date.available2022-04-07T09:50:16Z
dc.date.issued2020
dc.identifier.citationMrisho, L., Mbilinyi, N., Ndalahwa, M., Ramcharan, A., Kehs, A.K., McCloskey, P., ... & Legg, J. (2020). Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava–CMD and CBSD. Frontiers in Plant Science, 11: 590889, 1-14.
dc.identifier.issn1664-462X
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7413
dc.description.abstractNuru is a deep learning object detection model for diagnosing plant diseases and pests developed as a public good by PlantVillage (Penn State University), FAO, IITA, CIMMYT, and others. It provides a simple, inexpensive and robust means of conducting in-field diagnosis without requiring an internet connection. Diagnostic tools that do not require the internet are critical for rural settings, especially in Africa where internet penetration is very low. An investigation was conducted in East Africa to evaluate the effectiveness of Nuru as a diagnostic tool by comparing the ability of Nuru, cassava experts (researchers trained on cassava pests and diseases), agricultural extension officers and farmers to correctly identify symptoms of cassava mosaic disease (CMD), cassava brown streak disease (CBSD) and the damage caused by cassava green mites (CGM). The diagnosis capability of Nuru and that of the assessed individuals was determined by inspecting cassava plants and by using the cassava symptom recognition assessment tool (CaSRAT) to score images of cassava leaves, based on the symptoms present. Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40–58%) and farmers (18–31%). Nuru’s accuracy in diagnosing cassava disease and pest symptoms, in the field, was enhanced significantly by increasing the number of leaves assessed to six leaves per plant (74–88%). Two weeks of Nuru practical use provided a slight increase in the diagnostic skill of extension workers, suggesting that a longer duration of field experience with Nuru might result in significant improvements. Overall, these findings suggest that Nuru can be an effective tool for in-field diagnosis of cassava diseases and has the potential to be a quick and cost-effective means of disseminating knowledge from researchers to agricultural extension agents and farmers, particularly on the identification of disease symptoms and their management practices.
dc.description.sponsorshipConsultative Group on International Agricultural Research
dc.description.sponsorshipPennsylvania State University
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipSchmidt Futures
dc.description.sponsorshipSelf Help Africa
dc.format.extent1-14
dc.language.isoen
dc.subjectAfrican Cassava Mosaic Virus
dc.subjectDiseases
dc.subjectPlant Diseases
dc.subjectAfrica
dc.subjectKenya
dc.subjectTanzania
dc.titleAccuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava-CMD and CBSD
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationUniversity of Dar es Salaam
cg.contributor.affiliationBayer Crop Science, United States
cg.contributor.affiliationThe Pennsylvania State University
cg.contributor.affiliationOak Ridge Institute for Science and Education
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryTanzania
cg.coverage.hubEastern Africa Hub
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidMRISHO:2020
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectCassava
cg.iitasubjectDisease Control
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Diseases
cg.iitasubjectPlant Health
cg.iitasubjectPlant Production
cg.journalFrontiers in Plant Science
cg.notesOpen Access Journal; Published online: 18 Dec 2020
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.3389/fpls.2020.590889
cg.iitaauthor.identifierJames Legg: 0000-0003-4140-3757
cg.futureupdate.requiredNo
cg.identifier.issue590889
cg.identifier.volume11


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