{ "id": "AIBD-0191", "title": "PlantVillage Nuru identifies cassava disease symptoms at 65% accuracy against 40–58% for extension agents", "summary": "IITA-led evaluation in Kenya and Tanzania: the offline deep-learning app out-diagnosed extension officers (40–58%) and farmers (18–31%); six-leaf assessment raised accuracy to 74–88%.", "date_observed": "2020-12-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Nuru is a deep-learning object-detection model inside the free PlantVillage Android app, trained on 2,756 cassava leaf images and able to run offline. PlantVillage (Penn State) developed it with FAO, IITA, CIMMYT and others. IITA researchers compared Nuru with cassava experts, agricultural extension officers and farmers. The targets were cassava mosaic disease (CMD), cassava brown streak disease (CBSD) and cassava green mite damage. They inspected plants in the field and scored a 170-image cassava symptom recognition assessment tool (CaSRAT) built from a farm in Mkuranga, Tanzania. CaSRAT was also used with 30 extension agents and 50 farmers from Busia County, western Kenya, before and after they were introduced to the app. Nuru diagnosed cassava disease symptoms at 65% accuracy in 2020 versus 40–58% for extension agents and 18–31% for farmers. It rose to 74–88% with six leaves per plant assessed. Mild symptoms were recognized at only 13–37%. Two weeks of practical use gave extension workers only a slight gain. No farm-level outcome is reported.", "category": "general benefit", "unexpected": false, "purpose": "Diagnose cassava mosaic disease, cassava brown streak disease and cassava green mite damage from leaf images on an offline Android phone.", "system": "PlantVillage Nuru cassava model (deep-learning object detection)", "model_version": "PlantVillage Nuru V2.6.0-39", "developer": "PlantVillage, Pennsylvania State University, with FAO, IITA, CIMMYT and others", "deployer": "IITA field evaluation with extension officers and farmers in Busia County, Kenya, and in Tanzania", "location": "Busia County, Kenya; Mkuranga, Tanzania", "domain": "Agriculture / crop disease diagnosis", "segment": "Smallholder cassava", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The paper also frames Nuru as an e-extension tool for transferring diagnostic knowledge, but the measured result is diagnostic accuracy.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Farmers scored 18–31% on diagnosis. The paper reports only a “slight increase” in extension workers' skill after two weeks and no yield or income outcome." }, { "group": "organizational", "depth": null, "reason": "No deployment outcome for extension services is documented beyond the accuracy comparison." } ], "ai_role": { "status": "assessed", "statement": "The study measures the model's diagnostic accuracy against humans. It does not document any alert that changed a management decision. Authors include the model's developers at IITA and Penn State, so the source is peer-reviewed rather than independent of the makers." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP. 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 (2020). doi:10.3389/fpls.2020.590889", "title": "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD", "url": "https://doi.org/10.3389/fpls.2020.590889", "date": "2020-12-18", "written_by": "makers", "quote": "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%)." }, { "kind": "peer-reviewed study", "citation": "Adjéi EA, Traoré K, Sawadogo/Compaoré EMFW, et al. Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man. Frontiers in Agronomy 6 (2024). doi:10.3389/fagro.2024.1433204", "title": "Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d'Ivoire: a case study in the departments of Dabou, Bouaké, and Man", "url": "https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full", "date": "2024-09-13", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A model trained on 2,756 cassava leaf images", "Field plants and a 170-image CaSRAT set scored by consensus of cassava experts", "Comparison groups of 30 extension agents and 50 farmers from Busia County" ], "must_hold": [ "Symptoms expressed on the assessed leaves (mild symptoms were recognized at only 13–37%)", "Six leaves per plant assessed to reach 74–88%" ], "echoes": [ { "date": "2018-06-25", "setting": "Fall armyworm model in the same app, launched by FAO and Penn State", "model_version": "Nuru fall armyworm", "outcome": "unknown", "source": "FAO news release, 25 June 2018" } ] }, "paired_incident": null, "headline": "PlantVillage Nuru out-diagnoses extension agents, 65% vs 40–58%", "metric": "65% accuracy in 2020 versus 40–58% for extension agents and 18–31% for farmers", "comparator": { "against": "agricultural extension agents and farmers diagnosing the same cassava symptoms", "before": "40–58% accuracy (agricultural extension agents); 18–31% (farmers)", "after": "65% accuracy in 2020 (Nuru)", "basis": "measured", "quote": "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%).", "url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full" }, "later": { "status": "scaled", "date": "2024-09-13", "note": "A September 2024 study reports that the WAVE center has disseminated Nuru to cassava farmers in Côte d'Ivoire since 2020, with a 45% adoption rate among 152 respondents in three departments.", "quote": "In this regard, WAVE has been disseminating a plant disease detection smart application, “PlantVillage Nuru” to farmers since 2020.", "url": "https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full" }, "value": null, "developer_country": "United States", "deployer_country": "Nigeria", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Learning among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full; https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2024.1433204/full; https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.590889/full", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Pennsylvania_State_University; https://en.wikipedia.org/wiki/International_Institute_of_Tropical_Agriculture", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study and the makers' peer-reviewed study. 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